Miracle-dz/UniFAR-FTUs huggingface dataset:
How Eudaimonic Aspect of Subjective Well-Being Affect Transport Mode Choice? The Case of Thessaloniki, Greece

In recent years, the relationship between transportation and subjective well-being has been a major subject. Well-being is a factor that can affect travelers’ psychology and transport mode choice. For this reason, policymakers have attempted to improve travelers’ subjective well-being and promote sustainable modes of transport. For a better understanding of these factors, a questionnaire-based survey was conducted to identify the travel eudaimonia aspect of subjective well-being (comfort, safety, autonomy, self-confidence, physical, and mental health), for the various means of transport in the city of Thessaloniki. During the survey, 300 valid questionnaires were completed. The collection of the above data was followed by statistical analysis. The aim of the analysis was to identify the factors of travel eudaimonia that contributed to the mode choice. For that reason, four ordinal regression models were developed to determine how travel eudaimonia affected the usage frequency of the four available means of transport in the city of Thessaloniki (i.e., private car, bicycle, public transport, walking). Walking was rated higher than other modes in all factors, whilst cycling was rated high in physical and mental health, self-confidence, and autonomy, but low in comfort and safety. Public transport scored very low in all factors, demonstrating the poor quality of service provided by the city’s public transport. Moreover, from the ordinal regression models’ results, it could be demonstrated that travel eudaimonia factors had a significant role to play in mode choice. Recognizing the impact of these factors on transport mode choice is particularly useful for policymakers, researchers, and engineers, as it helps them to make informed decisions about what improvements are needed to promote sustainable modes of transport (mainly walking, cycling, and secondarily, public transport).
RDMA-Based Direct Transfer of File Data to Remote Page Cache

The performance of a distributed file system significantly affects data-intensive applications that frequently execute I/O operations on large amounts of data. Although many modern distributed file systems are geared to provide highly efficient I/O performance, their operations are nonetheless affected by runtime overhead in data transfer between client nodes and I/O servers. A large part of the overhead is caused by memory copies executed by the client interface using the FUSE framework or a special kernel module. In this paper, we propose a method based on InfiniBand RDMA that improves data transfer performance between client and server in a distributed file system. The major characteristic of the method is that it transfers file data directly from a server's memory to the page cache of a client node. The method minimizes memory copies that are otherwise executed in the client interface or the operating system kernel. We implemented the proposed method in the Gfarm distributed file system and tested it using I/O benchmark software and real applications. The experimental results showed that our method effected a performance improvement of up to 78.4% and 256.0% in sequential and random file reads, respectively, and an improvement of up to 6.3% in data-intensive applications.
Asymptotically Efficient Adaptive Allocation Schemes for Controlled I.I.D. Processes: Finite Paramet

The authors consider a controlled i.i. d. (independently identically distributed) process whose distribution is parametrized by an unknown parameter theta belonging to some known parameter space Theta , and a one-step reward associated with each pair of control and the following state of the process. The objective is to maximize the expected value of the sum of one-step rewards over an infinite horizon. By introducing the loss associated with a control scheme, it is shown that the problem is equivalent to minimizing this loss. Uniformly good adaptive control schemes are defined and emphasized. A lower bound on the loss associated with any uniformly good control scheme is developed. Finally, an adaptive control scheme is constructed whose loss equals the lower bound, and is therefore asymptotically efficient. >
A Distributed Dynamic Parallel Algorithm for SIFT Feature Extraction

This paper deals with the issue of developing efficient algorithms for accelerating SIFT (Scale Invariant Feature Transform) features extraction under distributed environment. The proposed distributed dynamic parallel algorithm (DDP-SIFT) using a special data parallel approach that divides the Gauss Scale Space by octave aimed at acquiring large image blocks which is of great importance in some application. To make this approach effective, steps of building Gauss Scale Space are changed, and only the prerequisite part which is only 1/13 of the whole pyramid will be produced before tasks allocation, and Allocation Data Quantity (ADQ) is decreased by 13 times. Data blocks are assembled as tasks maintained in task lists, and dynamically allocated to Computing Nodes. A refined-blocking approach is proposed to further improve load balance. Our investigations show that the proposed algorithm has remarkable performance on accelerating SIFT features extraction while pursuing large data blocks.
An Enhancement in Clustering for Sequential Pattern Mining through Neural Algorithm Using Web Logs

An Organization need to understand their customers' behavior, preferences and future needs which depend upon past behavior. Web Usage Mining is an active research topic in which customers session clustering is done to understand the customers activities. This paper investigates the problem of mining frequent pattern and especially focuses on reducing the number of scans of the database and reflecting the importance of pages. In the present work a novel method of pattern mining is presented to solve the problem through FSTSOM. In this Paper, the proposed method is an improvement to the web log mining method and to the online navigational pattern forecasting. Here, Neural based approach i.e. Self Organizing Map (SOM) is used for clustering of sessions as a trend analysis. SOM depends on the clustering performance with the number of requests. In the proposed method, using the SOM algorithm for Frequent Sequential Traversal Pattern Mining called FSTSOM. In this method, first using SOM algorithm and getting some cluster of web-logs. Then loading that web-log cluster, which is nearly related to frequent pattern. After that applying Min-Max Weight of Page in Sequential Traversal Pattern. Finally, established good prediction with the number of data and the excellence of the results.
Young children’s mathematical learning opportunities in family shopping experiences

ABSTRACT This article reports on a qualitative pilot study which documented the ways in which young children and their families notice, explore, and talk about mathematical concepts and processes as they participate in family shopping experiences. Six families, with children ranging from 12 months to 10 years, were video- and audio-recorded whilst shopping at 1 of 2 large retailers. The data reveal that young children and their families notice, explore, and talk about a great deal of implicit and explicit mathematics whilst shopping together. All of the children displayed instances of mathematical noticing, with the children ‘marking’ what they had noticed in both verbal and non-verbal forms. Furthermore, all six families explored and talked about what was noticed whilst shopping together. This study contributes new knowledge about the ways in which children and families interact with mathematics in community contexts.
Nested Pattern Queries Processing Optimization over Multi-dimensional Event Streams

Recently, many modern applications have required complex event processing technology to analyze multi-dimensional stream big data available in real-time data feeds. To address the above requirement, we develop a novel real-time event stream processing method called multi-query optimization strategy (MQOS). MQOS aims at exploiting not only common sub-expressions among nested event pattern queries, but also replicas of the appropriate common operators' results for the queries needed to minimize recalculation and re-communication costs. We first design a triaxial hierarchy consisting of nested query pattern, nested query concept and operator type hierarchies to specify the relationship among the sub-expressions of queries. Next, based on the triaxial hierarchy, we devise a cost-based heuristic to find an optimized query execution plan with minimum costs of operators and communications. We then propose three reuse schemes of common sub-expressions: nested query pattern-based, nested query concept-based and operator type-based reuse schemes. By integrating the optimized query execution plan-find approach with the three reuse schemes, we present the MQOS to achieve nested pattern queries processing optimization. Finally, our experiments tested on StreamBase under different workload conditions demonstrate the superiority of MQOS.
Attacking Embedded Systems through Power Analysis

--------------------------------------------------------------- ABSTRACT----------------------------------------------------------------------Embedded Systems are being used for the development and implementation of Safety and Mission Critical Systems. Malfunctions of such type of embedded systems will lead to disasters at times. The embedded systems must be fully secured from outside intervention in order to have effective functioning as well as to provide protective environment to these mission critical systems. There are several attacking systems discussed in the literature each requiring a kind of counter attacking system. Power Analysis and variations of power analysis are the significant attacking mechanisms discussed in the literature. Crypto servers are the main areas of attacking as they deal with securing the data that flow in-between several components of the embedded systems. Most of the attacking systems suggested in the literature suffer from lack of experimental models to emulate the attacking system. An attacking system could be amply proved when several samples of data are used for attacking and the samples of data provide for knowledge base. In this paper an experimental setup is proposed which is an embedded system by itself for creation of a Knowledgebase which shall form the basis for attacking. The experimental setup required for undertaking the actual attacking with the usage of the knowledgebase is also presented. Further, the proposed attacking system is applied for mission critical system and the experimental results obtained through the simulation are also presented.
Antibacterial and Antibiofilm Activity of Methanolic Plant Extracts against Nosocomial Microorganisms

Biofilm is a complex microbial community highly resistant to antimicrobials. The formation of biofilms in biotic and abiotic surfaces is associated with high rates of morbidity and mortality in hospitalized patients. New alternatives for controlling infections have been proposed focusing on the therapeutic properties of medicinal plants and their antimicrobial effects. In the present study the antimicrobial and antibiofilm activities of 8 methanolic plant extracts were evaluated against clinical isolated microorganisms. Preliminary screening by diffusion well assay showed the antimicrobial activity of Prosopis laevigata, Opuntia ficus-indica, and Gutierrezia microcephala. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined ranging from 0.7 to >15 mg/mL. The specific biofilm formation index (SBF) was evaluated before and after the addition of plant extracts (MBC × 0.75). Opuntia ficus-indica caused the major reduction on SBF in dose-dependent manner. Cytotoxic activity of plant extracts was determined using brine shrimp lethality test (Artemia salina L.). Lethal Dose concentration (LD50 values) of the plant extracts was calculated. LD50 values for P. laevigata and G. microcephala were 141.6 and 323.3 µg/mL, respectively, while O. ficus-indica showed a slight lethality with 939.2 µg/mL. Phytochemical analyses reveal the presence of flavonoids, tannins, and coumarines.
Computational Efficiency of Optimized Shortest Path Algorithms

1. ABSTRACT The main purpose of this study is to evaluate the computational efficiency of optimized shortest path algorithms. Our study establishes the relative order of the shortest path algorithms with respect to their known computational efficiencies is not significantly modified when the algorithms are adequately coded. An important contribution of this study is the inclusion of the re-distributive heap algorithm. In spite of having its best theoretical efficiency, the re-distributive heap algorithm does not emerge as the best method in terms of computational efficiency. The complexity of Dijkstra's algorithm depends heavily on the complexity of the priority queue Q. If this queue is implemented naively (i.e. it is re -ordered at every iteration to find the minimum node), the algorithm performs in O(n 2 ), where n is the number of nodes in the graph. With a real priority queue kept ordered at all times, as we implemented it, the complexity averages O(n log m). The logarithm function stems from the collections class, a red-black tree implementation which performs in O(log (m)).
Powder metallurgy of the porous Ti-13Nb-13Zr alloy of different powder grain size

ABSTRACT The objective of the present project was to determine the effects of powder granulation (fraction of grain size) for the Ti-13Nb-13Zr alloy, produced by powder metallurgy, on its porosity, grain cohesion, compressive strength, and Young`s modulus. Two powder fractions, 45–105 µm, and 106–250 µm were applied. The 50 mass pct of NH4HCO3 was added as a space holder. The specimens were in compaction stage uniaxially pressed at pressure 625 MPa for 120 s. The brown bodies were sintered at a temperature 1150°C for 3.5 h. The well-joined grains were observed for both powder granulations. The increase in powder granulation resulted in an increase of porosity from 51% to 59%, and it was only 30% with no space holder used. The compressive strength increased with decreased porosity from 57 to 236 MPa. Young`s modulus was measured as 4.8 GPa for finer powder and 0.9 GPa for coarser powder. It is evident from the results obtained that the applied process parameters, the space holder and its fraction, and the use powder granulation between 45 and 105 µm bring out the porous material fulfilling mechanical and biological requirements specific of load-bearing titanium implants.
A Robust Iris Identification System Based on Wavelet Packet Decomposition and Local Comparisons of the Extracted Signatures

This paper presents a complete iris identification system including three main stages: iris segmentation, signature extraction, and signature comparison. An accurate and robust pupil and iris segmentation process, taking into account eyelid occlusions, is first detailed and evaluated. Then, an original wavelet-packet-based signature extraction method and a novel identification approach, based on the fusion of local distance measures, are proposed. Performance measurements validating the proposed iris signature and demonstrating the benefit of our local-based signature comparison are provided. Moreover, an exhaustive evaluation of robustness, with regards to the acquisition conditions, attests the high performances and the reliability of our system. Tests have been conducted on two different databases, the well-known CASIA database (V3) and our ISEP database. Finally, a comparison of the performances of our system with the published ones is given and discussed.
A Segmentation Transfer Approach for Rigid Models

In this paper, we propose using a segmented example model to perform a semantic oriented segmentation of rigid 3D models of the same class (tables, chairs, etc.). For this, we introduce an alignment method that maps the meaningful parts of the models and we develop a novel approach based on random walks to transfer a consistent segmentation from the example to the target model. The example-driven segmentation is fast and entirely automatic. We demonstrate the effectiveness of our approach through multiple results of inter-shape segmentation transfer presented for different classes of rigid models.
Shallow Classifier with Sampling for Sleep Stage Classification of Autism Patients

Autism is a brain development disorder that affects the patient's ability to communicate and interact with others. Most people with autism get sleep disorders. But they have some difficulty to communicate, so this problem is getting worse. The alternative that can be done is to detect sleep disorders through polysomnography. One of the test purposes is to classify the sleep stages. The doctors need a long time to process it. This paper presents an automatic sleep stage classification. The classification was based on the shallow classifiers, namely naive Bayes, k-nearest neighbor (KNN), multi-layer perceptron (MLP), and C4.5 (a type of decision tree). On the other hand, this dataset has a class imbalance problem. As a solution, this study carried out the mechanism of resampling. The results show that the use of d as a measure of the uniformity of data distribution greatly influenced the classification performance. The higher d, the more uniform the distribution of data (0 <= d <= 1). The performance with d = 1 was higher than d = 0. On the other hand, KNN was the best classifier. The highest accuracy and F-measure were 83.07 and 82.80 respectively.
New Product Development: Delivering Evidence of What Works

New product developers are required to understand and apply a wide variety of processes and tools as they produce new devices or services. Individuals who are seeking alternative or new methods for their process may often be overwhelmed by the amount of academic and practice literature documenting “best practices. ” Unfortunately, new product developers may not have sufficient time to invest in exploring alternative strategies, resulting in the repetition of old possibly outdated practices. This paper presents the method and preliminary results of a scoping review designed to consolidate information on effective new product development practices into a comprehensive knowledge base. New product developers will enjoy the ease of finding alternative effective practices, while researchers will find value in the consolidated presentation of new product development practices and study methodologies that lend themselves to systematic reviews.
Doppler Frequency Offset Estimation in OFDM systems

This paper presents frequency offset estimation algorithm in wireless OFDM cellular system such as 3G-LTE [1]. We first analyze the method of frequency offset estimation in general OFDM system. The analysis will show that the value of frequency offset estimation in cell boundary is inaccurate. Because doppler frequencies of signals transmitted from different base stations are different, the error of frequency offset estimation value is generated. We show how proposed algorithm can reduce the error of frequency offset estimation. Results of simulation show that the utilization of proposed algorithm can reduce the error of frequency offset estimation considerably. Proposed frequency offset estimation algorithm can increase overall system capacity because of performance improvement in cell boundary. Especially, 16-QAM or 64-QAM modulations for high data rate transmission is extremely sensitive to frequency offset, therefore proposed algorithm increases channel capacity for user in cell boundary.
Addressing Term Mismatch in Information Retrieval by Using Document Expansion

A common approach to solving the term mismatch between a user’s query and relevant documents is query expansion, which augments a user’s query with additional, related terms in order to increase coverage of relevant documents. The system proposed in this paper presents an alternative means of ameliorating the term mismatch problem for certain corpora. Instead of using query expansion, this system uses document expansion. Specifically, for corpora containing inter-document references (e.g., links or citations), this system uses those references to expand the set of terms contained within a given document. Preliminary evaluation of the results of using document expansion in a sample implementation indicates that this approach can provide mild benefit to the perceived helpfulness of a user’s query.
The Campylobacter jejuni NADH:Ubiquinone Oxidoreductase (Complex I) Utilizes Flavodoxin Rather than NADH

ABSTRACT Campylobacter jejuni encodes 12 of the 14 subunits that make up the respiratory enzyme NADH:ubiquinone oxidoreductase (also called complex I). The two nuo genes not present in C. jejuni encode the NADH dehydrogenase, and in their place in the operon are the novel genes designated Cj1575c and Cj1574c. A series of mutants was generated in which each of the 12 nuo genes (homologues to known complex I subunits) was disrupted or deleted. Each of the nuo mutants will not grow in amino acid-based medium unless supplemented with an alternative respiratory substrate such as formate. Unlike the nuo genes, Cj1574c is an essential gene and could not be disrupted unless an intact copy of the gene was provided at an unrelated site on the chromosome. A nuo deletion mutant can efficiently respire formate but is deficient in α-ketoglutarate respiratory activity compared to the wild type. In C. jejuni, α-ketoglutarate respiration is mediated by the enzyme 2-oxoglutarate:acceptor oxidoreductase; mutagenesis of this enzyme abolishes α-ketoglutarate-dependent O2 uptake and fails to reduce the electron transport chain. The electron acceptor for 2-oxoglutarate:acceptor oxidoreductase was determined to be flavodoxin, which was also determined to be an essential protein in C. jejuni. A model is presented in which CJ1574 mediates electron flow into the respiratory transport chain from reduced flavodoxin and through complex I.
Fast dynamic time warping using low-rank matrix approximations

Dynamic Time Warping (DTW) is a computationally intensive algorithm and computation of a local (Euclidean) distance matrix between two signals constitute the majority of the running time of DTW. In earlier work, a matrix multiplication based formulation for computing the Euclidean distance matrix was proposed leading to faster DTW. In this work, we propose the use of (i) direct low-rank factorization of template matrix and (ii) optimal low-rank factorization of the Euclidean distance matrix, leading to further computational speedup without significantly affecting accuracy. We derive a condition for achieving computational savings using low-rank factorizations. We analyze the condition for achieving computational savings over these low-rank factorizations by using separate low-rank factors for each class of templates to further reduce the rank within each class. We show that, using per-class factors, we can achieve significant average rank reduction with further computational savings for applications with high inter class variability in the feature space. We observe that our low-rank factorization methods result in lesser errors in the Euclidean distance matrix compared to Principal Component Analysis (PCA). We analyze the error behavior using spoken digit and heart auscultation sound datasets and achieve speedups of upto 4.59x for spoken digit recognition task on a dual core mobile CPU.