Miracle-dz/UniFAR-FTUs huggingface dataset:
On the energy efficiency in multi-user multi-relay coded network

In this paper, the energy efficiency (EE) of a cooperative diversity system with maximum diversity network coding (MDNC) is studied. In the considered system, channel state information (CSI) is only available at the receivers for all the channels. We formulate the problem of maximizing the EE under the constraint on the outage probability. The problem is NP-hard due to the non-convexity of the outage probability function and the nonlinear fractional structure of the introduced EE. To solve the optimization problem efficiently, first, the outage probability function is tightly approximated as a log-convex form in the high signal-to-noise ratio (SNR) region. Further, based on the fractional programming, we transform the introduced EE into a subtractive-form, which is proved to be a convex form. The tradeoff between outage probability and EE is given. The results show that our power allocation (PA) policy can substantially increase the EE. We show that EE can be increased if more relays forward the messages. Additionally, we also investigate the effect of the relay locations on the EE and demonstrate that the increase in the transmission distance in the first hop causes the loss of the EE. The loss can be reduced by our PA policy.
Privacy-Preserving Utility Verification of the Data Published by Non-Interactive Differentially Private Mechanisms

In the problem of privacy-preserving collaborative data publishing, a central data publisher is responsible for aggregating sensitive data from multiple parties and then anonymizing it before publishing for data mining. In such scenarios, the data users may have a strong demand to measure the utility of the published data, since most anonymization techniques have side effects on data utility. Nevertheless, this task is non-trivial, because the utility measuring usually requires the aggregated raw data, which is not revealed to the data users due to privacy concerns. Furthermore, the data publishers may even cheat in the raw data, since no one, including the individual providers, knows the full data set. In this paper, we first propose a privacy-preserving utility verification mechanism based upon cryptographic technique for DiffPart-a differentially private scheme designed for set-valued data. This proposal can measure the data utility based upon the encrypted frequencies of the aggregated raw data instead of the plain values, which thus prevents privacy breach. Moreover, it is enabled to privately check the correctness of the encrypted frequencies provided by the publisher, which helps detect dishonest publishers. We also extend this mechanism to DiffGen-another differentially private publishing scheme designed for relational data. Our theoretical and experimental evaluations demonstrate the security and efficiency of the proposed mechanism.
Observing the Impact of Multiple Metrics and Runtime Adaptations on BSP Process Rescheduling

Process rescheduling is an useful mechanism to offer runtime load balancing, mainly in dynamic and heterogeneous environments. In this context, we developed a model called MigBSP which controls the process migration on BSP (Bulk Synchronous Parallel) applications. A BSP application is divided in one or more supersteps, each one containing both computation and communication phases followed by a barrier synchronization. Since the barrier waits for the slowest process, MigBSP's final objective is to adjust the processes location in order to reduce the supersteps' times. Its novel ideas are twofold. The former is represented by the combination of three metrics - Memory, Computation and Communication - in order to measure the Potential of Migration of each BSP process. The second idea consists in offering efficient adaptations that work on the rescheduling frequency. Both ideas turn MigBSP a viable model for getting performance on BSP applications. Meanwhile, it provides a low overhead on application execution when migrations do not take place. This paper presents MigBSP's algorithms, the parallel machine organization, some experimental results and related work.
[Rapid identification of 14 bile acids contained in Qingkailing injection by HPLC-ESI-MS/MS].

OBJECTIVE To investigate ESI-MS/MS fragmentation pattern of bile acids contained in Qingkailing injection, and the rapid identification method. METHOD HPLC-ESI-MS/MS technology was adopted to investigate ESI-MS/MS fragmentation pattern of five bile acids contained in Qingkailing injection, and rapidly identify bile acids contained in Qingkailing injection from eight different manufacturers. RESULT 5 bile acids showed similar ESI-MS/MS fragmentation patterns, based on which 14 bile acids were rapidly separated from Qingkailing injection. Among them, 11 were found in injections of all of the manufacturers, and the rest three were found in individual manufacturers. CONCLUSION HPLC-ESI-MS/MS is used to rapidly identify bile acids from complex material systems, which provides an effective method for interpreting the complex material base of compound traditional Chinese medicine preparations and enhance the quality standards.
Multi-domain data modeling for biometrics

Recently, much work has been performed on CBIR (content based image retrieval) that treats images as single data domain. However, in our highly digitized society, information is being supplied in multiple domains where the data is linked across domains. For example, a web site does contain images, but it may also contain text, hyperlinks, documents, sound files, movies, and other domains of data. Performing recall operations within single domains eliminates the possibility of employing cross-domain inferences. In this work, a multi-domain search space is presented in with two domains: speech and facial images. A single search space is created that contains data from these vastly different domains and cross-domain inferences are allowed. In other words, queries in the speech domain can retrieve image data even if there was no hard link between these data samples. Generation of multidomain search spaces will eventually expand CBIR systems to include data from a variety of sources.
Les questions de gouvernance dans les entreprises socialement responsables

Cet article se propose d’etudier si les caracteristiques de gouvernance des entreprises socialement responsables different de celles des autres entreprises. A partir d’un echantillon d’entreprises socialement responsables (classees dans Business Ethics magasine) et d’un echantillon temoin pour la periode 2006-2009, nous avons cherche a savoir si la responsabilite sociale etait un levier determinant qui contribuerait a l’amelioration de la qualite de gouvernance. Sur les echantillons concernes, la demonstration n’a pu etre faite d’un lien entre la responsabilite sociale et la gouvernance.
Election Campaign Algorithm for Multimodal Function Optimization

In this paper, we present a new algorithm named Election campaign algorithm (ECA) for the multimodal function optimization. It acts by simulating the behavior that the election candidates pursue the highest support in election campaign. The proposed approaches are validated using test functions taken from the specialized literature, and our results are compared with those obtained by genetic algorithm (GA) and particle swarm optimization algorithm (PSO). Our comparative study indicates that ECA verifies its good performance when dealing with multimodal functions.
Ieee Ipas'14: International Image Processing Applications and Systems Conference 2014 Uncertainty Heuristics of Large Margin Active Learning for Hyperspectral Image Classification

—The difficulties of having expertise in expert systems, the increasing of the data volume, self adaptation and prediction , all those problems are solved in the presence of learning. The classical definition of learning in cognitive science is the ability to improve the performance as the exercise of an activity. With learning, knowledge is automatically extracted from a data set. In this paper, we are interested to study efficient active learning methods. These methods are based on the definition of an efficient training set by iteratively adapting it through adding the most informative unlabeled instances. The selection of these instances are generally based on an uncertainty and diversity criteria. This study is focused on the uncertainty criterion. A review of the principal families of active learning algorithms is presented. Then the large-margin active learning techniques are detailed and evaluations of the contribution of large margin uncertainty criteria are presented.
A Mechanistic Review of Cell Death in Alcohol-Induced Liver Injury.

Alcoholic liver disease (ALD) is a major health problem in the United States and worldwide without successful treatments. Chronic alcohol consumption can lead to ALD, which is characterized by steatosis, inflammation, fibrosis, cirrhosis, and even liver cancer. Recent studies suggest that alcohol induces both cell death and adaptive cell survival pathways in the liver, and the balance of cell death and cell survival ultimately decides the pathogenesis of ALD. This review summarizes the recent progress on the role and mechanisms of apoptosis, necroptosis, and autophagy in the pathogenesis of ALD. Understanding the complex regulation of apoptosis, necrosis, and autophagy may help to develop novel therapeutic strategies by targeting all 3 pathways simultaneously.
Antimicrobial Activity of Extracts of Chemical Races of the Lichen Pseudevernia furfuracea and their Physodic Acid, Chloroatranorin, Atranorin, and Olivetoric Acid Constituents

The antimicrobial activity and the MIC values of the ethanol, chloroform, diethyl ether, and acetone extracts of the chemical races of Pseudevernia furfuracea (var. furfuracea and var. ceratea) and their physodic acid, chloroatranorin, atranorin, and olivetoric acid constituents have been investigated against some microorganisms. Nearly all extracts of both chemical races showed antimicrobial activity against Aeromonas hydrophila, Bacillus cereus, Bacillus subtilis, Listeria monocytogenes, Proteus vulgaris, Staphylococcus aureus, Streptococcus faecalis, Yersinia enterocolitica, Candida albicans, Candida glabrata, Alternaria alternata, Ascochyta rabiei, Aspergillus niger, Fusarium culmorum, Fusarium moniliforme, Fusarium oxysporum, Fusarium solani, and Penicillium notatum. There was no antimicrobial activity of the extracts against Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Pseudomonas syringae, Salmonella typhimurium, Alternaria citri, Alternaria tenuissima, and Gaeumannomyces graminis. Chloroatranorin and olivetoric acid were active against the same microorganisms with few exceptions. Physodic acid was active against about the same bacteria and yeasts and inactive against all of the filamentous fungi tested. Also no activity of atranorin against the filamentous fungi was observed.
On the Complexity of Monotonic Inheritance with Roles

We investigate the complexity of reasoning with monotonic inheritance hierarchies that contain, beside ISA edges, also ROLE (or FUNCTION) edges. A ROLE edge is an edge labelled with a name such as spouse-of or brother-of. We call such networks ISAR networks. Given a network with n vertices and m edges, we consider two problems: (P1) determining whether the network implies an isa relation between two particular nodes, and (P2) determining all isa relations implied by the network. As is well known, without ROLE edges the time complexity of P1 is O(m), and the time complexity of P2 is O(n3). Unfortunately, the results do not extend naturally to ISAR networks, except in a very restricted case. For general ISAR network we frost give an polynomial algorithm by an easy reduction to proposional Horn theory. As the degree of the polynomial is quite high (O(mn4) for P1, O(mn6) for P2), we then develop a more direct algorithm. For both P1, and P2, its complexity is O(n3 + m2). Actually, a finer analysis of the algorithm reveals a complexity of O(nr(Zog r) + n2r + n3), where r is the number of different ROLE labels. One corolary is that if we fix the number of ROLE labels, the complexity of our algorithm drops back to O(n3).
Bounded Model Checking of Incomplete Networks of Timed Automata

Verification of real-time systems - e.g. communication protocols or embedded controllers - is an important task. One method to detect errors is called bounded model checking (BMC). In BMC the system is iteratively unfolded and then transformed into a satisfiability problem. If an appropriate solver finds the $k$-th instance to be satisfiable a counterexample for a given safety property has been found. In this paper we present a first approach to apply BMC to networks of timed automata (that is a system of several interacting subautomata) where parts of the network are unspecified (so called blackboxes). Here, we would like to answer the question of unrealizability, that is, is there a path of a certain length violating a safety property regardless of the implementation of the blackboxes. We provide solutions to this problem for two timed automata communication models. For the simple synchronization model, a BMC approach based on fixed transitions is introduced resulting in a SAT-Modulo-Theory formula. With respect to the use of bounded integer variables for communication, we prove unrealizability by introducing universal quantification, yielding more advanced quantified SAT-Modulo-Theory formulas.
Human Movements Separation Based on Principle Component Analysis

With more and more attention to terrorist attacks, rescue after disaster and medical treatments, the study on human motions has become a hot topic in recent years. Thanks to the unique mechanism of humans, the m-D signatures, which contain extensive information, of each segment are obviously distinct. It remains a great challenge to separate the movement of humans' each part. In this paper, a method for human movements separation based on a principle component analysis (PCA) is proposed. As one of the classical methods in the blind source separation problems, PCA decomposes the signal to a series of orthogonal basis functions to construct the Eigen subspace. The original signal can be represented by the linear combination of the orthogonal basis functions. In addition, the Akaike information criterion is utilized to determine the minimal number of output for PCA. Furthermore, the ixegram and the optimization theory are combined to cluster the principle components to three new groups. Each group depicts one motion form of human. Simulated results verify the superiority of the proposed algorithm.
Unraveling Change: Social Bonds and Recidivism among Released Offenders

Abstract Correctional researchers have increasingly focused on social bonding as a key pathway by which parolees desist from crime after release. Most work to date has focused on levels of bonds, either at reentry or as a function of events occurring in the community. However, few have assessed whether the magnitude of change in bonds during incarceration has an additional effect on desistance. Distinguishing between levels and change with respect to bonds may have important implications for understanding how bonds impact behavior. This paper addresses this gap by drawing on survey data from a sample of inmates at the start and end of their six-month prison terms. Recidivism is assessed as a function of change in social bonds (attachments and beliefs) from entrance to exit from prison, as well as levels of bonds at release. Our findings indicate that changes in social relationships predict recidivism, whereas improvements in prosocial beliefs do not. The data also suggest that the level of prosocial belief at release is significantly related to recidivism, whereas the level of attachment is not. The implications of these findings and directions for future research are discussed.
Reexamining usability dimensions: The case of social media

This study aims to reexamine some traditional usability principles in the context of social media. The instrument was based on 14 items of usability dimensions (i.e. learnability, flexibility, and robustness) proposed by Dix et al. [1], we employed exploratory factor analysis (EFA) to contextualize them into the suitability of social media usability. Using 54 responses from active users of Facebook and Twitter, this study revealed five new usability dimensions of social media: interactivity, intuitiveness, conformability, adaptability, and forgiveness. The findings substantiate some claims that usability is context-specific: culturally variant and dependent upon the product under examination.
A Proactive Distributed Denial of Service Protection Framework

Securing communication networks against distributed denial of service attacks (DDoS) is still one of the most challenging network security issues. We propose a framework to protect network routers and hosts against resource starvation caused by DDoS attacks. We pro-actively build overlay groups of neighboring enhanced routers according to current traffic patterns. During ongoing attacks, the framework provides knowledge and mechanisms to forward alerts and mitigation rules towards the attacking traffic sources. Within the steadily growing protected area, we are able to release the mitigation rules in order to resume normal network operation. In this paper we present architecture and auto-configuration mechanisms of the framework including communication protocols and messages.
Performance-driven stepwise refinement of component-based architectures

Detailed models of component-based software architectures are crucial for the accurate evaluation and prediction of systems quality attributes (e.g., performance) during early development stages. Ideally, the increased complexity of such models should not incur additional modelling effort for developers. Specific incremental model refinements called completions provide a systematic approach to reduce the modelling effort. Completions transparently integrate low-level details that affect system's quality (e.g. performance impact of middleware configuration) into component-based architectural models, using model-to-model transformations. When multiple completions are to be applied, the necessary model transformations are executed in a chain. In such scenarios, conflicts between different completions are likely. In current practice, the conflicts are supposed to be resolved by software architects, who decide on the suitable transformation order manually. However, this approach is time-consuming, can be error-prone, and is likely to result in suboptimal designs. In this paper, we aim to automate the resolution of conflicts. We define a technique to identify and resolve possible conflicts. The technique locally optimises the completions order, based on its validity and quality attributes semantics. We validate our approach by applying it to an architecture model of a component-based business information system and analyse the impact of different completions orders and the complexity of possible conflicts.
Supporting families in the early education of children with special needs: The perspectives of Portage home visitors.

ABSTRACT This qualitative study focuses on Portage home visiting. In‐depth interviews were conducted with 12 Portage home visitors which were transcribed and analysed according to the procedures of Grounded Theory. The following three main themes were identified: the ethos of the Portage model of early intervention; the process of Portage home visiting; and the Portage home visitor as a family supporter. A proposed model of the factors that influence the Portage practice of family support is derived from the data. The discussion addresses the relationship of these findings to the management of Portage home visiting and highlights the use of qualitative methodology in researching the early education of children with special needs.
Rapid training of higher-order neural networks for invariant pattern recognition

The authors demonstrate a second-order neural network that has learned to distinguish between two objects, regardless of their size or translational position, after being trained on only one view of each object. Using an image size of 16*16 pixels, the training took less than 1 min of run time on a Sun 3 workstation. A recognition accuracy of 100% was achieved by the resulting network for several test-object pairs, including the standard T-C problem, for any translational position and over a scale factor of five. The second-order network takes advantage of known relationships between input pixels to build invariance into the network architecture. The use of a third-order neural network to achieve simultaneous rotation, scale, and position invariance is described. Because of the high level of invariance and rapid, efficient training, initial results show higher order neural networks to be vastly superior to multilevel first-order networks trained by backpropagation for applications where invariant pattern recognition is required. <<ETX>>
Visual Control of Robots Using Range Images

In the last years, 3D-vision systems based on the time-of-flight (ToF) principle have gained more importance in order to obtain 3D information from the workspace. In this paper, an analysis of the use of 3D ToF cameras to guide a robot arm is performed. To do so, an adaptive method to simultaneous visual servo control and camera calibration is presented. Using this method a robot arm is guided by using range information obtained from a ToF camera. Furthermore, the self-calibration method obtains the adequate integration time to be used by the range camera in order to precisely determine the depth information.
General restoration filter for vibrated image restoration

Low temporal frequency vibrations involve random image degradation depending on the instant of exposure. Exact restoration requires calculating a specific filter unique to each vibrated image. In order to calculate the restoration filter for each image the specific degradation function is needed. Therefore, the instant of exposure has to be measured or estimated by some other means. In this work a general restoration filter for a single vibrated image is developed. The filter is independent of the instant of exposure. Assuming that receiving each image from a vibrated image ensemble is equally likely, the filter is designed to give best performance averaged over the ensemble. Even though when applying the new filter to any vibrated image the restoration achieved is slightly poorer than with an exact filter based on the specific motion function, the new filter has the advantage of simplicity. Since the instant of exposure is not needed a new appropriate filter does not have to be calculated for each exposure, the proposed filter is more practical and suitable for real time restoration.