Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized retrieval of sports video

There has been a growing demand for effective access to video information from media archives in recent years. Personalized video retrieval is one of the most challenging issues and has spurred a significant interest in many research communities. In this paper, a novel approach is proposed to achieve personalized retrieval of sports video, which includes two research tasks: semantic annotation of sports video and acquisition of user's preference. For semantic annotation, a multi-modal framework is employed to detect sports event and index the sports video content. Web-casting text, as external information, is utilized to detect semantic events in sport videos. The semantic concepts and keywords included in the web-casting text are extracted to annotate and index the sport event segments automatically. For user's preference acquisition, relevance feedback is applied to model user's preference and non-preference, and re-ranking is used to refine the results. First, the user is asked to label some video segments as desirable and undesirable. Then, we use these labels to infer the user's interesting points (e.g. the player, the event type, the team, etc.) by analysis of text keywords; the low-level video features are also adopted as a supplementary to reflect the user's preference. The overall new rank of the results is the combination of the user's high-level and low-level preference. Experiments conducted on real-world soccer game videos show that the proposed method has an encouraging performance.
Ontology alignment argumentation with mutual dependency between arguments and mappings

For a successful communication, autonomous entities (e.g. agents, web services, peers) must reconcile vocabulary used in their ontologies. The result is a set of mappings between ontology entities. Since each party might have its own perspective about what are the best mappings, conflicts will arise. Toward a mapping consensus building between information exchanging parties, this paper proposes an approach based on a formal argumentation framework, whose existing ontology matching algorithms generate the mappings, which are further interpreted into semantic arguments employed during the argumentation. The proposal models a mutual dependency between the mappings and arguments, which goes beyond the state of the art in argumentation-based ontology alignment negotiation, better reflecting the requirements of the task.
University social network benefits analysis and proposed framework

Social learning is a natural way of acquiring knowledge and approaching various problems. In high education, students widely use social learning which is to a large extent facilitated by todays' popular social network applications. However, as of yet high education systems typically do not recognise the value of the data produced by or embedded within these social network applications. In this paper we present survey results through which we examine certain recent learning habits among students in Croatia. We predominantly investigated their usage of Facebook, today's leading social network application, specifically for the purposes of learning. Based on these findings, and taking into account common communication problems we perceive in our work, we propose a framework for University Social Network construction. We presume various usages of it by different stakeholders, propose node construction within the network compliant with educational and research processes and recommend flexible privacy levels for communication units.
Forecasting the Tehran Stock Market by Artificial Neural Network

One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. Potential significant benefits of solving these problems motivated extensive research for years. In this paper, computational data mining methodology was used to predict seven major stock market indexes. Two learning algorithms including Linear Regression and Neural Network Standard feed-forward back prop (FFB) were tested and compared. The models were trained from four years of historical data from March 2007 to February 2011 in order to predict the major stock prices indexes in the Iran (Tehran Stock Exchange). The performance of these prediction models was evaluated using two widely used statistical metrics. We can show that using Neural Network Standard feed-forward back prop (FFB) algorithm resulted in better prediction accuracy. In addition, traditional knowledge shows that a longer training period with more training data could help to build a more accurate prediction model. However, as the stock market in Iran has been highly fluctuating in the past two years, this paper shows that data collected from a closer and shorter period could help to reduce the prediction error for such highly speculated fast changing environment.
TrIMPI: A Data Structure for Efficient Pattern Matching on Moving Objects

Managing movement data effi ciently often requires the exploitation of some indexing scheme. Taking into account the kind of queries issued to the given data, several indexing structur es have been proposed which focus on spatial, temporal or spatio-temporal data. Since all these approaches consider only raw data of moving objects, they may be well-suited if the queries of interest c ontain concrete trajectories or spatial regions. However, if the q uery consists only of a qualitative description of a trajectory, e.g . by stating some properties of the underlying object, sequential scans on the whole trajectory data are necessary to compute the property, even if an indexing structure is available. The present paper presents some results of an ongoing work on a data structure for Trajectory Indexing using Motion Property Information (TrIMPI). The proposed approach is flexible since it allows the user to define application-specific properties of tr ajectories which have to be used for indexing. Thereby, we show how to effi ciently answer queries given in terms of such qualitative descriptions. Since the index structure is built on top of ordi nary data structures, it can be implemented in arbitrary database management systems.
In vitro and in vivo studies of pirarubicin-loaded SWNT for the treatment of bladder cancer

Intravesical chemotherapy is an important part of the treatment for superficial bladder cancer. However, the response to it is limited and its side effects are extensive. Functional single-walled carbon nanotubes (SWNT) have shown promise for tumor-targeted accumulation and low toxicity. In the present study, we performed in vivo and in vitro investigations to determine whether SWNT-based drug delivery could induce high tumor depression in rat bladder cancer and could decrease the side effects of pirarubicin (tetrahydropyranyl-adriamycin, THP). We modified SWNT with phospholipid-branched polyethylene glycol and constructed an SWNT-THP conjugate via a cleavable ester bond. The cytotoxicity of SWNT-THP against the human bladder cancer cell line BIU-87 was evaluated in vitro. Rat bladder cancer in situ models constructed by N-methyl-N-nitrosourea intravesical installation (1 g/L, 2 mg/rat once every 2 weeks for 8 weeks) were used for in vivo evaluation of the cytotoxicity of SWNT and SWNT-THP. Specific side effects in the THP group including urinary frequency (N = 12), macroscopic hematuria (N = 1), and vomiting (N = 7) were identified; however, no side effects were observed with SWNT-THP treatment. Flow cytometry was used to assess the cytotoxicity in vitro and in vivo. Results showed that SWNT alone did not yield significant tumor depression compared to saline (1.74 ± 0.56 and 1.23 ± 0.42%) in vitro. SWNT-THP exhibited higher tumor depression than THP-saline in vitro (74.35 ± 2.56 and 51.24 ± 1.45%) and in vivo (52.46 ± 2.41 and 96.85 ± 0.85%). The present findings indicate that SWNT delivery of THP for the treatment of bladder cancer leads to minimal side effects without loss of therapeutic efficacy. Therefore, this nanotechnology may play a crucial role in the improvement of intravesical treatment of bladder cancer. Key words: Single-walled carbon nanotubes; Bladder cancer; Drug vehicle; THP; Intravesical chemotherapy
Categorical Probability Proportion Difference (CPPD): A Feature Selection Method for Sentiment Classification

Sentiment analysis is to extract the opinion of the user from of the text documents. Sentiment classification using machine learning methods face problem of handling huge number of unique terms in a feature vector for the classification. Thus it is required to eliminate the irrelevant and noisy terms from the feature vector. Feature selection methods reduce the feature size by selecting prominent features for better classification. In this paper, a new feature selection method namely Probability Proportion Difference (PPD) is proposed which is based on the probability of belongingness of a term to a particular class. It is capable of removing irrelevant terms from the feature vector. Further, a Categorical Probability Proportion Difference (CPPD) feature selection method is proposed based on Probability Proportion Difference (PPD) and Categorical Proportion Difference (CPD). CPPD feature selection method is able to select the features which are relevant and capable of discriminating the class. The performance of the proposed feature selection methods is compared with the CPD method and Information Gain (IG) method which has been identified as one of the best feature selection method for sentiment classification. Experimentation of proposed feature selection methods was performed on two standard datasets viz. movie review dataset and product review (i.e. book) dataset. Experimental results show that proposed CPPD feature selection method outperforms other feature selection method for sentiment classification.
DOA-based beamforming for multi-cell massive MIMO systems

This paper proposes a direction-of-arrival (DOA)-based beamforming approach for multi-cell massive multiple-input multiple-output systems with uniform rectangular arrays (URAs). The proposed approach utilizes the steering vectors of the URA to form a basis of the spatial space and selects the partial space for beamforming according to the DOA information. As a result, the proposed approach is of lower computational complexity than the existing methods which utilize the channel covariance matrices. Moreover, the analysis demonstrates that the proposed approach can eliminate the interference in the limit of infinite number of the URA antennas. Since the proposed approach utilizes the multipaths to enhance the signal rather than discarding them, the proposed approach is of better performance than the existing low-complexity method, which is verified by the simulation results.
An efficient system for the detection of exudates in colour fundus images using image processing technique

Diabetic Retinopathy is the major cause of blindness in many diabetic patients. Automatic detection of exudates in retinal images can assist in early screening of Diabetic Retinopathy. Several techniques can achieve good performance on a good quality retinal images. But when the image is of low quality, we need a new method. In this paper, we presented a novel method for the detection of exudates in low quality retinal images. The colour retinal images are pre-processed by a hyperbolic median filter and then segmented using fuzzy c-means clustering algorithm. After segmenting the images, a set of features based on colour, size and texture are extracted. Then these features are optimized using Particle Swarm Optimization (PSO) technique. Finally the features are classified using a recursive Support Vector Machine (SVM) Classifier. The proposed method achieves an accuracy of 98% and predictivity of 98.5% for the identification of exudates.
JP2: collecting dynamic bytecode metrics in JVMs

The collection of dynamic metrics is an important part of performance analysis and workload characterization. We demonstrate JP2, a new tool for collecting dynamic bytecode metrics for standard Java Virtual Machines (JVMs). The application of JP2 is a three-step process: First, an online step instruments the application for profiling. Next, the resulting profile is dumped in an appropriate format for later analysis. Finally, the desired metrics are computed in an offline step. JP2's profiles capture both the inter-procedural and the intra-procedural control flow in a callsite-aware calling-context tree, where each node stores, amongst others, the execution count for each basic block of code. JP2 uses portable bytecode instrumentation techniques, is Open Source, and has been tested with several production JVMs.
A dorsal hand vein pattern recognition algorithm

An algorithm for dorsal hand vein pattern recognition is presented in the paper. To improve the recognition ratio, the vein skeleton extracting with little distortion is very important. Firstly, the algorithm acquires a clean, one-pixel-width skeleton with little distortion after a series of processes: size and gray normalizing, Gaussian lowpass and median filtering, NiBlack local dynamic thresholding segmenting, area thresholding, morphological opening and closing, median filtering, conditional thinning, spurs pruning. Then, the seven corrected moment invariants of the vein skeleton are extracted as the feature vector. At last, the feature vector is input into SVM for training and recognition. Experiment shows the algorithm achieves a higher recognition ratio of 95.5%.
A ultra-low power channel selection filter for low-IF FSK biomedical receivers

Ultra-low power (ULP) receivers can be applied in wearable and implantable biomedical sensors. In this paper, a ULP band-pass filter designed as a channel selection filter for low-IF FSK biomedical receivers is implemented in 1.8 V 0.18 μm CMOS process. Under power consumption of 200 μW, the measured central frequency and bandwidth is 1.5 MHz and 330 kHz, respectively. As applied in a low-IF FSK biomedical receiver, data rate of 330 kb/s can be achieved.
Palmprint and Handgeometry Recognition using FAST features and Region properties

Biometrics recognition system is more reliable and popular. In this paper we describe a palmprint and handgeometry based person identification consisting of three main steps preprocessing techniques such as morphological operations. The feature extraction techniques such as FAST feature algorithm and region properties is used to independently extract palmprint and handgeometry features. Feature matching with euclidean distance classifier. These techniques are more reliable and faster than traditional techniques used. We finally conclude that the proposed methodology has better performance . This is supported by our experiments which are able to achieve recognition rate for palmprint 100 % and for handgeometry 93.75 %.
Author's response to reviews Title: Polymorphisms in the xenobiotic transporter Multidrug Resistance 1 (MDR1) gene and interaction with meat intake in relation to risk of colorectal cancer in a Danish prospective case-cohort study. Authors:

Background: Genetic variation that modifies the gut barrier function or the inflammatory response may modify the risk of colorectal cancer (CRC). The intestinal xenobiotic transporters, Multidrug Resistance 1 (MDR1/ABCB1) and Breast Cancer Resistance Protein (BCRP/ABCG2) contribute to the intestinal homeostasis by exporting a broad spectrum of substrates including potential carcinogens from the intestinal lining into the lumen. Cyclooxygenase-2 (COX-2/PTGS2/PGHS2) plays a key role in the intestinal immune response to luminal antigens. The aim of this study was to investigate if polymorphisms in these genes were associated with CRC risk, and to investigate possible interactions with lifestyle factors such as alcohol consumption, smoking, meat consumption and use of non-steroidal anti-inflammatory drugs (NSAID). Methods: MDR1 C3435T (rs1045642) and intron3 G-rs3789243-A, BCRP C421A (rs2231142), and COX-2 A-1195G (rs689466), G-765C (rs20417), and T8473C (rs5275) polymorphisms were assessed together with lifestyle factors in a nested case-cohort study of 364 cases and a random cohort sample of 772 participants from the Danish prospective Diet, Cancer and Health study. Results: Carriers of the variant allele of MDR1 intron 3 were at 1.52-fold higher risk of CRC compared with homozygous wild type allele carriers (Incidence rate ratio (IRR) =1.52. 95 % Confidence Interval (CI): 1.12-2.06). Homozygous carriers of the T-allele of MDR1 C3435T had a lower risk of CRC compared with homozygous C-allele carriers (IRR= 0.66. 95% CI: 0.45-0.98). There was interaction between the studied MDR1 polymorphisms and intake of red and processed meat in relation to CRC risk. Homozygous MDR1 C3435T Callele carriers were at 8% increased risk pr 25 g meat/day (CI: 1.00-1.16) whereas variant allele carriers were not at increased risk (p for interaction = 0.02). There was interaction between NSAID use and MDR1 C3435T and COX-2 T8473C (p-values for interaction 0.001 and 0.04, respectively). Use of NSAID was associated with an increased risk among homozygous wild type allele carriers (MDR1 C3435T: IRR=2.34 (1.22-4.48) and COX-2 T8473C: IRR=1.38 (0.89-2.14), but not among variant allele carriers. Conclusions: Two polymorphisms in MDR1 were associated with higher risk of CRC and there was interaction between the polymorphisms and meat intake in relation to CRC risk.
SEARCHING PARADIGMS FOR COMMUNICATION OF WORK ORGANISATIONS

The purpose of the paper is to discuss communication of work organisations in the light of previous and present research and to suggest paradigms for organisations of postmodernist era. Firstly, a summary is done about the theoretical approaches to organisation and organisational communication aimed at finding common denominators. This is done by using some well-known metaphors of organisation and schools of communication. On the basis of this knowledge, three communication paradigms are formed: the rational, the dissipative, and the responsible dialogue. Secondly, the paradigms are reflected towards a recent case study of a knowledge intensive organisation. The study gave evidence for two paradigms, the rational one and the responsible one. It also suggested that more than one paradigm may prevail simultaneously in an organisation.
OCM—a monitoring system for interoperable tools

On-line tools for parallel and distributed programs require a facility to observe and possibly manipulate the programs’ run-time behavior, a so called monitoring system. Currently, most tools use proprietary monitoring techniques that are incompatible to each other and usually apply only to specific target plattforms. The On-line Monitoring Interface Specification (OMIS) is the first specification of a universal interface between on-line tools and a monitoring system, which is not targeted towards a single specific class of tools (e.g. performance analyzers). Instead, it can serve very different kinds of tools at the same time, thus enabling interoperable, portable and uniform tool environments. The paper gives an introduction into the basic concepts of OMIS and presents the design and implementation of an OMIS compliant monitoring system (OCM). Special attention is given to the techniques needed to deal with the distributed target system. the parallel, distributed application, a so called monitoring module. Usually every tool includes a monitoring module that is specifically adapted to its needs. Since these modules typically use special low level interfaces to the operating system and possibly the underlying hardware, these layers tend to be incompatible to those required by other tools. As a result of this, it is impossible for a developer to connect two or more tools to a running application at the same time. Even worse, if he wants to use two different tools, one after the other, he usually has to re-compile, re-link, or otherwise re-process his application. In this paper we will describe the design and implementation of a universally usable on-line monitoring infrastructure that is general enough to support a wide variety of tools and their interoperable use. We will first describe existing approaches, analyze the requirements for a general monitoring interface, then introduce our approach and finally describe our implementation.
Mathematical Modeling for Network Selection in Heterogeneous Wireless Networks — A Tutorial

In heterogeneous wireless networks, an important task for mobile terminals is to select the best network for various communications at any time anywhere, usually called network selection. In recent years, this topic has been widely studied by using various mathematical theories. The employed theory decides the objective of optimization, complexity and performance, so it is a must to understand the potential mathematical theories and choose the appropriate one for obtaining the best result. Therefore, this paper systematically studies the most important mathematical theories used for modeling the network selection problem in the literature. With a carefully designed unified scenario, we compare the schemes of various mathematical theories and discuss the ways to benefit from combining multiple of them together. Furthermore, an integrated scheme using multiple attribute decision making as the core of the selection procedure is proposed.
Efficient rank join with aggregation constraints

We show aggregation constraints that naturally arise in several applications can enrich the semantics of rank join queries, by allowing users to impose their application-specific preferences in a declarative way. By analyzing the properties of aggregation constraints, we develop efficient deterministic and probabilistic algorithms which can push the aggregation constraints inside the rank join framework. Through extensive experiments on various datasets, we show that in many cases our proposed algorithms can significantly outperform the naive approach of applying the state-of-the-art rank join algorithm followed by post-filtering to discard results violating the constraints.
Metabolic alterations accompanying oncogene-induced senescence

Senescence is defined as a stable cell growth arrest. Oncogene-induced senescence (OIS) occurs in normal primary human cells after activation of an oncogene in the absence of other cooperating oncogenic stimuli. OIS is therefore considered a bona fide tumor suppression mechanism in vivo. Indeed, overcoming OIS-associated stable cell growth arrest can lead to tumorigenesis. Although cells that have undergone OIS do not replicate their DNA, they remain metabolically active. A number of recent studies report significant changes in cellular metabolism during OIS, including alterations in nucleotide, glucose, and mitochondrial metabolism and autophagy. These alterations may be necessary for stable senescence-associated cell growth arrest, and overcoming these shifts in metabolism may lead to tumorigenesis. This review highlights what is currently known about alterations in cellular metabolism during OIS and the implication of OIS-associated metabolic changes in cellular transformation and the development of cancer therapeutic strategies.