Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual detection of objects by mobile agents using CBVIR techniques of low complexity

Visual search for objects of interest in complex environment is an important (and still challenging) problem in mobile robotics. In particular, the usage of content-based visual information retrieval (CBVIR) methods, which are a natural choice for such tasks, is often constrained by the real-time requirements, and the mobility of searching agents is sometimes not sufficiently exploited in the search model. In this paper, a CBVIR-based scheme is proposed, which takes into account motion of the searching agents to achieve a low-cost and high-speed detection of objects of interest in cluttered scenes, with good overall performances. We combine standard CBVIR tools, i.e. MSER detector and SIFT descriptor (quantized into sufficiently large vocabularies) assuming additionally that objects become objects of interest only when approached closely enough by the mobile agent, i.e. when seen at an adequately large scale. Thus, an object of interest is considered detected only if a sufficient number of keypoints from the current video-frame are matched (including the corresponding matches of scales) to the keypoints from the database images of the object. Preliminary experiments on a limited-size dataset confirm performances of the scheme, although in the classical task of video-frame retrieval the scheme cannot compete with more sophisticated CBVIR algorithms. The scheme can prospectively become more flexible if combined with a range-finding device so that the approximate distances to the scene components within the currently inspected part of the image can be used to proportionally modify the scale correspondences.
An efficient and secure pairing-based fair blind signature scheme with message recovery

In this paper, we propose a new fair blind signature scheme with message recovery using pairing-based self-certified public key cryptosystems. Preserving the merits inherent in pairing-based cryptosystems, it can possess fewer bits to achieve the higher security level. In addition, our new scheme has the advantage that the authentication of the public key can be accomplished with the verification of the fair blind signature in a logically single step. Furthermore, the fairness of blind signature with message recovery can be actually achieved in our proposed scheme. Based on the proposed security proofs and performance evaluation, we affirm that we not only improve the efficiency of the previously proposed schemes, but also achieve the essential properties of blind signature with provable security.
A Multi-modal Information Presentation Method for Performers in Stuffed Suits

Performers in stuffed suits cannot act naturally because it is difficult for them to aware surrounding situations clearly only using peep holes. To solve this problem, our research group proposed a system for supporting performers in stuffed suits. By using this system, the performer can see their surrounding information around him/her through camera images attached on the stuffed suit. However, there are two problems when actually using the system. The first is that the dependability of the system is not guaranteed, and the other is that the performer cannot look at the camera image well enough while performing. The main purpose of our research is to apply a dependable method for wearable system by device bypassing for supporting performers in stuffed suits. In this paper, we implemented three information presenting devices and investigated the relationship between a performer's surroundings and cognition difficulty with information presentation.
Understanding the Gaps Between Turkish Teachers’ Beliefs and Practices for Dealing With Preschoolers’ Peer Relationship Problems

ABSTRACT Research Findings: This study explores the beliefs, self-reported practices, and observed practices of Turkish preschool teachers toward children’s peer relationship problems as well as the gap between their beliefs and practices. Five female teachers of 5-year-old children were selected as participants for this multiple case study. Approximately 79 hr of observation was conducted on the children’s daily routines and activities in a natural classroom environment, which specifically included the teachers’ problem-solving strategies while confronting peer relationship problems. The participants were also interviewed regarding their beliefs and strategies for solving peer relationship problems. The findings of this qualitative investigation indicated that the teachers’ beliefs and self-reported practices included both teacher- and child-related factors. However, the observed practices included several teacher-initiated strategies; child-initiated strategies appeared, albeit less than teacher-initiated ones. This affirms the teachers’ stress on children’s role in managing peer problems. Although some of the teacher practices and beliefs were consistent, certain inconsistencies may indicate the presence of external constraints. Practice or Policy: The results of this study highlight the importance of teacher education programs providing preservice and in-service teachers with the knowledge and skills required to understand children’s peer relationships, guide their interactions, and apply appropriate intervention strategies for various peer relationship problems.
Hoarding Content in an M-Learning System

The advances in mobile technologies predispose the support of learners' and teachers' activities on the move. As the nowadays' infrastructure still does not provide cheap 'always on' connection at every place and every moment, a mobile learning system should provide also support for offline usage of learning material. The process of automatically selecting and pre-fetching content to be used during the disconnected periods is called hoarding. Considering a situation where the mobile device's limited memory can not hold all available materials, the aim of the hoarding is to predict what the user will really need and use. A high accuracy of the set selection is necessary, as in the learning scenario every miss can lead to student's misunderstanding of the studied subject. Hoarding is still not well explored. We are currently developing a mobile version of an existing language learning online platform. Our Mobile ELDIT system strongly needs a hoarding subsystem. We have developed a basic hoarding algorithm, whose preliminary outcomes we report and we want to study the parameters that could help a hoarding algorithm improvement in order to cover the peculiarity in m-learning scenario. Our goal is to provide an efficient strategy, taking into account additional parameters automatically extracted by the system.
Inverter for microturbines based on multiobjective genetic algorithm

In this paper, authors present a new design method for pulse width modulation inverters in microturbines by using a multiobjective genetic algorithm. The design problem is converted to an equivalent optimization problem, and then a multiobjective genetic algorithm is adopted to find a solution. The genetic algorithm is proposed to design a fuzzy controller. In this GA approach, an individual is constructed to represent the fuzzy controller. Multiobjective genetic algorithm confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This optimal fuzzy controller is suitable for the specific harmonic elimination PWM technology, as demonstrated by the examples given in this paper
Identification of network traffic based on support vector machine

Along with the emergence and development of new applications of network which is represented by P2P network, existing network traffic identification methods do not respond to the environment of network traffic which cannot be identified efficiently and accurately. In order to meet the needs of network traffic, the method of network traffic identification based on support vector machine (SVM) is proposed. Over the use of the public data set and the real-time traffic for a combination of supervised learning, this method constructs a reasonable training set and testing set to experiment. And it fully proves that the method for identification of network traffic has high accuracy, low complexity and high recognition efficiency, and the practical feasibility in real-time traffic identification.
Aging in glassy systems: new experiments, simple models, and open questions

We review the most striking experimental results on aging in a variety of disordered systems, which reveal similar features but also important differences. We argue that a generic model that reproduce many of these features is that of {\it pinned defects} in a disordered environment, hopping between different metastable states. The fact that energy barriers grow with the size of the reconforming regions immediately leads to a strong hierarchy of time scales. In particular, long wavelength glassy, aging modes and short wavelength equilibrated modes coexist in the system and offer a simple mechanism to explain the rejuvenation/memory effect, which does not rely on `chaos' with temperature. These properties can be discussed within simplified models (such as the Sinai model) where the dynamics of the whole pinned object is reduced to that of its center of mass in a disordered potential. While the experiments in random ferromagnet-like systems can be satisfactorily accounted for, the correct picture for the aging dynamics of spin-glasses is still missing. Following recent ideas of Houdayer and Martin, we propose the idea that the ordered phase a spin-glass contains a large number of pinned, zero tension walls. Finally, we briefly discuss the status of the mean-field theories of aging.
DTC: A Dynamic Transaction Chopping Technique for Geo-replicated Storage Systems

Large Web applications usually require replicating data across geo-distributed datacenters to achieve high locality, durability and availability. However, maintaining strong consistency in geo-replicated systems usually suffers from long latency due to costly coordination across datacenters. Among others, transaction chopping is an effective and efficient approach to cope with such a challenge. In this paper, we propose DTC (Dynamic Transaction Chopping), a novel technique that chops transactions and checks their conflicts in a dynamic and automatic way, during application execution. DTC mainly consists of two parts: a dynamic chopper that chops transaction dynamically according to data partition scheme, and a conflict detection algorithm for determining the safety of the dynamic chopping. Compared with existing transaction chopping technique for geo-replicated systems, DTC has several advantages, including transparency to programmers, flexibility in conflict analysis, high degree of piecewise execution, and adaptability to dynamic partition schemes. We implement our DTC technique and conduct experiments to examine the correctness of DTC and evaluate its performance. The experiment results show that our DTC technique can achieve much more piecewise execution than the existing chopping approach does, and reduce execution time obviously.
A feasability study of drowsiness detection using driving behaviour parameters

One of the main causes of car accidents is drowsiness. There have been many studies regarding driving monitoring systems in the past few years, although most of them are focused in simulator environments. This paper presents a system to detect drowsiness patterns in real driving environments, where many external conditions need to be taken into account. Initial tests were done in simulator, followed by tests in real vehicles. Although two different approaches have been developed, this paper is focused in the inadequate driving identification based on the steering movements. Its sub-modules are also presented, with a special focus on the active driving detector.
Stabilization of Growing Retinal Axons by the Combined Signaling of Nitric Oxide and Brain-Derived Neurotrophic Factor

The pattern of axonal projections early in the development of the nervous system lacks the precision present in the adult. During a developmental process of refinement, mistargeted projections are eliminated while correct projections are retained. Previous studies suggest that during development nitric oxide (NO) is involved in the elimination of mistargeted retinal axons, whereas brain-derived neurotrophic factor (BDNF) may stabilize retinal axon arbors. It is unclear whether these neuromodulators interact. This study showed that NO induced growth cone collapse and retraction of developing retinal axons. This effect was not attributable to NO-induced neurotoxicity. BDNF protected growth cones and axons from the effects of NO. This effect was specific to BDNF, because neither nerve growth factor (NGF) nor neurotrophin-3 (NT-3) prevented NO-induced growth cone collapse and axon retraction. Exposure to both BDNF and NO, but not either factor alone, stabilized growth cones and axons. Stabilized axons exhibited minimal retraction or extension. This response appears to be a new axon “state” and not simply a partial amelioration of the effect of NO, because lower doses of BDNF or NO allowed axon extension. Furthermore, BDNF/NO-induced growth cone stabilization correlated with the appearance of a cytochalasin D-resistant population of actin filaments. BDNF protection from NO likely was mediated locally at the level of the growth cone, because growth cones or individual filopodia in contact with BDNF-coated beads were protected from NO-induced collapse. These findings suggest a cellular mechanism by which some axonal connections are stabilized and some are eliminated during development.
G-Storm: GPU-enabled high-throughput online data processing in Storm

The Single Instruction Multiple Data (SIMD) architecture of Graphic Processing Units (GPUs) makes them perfect for parallel processing of big data. In this paper, we present the design, implementation and evaluation of G-Storm, a GPU-enabled parallel system based on Storm, which harnesses the massively parallel computing power of GPUs for high-throughput online stream data processing. G-Storm has the following desirable features: 1) G-Storm is designed to be a general data processing platform as Storm, which can handle various applications and data types. 2) G-Storm exposes GPUs to Storm applications while preserving its easy-to-use programming model. 3) G-Storm achieves high-throughput and low-overhead data processing with GPUs. We implemented G-Storm based on Storm 0.9. 2 and tested it using two different applications: continuous query and matrix multiplication. Extensive experimental results show that compared to Storm, G-Storm achieves over 7x improvement on throughput for continuous query, while maintaining reasonable average tuple processing time. It also leads to 2.3x throughput improvement for the matrix multiplication application.
USER SPECIFIC TEMPLATE MATCHING FOR EVENT DETECTION USING SINGLE CHANNEL EEG

A major problem with Brain Computer Interface (BCI) technology is the variability of performance when applied to multiple users. We introduce a user-specific training scheme that builds a signal template specific to each user based on EEG recordings. As a feature we use the slow cortical change associated with a movement known as the Bereitschaftspotential (BP) measured using one bi-polar channel placed between C3 and A1 in the 10-20 standard electrode scheme. Signal processing techniques such as embedding, median and exponential weighting filters as well as normalisation of data and simulated annealing are implemented to make the template as robust as possible. Results still indicate a large variability in performance with accuracies from 59% up to 86%. It is expected that the results of this classification will be most useful in a multi-feature classification scheme. The simplicity of the training protocol and the number of electrodes used for detection are useful when considering clinical application of BCIs.
A nine-level inverter for open-end induction motor

A novel nine-level inverter topology is presented for an open-end winding induction motor drive. The presented topology has the highest number of levels for a given number of dc sources and power electronics switches. The conventional configurations for multi-level inverter are neutral point clamp (NPC), flying capacitor (FC) and series/cascade H-bridge. The proposed topology does not require any clamping diodes as they are needed in NPC. Apart from this there is no requirement of capacitor bank as compare to flying capacitor schemes where for nine-level inverter, twenty eight capacitor banks per phase are required. The proposed inverter scheme requires only four DC sources whereas series/cascade H-bridge nine-level inverter requires at least six DC sources. Extensive simulation study of the presented topology is done for the complete modulation range.
MPEG-7 meta-data enhanced encoder system for embedded systems

We describe a MPEG-7 Meta-Data enhanced Audio-Visual Encoder system that targets DVD recorders. We extract features in the compressed domain with both video and audio, which allows us to add the meta-data extraction without altering the hardware architecture of the encoder core. Our feature extraction algorithms are simple, and thus implementable through a simple combination of software and hardware on the integrated DVD chip. The primary application of the meta-data is video summarization, which enables rapid browsing of stored video by the end user. The simplicity of our summarization and feature extraction algorithms enables incorporation of the powerful functionality of smart content navigation through content summarization, into the DVD recorder at a low cost.
Regional multi-focus image fusion using clarity enhanced image segmentation and sparse representation

To obtain the underlying information from the original multi-focus images and make the fused image clearer, a novel approach based on segmentation of a mix of some clarity enhanced images and the classical sparse representation is proposed. We first use the sparse representation to calculate the relative clarity degree and add it to the original image to construct the clarity enhanced image. Meanwhile, we use the technique of normalized cuts (Ncut) to segment the mix of clarity enhanced images and use the region based method instead of the pixel based method to construct the fused image. The sparse coefficients matrix of fused image is constructed by using the mean-max rule based on the partition results. Finally, the fused image is obtained after inverse transformation. The experimental results demonstrate that the proposed method is a good candidate for multifocus image fusion problems.
CUSTOMER SATISFACTION DEGREE EVALUATION MODEL IN LOGISTICS USING SVM

Abstract Customer satisfaction is important for the development of advanced logistical distribution. The paper presents a novel evaluation model of customer satisfaction degree (CSD) based on support vector machine (SVM). The relations of the suppliers and customers are analyzed, then the evaluation index system and fuzzy quantitative methods are provided firstly. The CSD evaluation system including nine indices and three ranks based on one-against-one mode of SVM is built. Last the simulation experiment is given to support the theoretical result.
GSK3β Regulates Differentiation and Growth Arrest in Glioblastoma

Cancers are driven by a population of cells with the stem cell properties of self-renewal and unlimited growth. As a subpopulation within the tumor mass, these cells are believed to constitute a tumor cell reservoir. Pathways controlling the renewal of normal stem cells are deregulated in cancer. The polycomb group gene Bmi1, which is required for neural stem cell self-renewal and also controls anti-oxidant defense in neurons, is upregulated in several cancers, including medulloblastoma. We have found that Bmi1 is consistently and highly expressed in GBM. Downregulation of Bmi1 by shRNAs induced a differentiation phenotype and reduced expression of the stem cell markers Sox2 and Nestin. Interestingly, expression of glycogen synthase kinase 3 beta (GSK3β), which was found to be consistently expressed in primary GBM, also declined. This suggests a functional link between Bmi1 and GSK3β. Interference with GSK3β activity by siRNA, the specific inhibitor SB216763, or lithium chloride (LiCl) induced tumor cell differentiation. In addition, tumor cell apoptosis was enhanced, the formation of neurospheres was impaired, and clonogenicity reduced in a dose-dependent manner. GBM cell lines consist mainly of CD133-negative (CD133-) cells. Interestingly, ex vivo cells from primary tumor biopsies allowed the identification of a CD133- subpopulation of cells that express stem cell markers and are depleted by inactivation of GSK3β. Drugs that inhibit GSK3, including the psychiatric drug LiCl, may deplete the GBM stem cell reservoir independently of CD133 status.
A completely distributed algorithm for user association in HetSNets

In this paper, the user association problem under quality of service (QoS) requirements in a heterogeneous and small cells network (HetSNet) is considered. We have shown in a previous work that this problem is NP-hard and thus cannot be solved optimally in polynomial time unless P = NP. Therefore, new suboptimal algorithms are needed in order to solve it efficiently. Even though, it is very hard to implement the suboptimal algorithm in a centralized fashion because it needs a high amount of information exchange between the base stations and the users and it suffers from a huge computational complexity. Thus, in this paper, we model the problem of user association in HetSNets as a non-cooperative game and we propose a completely distributed algorithm inspired by the theory of learning to solve it. Specifically, we propose a modified win-stay-lose-shift learning model in order to converge to a near optimal user association. We evaluate by simulations the performance of the proposed algorithm and and we show that it is close to the performance of the computationally complex optimal centralized algorithm which assumes complete channel information knowledge.
Tumor-reactive immune cells protect against metastatic tumor and induce immunoediting of indolent but not quiescent tumor cells

Two major barriers to cancer immunotherapy include tumor‐induced immune suppression mediated by myeloid‐derived suppressor cells and poor immunogenicity of the tumor‐expressing self‐antigens. To overcome these barriers, we reprogrammed tumor‐immune cell cross‐talk by combined use of decitabine and adoptive immunotherapy, containing tumor‐sensitized T cells and CD25+ NKT cells. Decitabine functioned to induce the expression of highly immunogenic cancer testis antigens in the tumor, while also reducing the frequency of myeloid‐derived suppressor cells and the presence of CD25+ NKT cells rendered T cells, resistant to remaining myeloid‐derived suppressor cells. This combinatorial therapy significantly prolonged survival of animals bearing metastatic tumor cells. Adoptive immunotherapy also induced tumor immunoediting, resulting in tumor escape and associated disease‐related mortality. To identify a tumor target that is incapable of escape from the immune response, we used dormant tumor cells. We used Adriamycin chemotherapy or radiation therapy, which simultaneously induce tumor cell death and tumor dormancy. Resultant dormant cells became refractory to additional doses of Adriamycin or radiation therapy, but they remained sensitive to tumor‐reactive immune cells. Importantly, we discovered that dormant tumor cells contained indolent cells that expressed low levels of Ki67 and quiescent cells that were Ki67 negative. Whereas the former were prone to tumor immunoediting and escape, the latter did not demonstrate immunoediting. Our results suggest that immunotherapy could be highly effective against quiescent dormant tumor cells. The challenge is to develop combinatorial therapies that could establish a quiescent type of tumor dormancy, which would be the best target for immunotherapy.