Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability-Aware Dynamic Voltage and Frequency Scaling

Dynamic voltage and frequency scaling (DVFS) is an effective method for controlling energy dissipation of embedded systems. However, recent researches have illustrated that DVFS techniques have compromising effects on the system reliability. Our analysis results show that, ignoring the effects of voltage scaling on fault rate could considerably decrease the system reliability. Therefore, we use the slack time to increase system reliability as well as to save energy by frequency and voltage scaling techniques. We first investigate the effects of frequency and voltage scaling on the system reliability exploiting the proposed formula and then propose a reliability-aware DVFS scheme in which the frequency and voltage are scaled considering reliability and performance constraints. Comparing the results to that of the traditional DVFS methods the proposed reliability-aware DVFS yields 50% better power saving for the same reliably level.
P16 and P53 Play Distinct Roles in Different Subtypes of Breast Cancer

Breast cancers are heterogeneous and complex diseases, and subtypes of breast cancers may involve unique molecular mechanisms. The p16INK4a and p53 pathways are two of the major pathways involved in control of the cell cycle. They also play key roles in tumorigenesis. However, whether the roles of these pathways differ in the subtypes of breast cancer is unclear. Therefore, p16 and p53 expression were investigated in different breast cancer subtypes to ascertain their contributions to these cancers. A total of 400 cases of non-invasive ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC), including the major molecular subtypes luminal-A, luminal-B, Her-2, and triple-negative subtypes, and 50 cases of normal controls were compared. Luminal-A cancers expressed the lowest level of p16 among the subtypes in DCIS, and the level of p16 expression was up-regulated in the luminal-A of IDC (P<0.008). Triple-negative breast cancers were characterized by a correlation of p53 overexpression with a high level of p16 expression. Luminal lesion types with high p16 expression in DCIS were found to be more likely to develop into aggressive breast cancers, possibly promoted by p53 dysfunction. Taken together, the present study suggest that p16 expression in luminal-A breast cancers is associated with their progression from DCIS to IDC, and both p53 and p16 expressions are important for the development of triple-negative breast cancers in DCIS and IDC.
Creativity in online gaming: individual and dyadic performance in Minecraft

Objective: This study was designed to examine possible new aspects of creative activity related to virtual environments. Design: The online gaming interface Minecraft was used to construct (on computer screens) complex structures such as buildings from ready-made blocks. Two modes were used: individual and dyadic. Participants (N=49, 29 males and 20 females, 18 to 29 years old, recruited on a snow-ball basis) were required to build staying at a distance two complex structures — a ship and a house; each structure was required to be highly creative, i.e. unusual and never seen before. Creativity was evaluated not by the final structure but by the number of ideas generated by the participants and produced either in practice or verbally. Each participant participated once in an individual and once in a dyadic session; the partners were selected randomly. The participants’ verbal activity and digital operations with the Minecraft interface were recorded using the FastStone Capture software package. All the ideas produced by participants were classified in accordance with the following criteria: type (conceptual, functional, selective, corrective, and intentional); level of the structure which the ideas referred to (the whole structure, a particular component of the structure, or an element of the structure); and the status of the verbalized ideas (implemented or unimplemented). Results and Conclusion: The results show that participants produced significantly more ideas and took significantly less time to build the prescribed structure (a house or a ship) within the individual session compared to the dyadic session. The originality of their ideas was measured by two psychologists independently: the two measures turned out to be close (r=0.876); the number of original ideas produced during individual and dyadic sessions do not differ significantly. Analysis of the implementation of the ideas showed that, within the dyadic sessions, participants produced significantly fewer ideas which were subsequently implemented. For the most part they frequently dropped and left unimplemented ideas referring to the levels of either components or elements of the structure. The results also showed that intentions were the only type of ideas which, after
Comparative study of data mining tools used for clustering

Clustering, a component of data mining is the process of grouping objects into several clusters such that objects in the same cluster have maximum similarity while the objects in different clusters has maximum dissimilarity. Clustering has been used in diverse fields including Text Mining, Pattern recognition, Image analysis, Bioinformatics, Machine Learning, Voice mining, Image processing, Web cluster engines, Whether report analysis etc. To perform the task of clustering, various data mining tools are freely available. These tools have their own features and carry out efficiently the task of generating clusters automatically for a given set of data. This paper discusses seven such tools in detail. A comparative study of these tools has been also done on basis of various parameters as License type of the tool, Programming Language used by tool, Interface provided by the tool, Developer of the tool etc. The goal is to provide the users/researchers all the necessary details about these clustering tools so that it may help them to select an appropriate tool for their use in cluster analysis.
Reviews-Dynamics in Action: Intentional Behavior as a Complex System

What is the difference between a wink and a blink? The answer is important not only to philosophers of mind, for significant moral and legal consequences rest on the distinction between voluntary and involuntary behavior. However, "action theory" -- the branch of philosophy that has traditionally articulated the boundaries between action and non-action, and between voluntary and involuntary behavior -- has been unable to account for the difference. Alicia Juarrero argues that a mistaken, 350-year-old model of cause and explanation -- one that takes all causes to be of the push-pull, efficient cause sort, and all explanation to be prooflike -- underlies contemporary theories of action. Juarrero then proposes a new framework for conceptualizing causes based on complex adaptive systems. Thinking of causes as dynamical constraints makes bottom-up and top-down causal relations, including those involving intentional causes, suddenly tractable. A different logic for explaining actions -- as historical narrative, not inference -- follows if one adopts this novel approach to long-standing questions of action and responsibility.
A Multi-Agent Traffic Control Model Based on Distributed System

With the development of urbanization construction, urban travel has become a quite thorny and imminent problem. Some previous researches on the large urban traffic systems easily change into NPC problems. We purpose a multi-agent inductive control model based on the distributed approach. To describe the real traffic scene, this model designs four different types of intelligent agents, i.e. we regard each lane, route, intersection and traffic region as different types of intelligent agents. Each agent can achieve the real-time traffic data from its neighbor agents, and decision-making agents establish real-time traffic signal plans through the communication between local agents and their neighbor agents. To evaluate the traffic system, this paper takes the average delay, the stopped time and the average speed as performance parameters. Finally, the distributed multi-agent is simulated on the VISSIM simulation platform, the simulation results show that the multi-agent system is more effective than the adaptive control system in solving the traffic congestion. Copyright © 2014 IFSA Publishing, S. L.
Order matters! Harnessing a world of orderings for reasoning over massive data

More and more applications require real-time processing of massive, dynamically generated, ordered data; order is an essential factor as it reflects recency or relevance. Semantic technologies risk being unable to meet the needs of such applications, as they are not equipped with the appropriate instruments for answering queries over massive, highly dynamic, ordered data sets. In this vision paper, we argue that some data management techniques should be exported to the context of semantic technologies, by integrating ordering with reasoning, and by using methods which are inspired by stream and rank-aware data management. We systematically explore the problem space, and point both to problems which have been successfully approached and to problems which still need fundamental research, in an attempt to stimulate and guide a paradigm shift in semantic technologies.
Comparison of the efficiency of different magnetization strategies for a variable speed induction machine drive

This paper introduces a novel experimental procedure to determine the power losses in variable speed induction motors. In the first part, a two-test procedure is designed to determine each contribution to the power losses in an electric drive, in particular the iron losses. These are identified on models from international standards, as a function of the operating conditions, and compared with standard empirical relations such as the Steinmetz equations. In the second part, the resulting models are employed to compare different magnetization strategies for steady-state control purposes. The results show that accounting for advanced models is beneficial in reducing the machine power losses.
Decomposition Techniques for Multilayer Perceptron Training

In this paper, we consider the learning problem of multilayer perceptrons (MLPs) formulated as the problem of minimizing a smooth error function. As well known, the learning problem of MLPs can be a difficult nonlinear nonconvex optimization problem. Typical difficulties can be the presence of extensive flat regions and steep sided valleys in the error surface, and the possible large number of training data and of free network parameters. We define a wide class of batch learning algorithms for MLP, based on the use of block decomposition techniques in the minimization of the error function. The learning problem is decomposed into a sequence of smaller and structured minimization problems in order to advantageously exploit the structure of the objective function. Theoretical convergence results are established, and a specific algorithm is constructed and evaluated through an extensive numerical experimentation. The comparisons with the state-of-the-art learning algorithms show the effectiveness of the proposed techniques.
Impact of the Adipokine Adiponectin and the Hepatokine Fetuin-A on the Development of Type 2 Diabetes: Prospective Cohort- and Cross-Sectional Phenotyping Studies

Background Among adipokines and hepatokines, adiponectin and fetuin-A were consistently found to predict the incidence of type 2 diabetes, both by regulating insulin sensitivity. Objective To determine to what extent circulating adiponectin and fetuin-A are independently associated with incident type 2 diabetes in humans, and the major mechanisms involved. Methods Relationships with incident diabetes were tested in two cohort studies: within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study (628 cases) and the Nurses' Health Study (NHS; 470 cases). Relationships with body fat compartments, insulin sensitivity and insulin secretion were studied in the Tübingen Lifestyle Intervention Program (TULIP; N = 358). Results Circulating adiponectin and fetuin-A, independently of several confounders and of each other, associated with risk of diabetes in EPIC-Potsdam (RR for 1 SD: adiponectin: 0.45 [95% CI 0.37–0.54], fetuin-A: 1.18 [1.05–1.32]) and the NHS (0.51 [0.42–0.62], 1.35 [1.16–1.58]). Obesity measures considerably attenuated the association of adiponectin, but not of fetuin-A. Subjects with low adiponectin and concomitantly high fetuin-A had the highest risk. Whereas both proteins were independently (both p<1.8×10−7) associated with insulin sensitivity, circulating fetuin-A (r = −0.37, p = 0.0004), but not adiponectin, associated with insulin secretion in subjects with impaired glucose tolerance. Conclusions We provide novel information that adiponectin and fetuin-A independently of each other associate with the diabetes risk. Furthermore, we suggest that they are involved in the development of type 2 diabetes via different mechanisms, possibly by mediating effects of their source tissues, expanded adipose tissue and nonalcoholic fatty liver.
University ’ s repository of research publications and other research outputs Collaborative support for community data sharing Conference Item

The Semantic Web aims to create a web of data where contents can be easily discovered and integrated using metadata. Many ontologies have been proposed over the years in different domains, thus producing a semantic heterogeneity that is difficult to manage. Various automated ontology mapping techniques and tools have been developed to facilitate the bridging and integration of distributed data repositories. Nevertheless, such tools are still in need of human supervision to ensure accuracy. The spread of Web 2.0 approaches demonstrate the possibility and the added value of using collaborative techniques for improving data sharing and consensus reaching. In this paper, we describe our prototype for collaborative ontology mapping and data sharing. The possibility to exploit ontology alignments for querying data is a key capability for data sharing in a networked ontology environment.
Multimodal DBN for Predicting High-Quality Answers in cQA portals

In this paper, we address the problem for predicting cQA answer quality as a classification task. We propose a multimodal deep belief nets based approach that operates in two stages: First, the joint representation is learned by taking both textual and non-textual features into a deep learning network. Then, the joint representation learned by the network is used as input features for a linear classifier. Extensive experimental results conducted on two cQA datasets demonstrate the effectiveness of our proposed approach.
Plausible Image Based Soft Shadows Using Occlusion Textures

This paper presents a novel image-based approach to render plausible soft shadows for complex dynamic scenes with rectangular light sources. The algorithm's performance is mostly independent of the scene complexity and the source's size. Occluders and receivers do not need to be separated and no knowledge about the scene representation is required, making the method easy to use. The main idea is to approximate the occlusion in the scene with pre-filtered occlusion textures. The visibility of the light source at a point in space is estimated by accumulating the occlusion caused by each texture, using a novel formula based on probabilities
Laser scanner and Photogrammetry for the survey of Monumental Cemetery in Piazza del Duomo, Pisa (Italy)

The present paper describes both a high-resolution survey of the facade of the Monumental Cemetery in Piazza del Duomo (Pisa, Italy) was realised. It’s performed via the Riegl LMS Z420i laser scanner available at the Department of Civil Engineering (DCE), seat of Topography and Photogrammetry, and a photogrammetric survey of the main entrance portal of the cemetery, in order to provide documentation for restoration purposes. Technological evolution of terrestrial laser scanning equipment has led to an ever increasing precision and performing speed of surveys. As a consequence, laser applications in architectural high-resolution surveys have become a valuable support and make up an excellent integration of traditional photogrammetric surveys. The abilities of surveying at very small scanning steps, along with the very high precision, which can be achieved even by a single measurement, and of near-real-time rendering of the photograph of the surveyed object on the three-dimensional model, allow implementation of the obtained model even for restoration purposes, which require detailed surveys at very great scales.
An efficient clustering framework for relevant web information

As the amount of available information on the Internet grows, it is becoming increasingly difficult for users to find information that is relevant to their needs. Against this backdrop, a need for an automated tool that can find information quickly and easily has surfaced. In this paper, we propose a Clustering Framework for crawling and clustering the necessary information from Web pages. The proposed clustering framework consists of three modules: a preprocessing module, clustering module and community module. Using this framework, we are able to automatically cluster Web pages according to topic and rank them in terms of relevance. We describe this framework, and show the results of our preliminary validation work.
The automatic design of feature spaces for local image descriptors using an ensemble of non-linear feature extractors

The design of feature spaces for local image descriptors is an important research subject in computer vision due to its applicability in several problems, such as visual classification and image matching. In order to be useful, these descriptors have to present a good trade off between discriminating power and robustness to typical image deformations. The feature spaces of the most useful local descriptors have been manually designed based on the goal above, but this design often limits the use of these descriptors for some specific matching and visual classification problems. Alternatively, there has been a growing interest in producing feature spaces by an automatic combination of manually designed feature spaces, or by an automatic selection of feature spaces and spatial pooling methods, or by the use of distance metric learning methods. While most of these approaches are usually applied to specific matching or classification problems, where test classes are the same as training classes, a few works aim at the general feature transform problem where the training classes are different from the test classes. The hope in the latter works is the automatic design of a universal feature space for local descriptor matching, which is the topic of our work. In this paper, we propose a new incremental method for learning automatically feature spaces for local descriptors. The method is based on an ensemble of non-linear feature extractors trained in relatively small and random classification problems with supervised distance metric learning techniques. Results on two widely used public databases show that our technique produces competitive results in the field.
Physical Movement Monitoring Using Body Sensor Networks: A Phonological Approach to Construct Spatial Decision Trees

Monitoring human activities using wearable sensor nodes has the potential to enable many useful applications for everyday situations. Limited computation, battery lifetime and communication bandwidth make efficient use of these platforms crucial. In this paper, we introduce a novel classification model that identifies physical movements from body-worn inertial sensors while taking collaborative nature and limited resources of the system into consideration. Our action recognition model uses a decision tree structure to minimize the number of nodes involved in classification of each action. The decision tree is constructed based on the quality of action recognition in individual nodes. A clustering technique is employed to group similar actions and measure quality of per-node identifications. We pose an optimization problem for finding a minimal set of sensor nodes contributing to the action recognition. We then prove that this problem is NP-hard and provide fast greedy algorithms to approximate the solution. Finally, we demonstrate the effectiveness of our distributed algorithm on data collected from five healthy subjects. In particular, our system achieves a 72.4% reduction in the number of active nodes while maintaining 93.3% classification accuracy.
Impact of JPEG2000 compression on spatial-spectral endmember extraction from hyperspectral data

Hyperspectral image compression has received considerable interest in recent years. However, an important issue that has not been investigated in the past is the impact of lossy compression on spectral mixture analysis applications, which characterize mixed pixels in terms of a suitable combination of spectrally pure spectral substances (called endmembers) weighted by their estimated fractional abundances. In this paper, we specifically investigate the impact of JPEG2000 compression of hyperspectral images on the quality of the endmembers extracted by algorithms that incorporate both the spectral and the spatial information (useful for incorporating contextual information in the spectral endmember search). The two considered algorithms are the automatic morphological endmember extraction (AMEE) and the spatial spectral endmember extraction (SSEE) techniques. Experimental results are conducted using a well-known data set collected by AVIRIS over the Cuprite mining district in Nevada and with detailed ground-truth information available from U. S. Geological Survey. Our experiments reveal some interesting findings that may be useful to specialists applying spatial-spectral endmember extraction algorithms to compressed hyperspectral imagery.
Towards the Classification of the Finnish Internet Parsebank: Detecting Translations and Informality

This paper presents the first results on detecting informality, machine and human translations in the Finnish Internet Parsebank, a project developing a large-scale, web-based corpus with full morphological and syntactic analyses. The paper aims at classifying the Parsebank according to these criteria, as well as studying the linguistic characteristics of the classes. The features used include both lexical and morpho-syntactic properties, such as syntactic n-grams. The results are practically applicable, with an AUC range of 85‐85% for the human, 98% for the machine translated texts and 73% for the informal texts. While word-based classification performs well for the indomain experiments, delexicalized methods with morpho-syntactic features prove to be more tolerant to variation caused by genre or source language. In addition, the results show that the features used in the classification provide interesting pointers for further, more detailed studies on the linguistic characteristics of these texts.