Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods to Characterize the Real-World Use of Rollators Using Inertial Sensors–A Feasibility Study

Rollators are widely used by people with mobility problems, but previous studies have been limited to self-report approaches when evaluating their real-world effectiveness. To support studies based on more robust datasets, a method to estimate mobility parameters, such as gait speed and distance traveled, in the real world is needed. Body-worn sensors offer one approach to the problem, but rollator-mounted sensors have some practical advantages providing direct insight into patterns of walking device used, an under-researched area. We present a novel method to estimate speed and distance traveled from a single rollator-mounted IMU. The method was developed using data collected from ten rollator users performing a series of walking tasks including obstacle negotiation. The IMU data is first pre-processed to account for noise, orientation offset, and rotation-induced accelerations. The method then uses a two-stage approach. First, activity classification is used to separate the rollator data into one of three classes (movement, turning, or other). Subsequently, the speed of movement and distance traveled is estimated, using a separate estimation model for each of the three classes. The results showed high classification accuracy (precision, recall, and F1 statistics all >0.9). Speed estimation showed mean absolute errors below 0.2 m/s. Estimates for distance traveled showed errors which ranged from 5% (straight line walking) to over 70%. The results showed some promise but further work with a larger data set is needed to confirm the performance of our approach.
Attention-based traffic sign recognition with an array of weak classifiers

Currently available traffic sign recognition systems typically focus on a single class of traffic sign and therefore, the algorithms are optimized to find only this specific class. To this end, a number of approaches for real time capable classification of mostly circular signs exist. Nevertheless, to simultaneously recognize a number of classes a different way has to be taken. This paper presents a real-time capable approach, which uses a two-tiered process independent of the diameter of the sign to cope with all distances. The first stage is our attention system, parameterized to find a number of different types of sign classes. The output of our attention system is a region of interest with a potential traffic sign candidate. The second stage is an array of weak classifiers similar to the idea of Viola and Jones [1], computing a probability value for each of the sign classes. As application area we focus on inner city and therefore, evaluate on the most important traffic sign classes of Stop and Give Way. Nevertheless, the approach can also detect Warning signs and is easily extensible to additional sign classes. The evaluation results show the reliability and mark it as first step towards an overall traffic sign recognition.
The Creation of Output and Quality in Services: A Framework to Analyze Information Technology‐Worker Systems

Information technology has profoundly impacted the operations of firms in the service industry and service environments within manufacturing. Two models are introduced that establish a conceptual framework linking firm profit to attributes of the IT‐worker system. The framework considers the impact of IT capabilities (such as functionality and ease‐of‐use) and worker skill as drivers of output volume and quality. The framework contrasts attributes of the IT‐worker systems when services are mass‐produced (flow shop) versus customized (job shop). Mathematical models are introduced to formalize the conceptual framework. Numerical examples are presented that illustrate the types of insights that can be obtained from the models.
SaaS-Based Services Information Technology Acceptance Model

To know about the factors which affect the SMEs' cognition and acceptance of SaaS-based services information technology, the paper study the SMEs' website technology of enterprises acceptance SaaS-based model, combining with technology acceptance and expectation disconfirmation theory. From the angle of network information service, it analyses external factors which affect the the SMEs' website technology of enterprises acceptance SaaS-based model. It puts forward the SaaS-based services information technology acceptance model through classic technology acceptance model and use expectation-disconfirmation theory to supplement the classic technology acceptance model. At last the paper give some suggestion to the SaaS-based SMEs' website construction service according to the situation of domestic SMEs' website construction.
Embedded Palmprint Recognition System Using OMAP 3530

We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the ccentral pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance.
Melodic Segmentation Using the Jensen-Shannon Divergence

This paper introduces an unsupervised model for melodic segmentation that extends a method initially proposed in computational biology. In the model segments are identified as sections of maximal contrast within a musical piece, using for this the Jensen-Shannon divergence. The model is extended upon its original formulation, and experiments to test its performance are carried out for a small set of selected folk song melodies. Generalization of the model is tested on 100 folk songs. Our results show a significant improvement upon the model's original formulation. In addition, we situate our model in the context of a cognition-based ensemble learning framework and justify its use within it. The need for such a cognition-based ensemble approach is also discussed.
A Tale of Two Parsers: Investigating and Combining Graph-based and Transition-based Dependency Parsing

Graph-based and transition-based approaches to dependency parsing adopt very different views of the problem, each view having its own strengths and limitations. We study both approaches under the framework of beam-search. By developing a graph-based and a transition-based dependency parser, we show that a beam-search decoder is a competitive choice for both methods. More importantly, we propose a beam-search-based parser that combines both graph-based and transition-based parsing into a single system for training and decoding, showing that it outperforms both the pure graph-based and the pure transition-based parsers. Testing on the English and Chinese Penn Treebank data, the combined system gave state-of-the-art accuracies of 92.1% and 86.2%, respectively.
ACN: An Associative Classifier with Negative Rules

Classification using association rules has added a new dimension to the ongoing research for accurate classifiers. Over the years, a number of associative classifiers based on positive rules have been proposed in literature. The target of this paper is to improve classification accuracy by using both negative and positive class association rules without sacrificing performance. The generation of negative associations from datasets has been attacked from different perspectives by various authors and this has proved to be a very computationally expensive task. This paper approaches the problem of generating negative rules from a classification perspective, how to generate a sufficient number of high quality negative rules efficiently so that classification accuracy is enhanced. We adopt a simple variant of Apriori algorithm for this and show that our proposed classifier "associative classifier with negative rules"(ACN) is not only time-efficient but also achieves significantly better accuracy than four other state-of-the-art classification methods by experimenting on benchmark UCI datasets.
A Survey of Energy Management in Interconnected Multi-Microgrids

An interconnected multi-microgrids (IMMGs) system takes advantage of various complementary power sources and effectively coordinates the energy sharing/trading among the MGs and the main grid to improve the stability, reliability, and energy efficiency of the system. The core of this structure is to achieve the optimal distribution of energy sharing through proper strategies. However, the volatility and intermittent characteristics of renewable resources, time-varying loads in the MGs, their correlated power generations, and the coupled energy among the MGs during energy trading, all bring about new challenges to achieving a stable operation and optimal scheduling in the power system. Many solutions have been proposed to solve these problems. In this paper, we provide an overview of the current energy management systems (EMS) in IMMGs, focusing on the IMMG structure, EMS objectives, timescales, and scheduling optimization structure. We then provide a review of the distributed optimization algorithms in IMMGs. We conclude this survey with a discussion of future directions.
Examining the Mediating Role of the Critical Psychological States between Job Characteristics and Job Satisfaction among Employees in the General Media Sector in Syria

This study examines the Relationship between Job characteristics and job satisfaction among employee in the general media sector. Job characteristics (i.e., Task variety, Task identity, Task significance, autonomy, and feedback) were tested as predictors of job satisfaction by using three critical psychological states as mediator variables. Data were collected from 103 employees working in many different sections by using JDS survey. Consistent with studies conducted in this domain SPSS was the basic tool especially hierarchical regression analyses. The study revealed that all job characteristics dimension was positively related to job satisfaction, and three CPS. And for mediation role we found all three CPS variable play this role but with different rates.
A Model-Driven Design Framework for Massively Parallel Embedded Systems

Modern embedded systems integrate more and more complex functionalities. At the same time, the semiconductor technology advances enable to increase the amount of hardware resources on a chip for the execution. Massively parallel embedded systems specifically deal with the optimized usage of such hardware resources to efficiently execute their functionalities. The design of these systems mainly relies on the following challenging issues: first, how to deal with the parallelism in order to increase the performance; second, how to abstract their implementation details in order to manage their complexity; third, how to refine these abstract representations in order to produce efficient implementations. This article presents the Gaspard design framework for massively parallel embedded systems as a solution to the preceding issues. Gaspard uses the repetitive Model of Computation (MoC), which offers a powerful expression of the regular parallelism available in both system functionality and architecture. Embedded systems are designed at a high abstraction level with the MARTE (Modeling and Analysis of Real-time and Embedded systems) standard profile, in which our repetitive MoC is described by the so-called Repetitive Structure Modeling (RSM) package. Based on the Model-Driven Engineering (MDE) paradigm, MARTE models are refined towards lower abstraction levels, which make possible the design space exploration. By combining all these capabilities, Gaspard allows the designers to automatically generate code for formal verification, simulation and hardware synthesis from high-level specifications of high-performance embedded systems. Its effectiveness is demonstrated with the design of an embedded system for a multimedia application.
Characterizing the Late Pleistocene MSA Lithic Technology of Sibudu, KwaZulu-Natal, South Africa

Studies of the African Middle Stone Age (MSA) have become central for defining the cultural adaptations that accompanied the evolution of modern humans. While much of recent research in South Africa has focused on the Still Bay and Howiesons Poort (HP), periods following these technocomplexes were often neglected. Here we examine lithic assemblages from Sibudu that post-date the HP to further the understanding of MSA cultural variability during the Late Pleistocene. Sibudu preserves an exceptionally thick, rich, and high-resolution archaeological sequence that dates to ∼58 ka, which has recently been proposed as type assemblage for the “Sibudan”. This study presents a detailed analysis of the six uppermost lithic assemblages from these deposits (BM-BSP) that we excavated from 2011–2013. We define the key elements of the lithic technology and compare our findings to other assemblages post-dating the HP. The six lithic assemblages provide a distinct and robust cultural signal, closely resembling each other in various technological, techno-functional, techno-economic, and typological characteristics. These results refute assertions that modern humans living after the HP possessed an unstructured and unsophisticated MSA lithic technology. While we observed several parallels with other contemporaneous MSA sites, particularly in the eastern part of southern Africa, the lithic assemblages at Sibudu demonstrate a distinct and so far unique combination of techno-typological traits. Our findings support the use of the Sibudan to help structuring this part of the southern African MSA and emphasize the need for further research to identify the spatial and temporal extent of this proposed cultural unit.
Mean Square Error Estimation in Thresholding

We present a novel approach to estimating the mean square error (MSE) associated with any given threshold level in both hard and soft thresholding. The estimate is provided by using only the data that is being thresholded. This adaptive approach provides probabilistic confidence bounds on the MSE. The MSE bounds can be used to evaluate the denoising method. Our simulation results confirm that not only does the method provide an accurate estimate of the MSE for any given thresholding method, but the proposed method can also search and find an optimum threshold for any noisy data with regard to MSE.
Predictive preemptive certificate transfer in Cluster-Based Certificate Chain

Mobile ad hoc networks are a set of nodes that cooperate and communicate wirelessly. This kind of networks in easy to deploy because there is no need of any pre-existing infrastructure. Security in Manets is a very important issue and it is hard to use conventional security techniques. Many approaches have been proposed to secure communication in Manets; most of them are based of public-key certifications which create a multitude of trust communication model.In this paper, we propose an amelioration of a distributed certificate chain that relies on the cluster based routing protocol. In our scheme, after forming clusters, the cluster-head node issue certificates for other nodes within its cluster. When a member node want migrates to an adjacent cluster, the cluster-head sends the node’s certificate to surrounding cluster-heads via gateway nodes. The protocol was doted by a preemptive predictive module to predict migration intention of member nodes. This approach has been evaluated by detailed simulation study. Simulation results show that this approach is scalable and generate lower certification overhead.
Using symbolic objects to cluster web documents

Web Clustering is useful for several activities in the WWW, from automatically building web directories to improve retrieval performance. Nevertheless, due to the huge size of the web, a linear mechanism must be employed to cluster web documents. The k-means is one classic algorithm used in this problem. We present a variant of the vector model to be used with the k-means algorithm. Our representation uses symbolic objects for clustering web documents. Some experiments were done with positive results and future work is optimistic.
Tracking data structures coherency in animated ray tracing for real-time 3D-rendering

Ray tracing is a well known method for photorealistic image synthesis, volume visualization and rendering. Over the last decade the method is being adopted throughout the research community around the world. With the advent of the high speed processing units, the method has been emerging from offline rendering towards real time rendering. The success behind ray tracing algorithms lies in the use of acceleration data structures and modern processing power of CPUs and GPUs. kd-tree is one of the most widely used data structures based on surface area heuristics (SAH). The major bottleneck in kd-tree construction is the time consumed to find optimum split locations. In this paper, we propose a prediction algorithm for animated ray tracing based on Kalman filtering. The algorithm successfully predicts the split locations for the next consecutive frame in the animation sequence. Thus, giving good initial starting points for one dimensional search algorithms to find optimum split locations - in our case parabolic interpolation combined with golden section search. With our technique implemented, we have reduced the ldquorunning kd-tree constructionrdquo time by between 78% and 87% for dynamic scenes with 16.8 K and 252 K polygons respectively.
Serum fluctuations of total and free tryptophan levels during the menstrual cycle are related to gonadotrophins and reflect brain serotonin utilization.

BACKGROUND Serotoninergic (5-HT) neurons are suggested to regulate estrous cycle in animal models. In the present study we evaluated whether a relationship exists between the serotoninergic precursors in the central nervous system and the gonadotrophin-ovarian cyclic function. METHODS We measured 5-HT precursors [free (FT) and total (TT) tryptophan] and LH, FSH and 17beta-estradiol (E2) levels in the serum of 15 fertile women with normal menstrual cycles during the follicular (cycle days 1-5, 7-11), mid-cycle (cycle days 14-16) and luteal (cycle days 17-19, 22-24) phases. RESULTS TT and FT were significantly increased in the 7-11 and 17-19 cycle days and were decreased at mid-cycle (P < 0.01), with a cyclic and opposite behaviour when compared to that of FSH and LH. Indeed, correlation analysis through the matrix of mean values showed that LH was negatively correlated to TT (r = -0.636) and FT (r = -0.574), as well as FSH (TT, r = -0.655; FT, r = -0.663), and that TT and FT were positively correlated to each other (r = 0.801; P < 0.001). Furthermore, whilst the two FT peaks reached approximately the same levels in the follicular and luteal phase, TT levels were approximately 30% higher in the luteal than in the follicular phase of the cycle: thus in the first (follicular) phase FT peak was relatively higher than that of TT, whereas the contrary occurred in the second (luteal) phase of the cycle. CONCLUSIONS Both TT and FT levels have cyclic variations throughout the menstrual cycle, being lowest at mid-cycle (14-16 cycle days), concomitant with the highest LH and FSH concentrations, and higher before and after mid-cycle phase, coinciding with the lowest circulating LH/FSH levels. Since TT and FT levels in the plasma have cyclic changes, our study: (i) suggests that a consumption of serum serotonin precursors takes place concomitant with gonadotrophin release during menstrual cycle; (ii) may represent an in vivo model to investigate this relationship in women in different physiopathological conditions.
Antioxidant Capacity of Melatonin on Preimplantation Development of Fresh and Vitrified Rabbit Embryos: Morphological and Molecular Aspects

Embryo cryopreservation remains an important technique to enhance the reconstitution and distribution of animal populations with high genetic merit. One of the major detrimental factors to this technique is the damage caused by oxidative stress. Melatonin is widely known as an antioxidant with multi-faceted ways to counteract the oxidative stress. In this paper, we investigated the role of melatonin in protecting rabbit embryos during preimplantation development from the potential harmful effects of oxidative stress induced by in vitro culture or vitrification. Rabbit embryos at morula stages were cultured for 2 hr with 0 or 10−3 M melatonin (C or M groups). Embryos of each group were either transferred to fresh culture media (CF and MF groups) or vitrified/devitrified (CV and MV groups), then cultured in vitro for 48 hr until the blastocyst stage. The culture media were used to measure the activity of antioxidant enzymes: glutathione-s-transferase (GST) and superoxide dismutase (SOD), as well as the levels of two oxidative substrates: lipid peroxidation (LPO) and nitric oxide (NO). The blastocysts from each group were used to measure the expression of developmental-related genes (GJA1, POU5F1 and Nanog) and oxidative-stress-response-related genes (NFE2L2, SOD1 and GPX1). The data showed that melatonin promoted significantly (P<0.05) the blastocyst rate by 17% and 12% in MF and MV groups compared to their controls (CF and CV groups). The GST and SOD activity significantly increased by the treatment of melatonin in fresh or vitrified embryos, while the levels of LPO and NO decreased (P<0.05). Additionally, melatonin considerably stimulated the relative expression of GJA1, NFE2L2 and SOD1 genes in MF and MV embryos compared to CF group. Furthermore, melatonin significantly ameliorated the reduction of POU5F1 and GPX1 expression induced by vitrification. The results obtained from the current investigation provide new and clear molecular aspects regarding the mechanisms by which melatonin promotes development of both fresh and vitrified rabbit embryos.
The Person-Centered e-Learning Pattern Repository: Design for Reuse and Extensibility

Patterns are currently a hot topic in educational technology research and practice. Aiming to further reuse of proven solutions to common problems, they seem particularly worthwhile for guiding e-learning practitioners in their efforts to enhance teaching and learning processes. Since the e-learning pattern community is just beginning to form, there is still much to be done to reach a sound conceptual foundation for pattern writers. This paper presents the initial release version of the pattern repository for Person-Centered e-Learning, which is basically a pedagogically enriched approach to blended learning. The patterns rely mostly on successful experiences in the context of computer science courses at the University of Vienna during the last three years. Consequently, the e-learning community is invited to reuse the patterns as well as to propose valuable improvements and extensions based on their own experiences.
Functional MR Imaging of Psychogenic Amnesia: A Case Report

We present here a case in which functional MR imaging (fMRI) was done for a patient who developed retrograde psychogenic amnesia for a four year period of her life history after a severe stressful event. We performed the fMRI study for a face recognition task using stimulation with three kinds of face photographs: recognizable familiar faces, unrecognizable friends' faces due to the psychogenic amnesia, and unfamiliar control faces. Different activation patterns between the recognizable faces and unrecognizable faces were found in the limbic area, and especially in the amygdala and hippocampus.