Miracle-dz/UniFAR-FTUs huggingface dataset:
How fashionable is each street?: Quantifying road characteristics using social media

Determining routes that provide opportunities to satisfy the various demands of users is still an open problem. This is because it is virtually impossible to manually quantify the characteristics of each road and there are few resources describing roads directly such that we meet any demand that may arise. The goal of this study is to automatically quantify the characteristics of roads for demands that can be described using keywords such as “fashionable”. To achieve this goal, we propose a two-stage method that analyzes social media and road networks. First, our method estimates the topic distribution (i.e., the characteristics) of each point-of-interest (POI) by analyzing geotagged texts with the Latent Dirichlet Allocation model. Next, it uses a Markov random field model to estimate the characteristics of each road on the basis of those of POIs and the road networks associated with the POIs. Experiments on real datasets demonstrate that our method achieves statistically significant improvements over baseline methods in terms of ranking quality in the information retrieval for roads in three areas given 25 keywords.
Plakophilin3 Loss Leads to an Increase in PRL3 Levels Promoting K8 Dephosphorylation, Which Is Required for Transformation and Metastasis

The desmosome anchors keratin filaments in epithelial cells leading to the formation of a tissue wide IF network. Loss of the desmosomal plaque protein plakophilin3 (PKP3) in HCT116 cells, leads to an increase in neoplastic progression and metastasis, which was accompanied by an increase in K8 levels. The increase in levels was due to an increase in the protein levels of the Phosphatase of Regenerating Liver 3 (PRL3), which results in a decrease in phosphorylation on K8. The increase in PRL3 and K8 protein levels could be reversed by introduction of an shRNA resistant PKP3 cDNA. Inhibition of K8 expression in the PKP3 knockdown clone S10, led to a decrease in cell migration and lamellipodia formation. Further, the K8 PKP3 double knockdown clones showed a decrease in colony formation in soft agar and decreased tumorigenesis and metastasis in nude mice. These results suggest that a stabilisation of K8 filaments leading to an increase in migration and transformation may be one mechanism by which PKP3 loss leads to tumor progression and metastasis.
Radio Communication Systems simulation: from the pioneers to the present

Simulation plays an important role in computer-aided analysis and design of communication systems. This paper is related with an historical perspective of the Radio Communication Systems (RCS) simulation from the early history in the 50's of 20th century to the present. In describing the history of simulation five distinct periods may be identified: early history, pioneering, innovation, revolution and evolution. Digital signal processing and programming languages were decisive in the simulation technical advance in each period. Time intervals roughly equate to a single decade, starting from the late 1950s to 2015.
Access Mechanism for Multihop Cellular Networks

We propose a CDMA-OFDM access mechanism for Multihop Cellular Networks (MCN). We construct groups within the MCN, where each group comprises of a source node, a destination node, their intermediate relay nodes and assign a correlated PN sequence to each such group. Within a particular group, a single carrier is assigned to each intermediate hop. The sub carriers assigned to the intermediate hops in a given group are mutually orthogonal. Hence the proposed OFDM is FDMA in nature. The sub carriers and transmit power levels to the relay nodes are assigned in such a way as to maximize the end-to-end throughput. The end-to-end throughput is formulated by assuming a Rayleigh flat fading channel between nodes. We present a method to reuse the PN codes assigned to the groups in a particular cell. We also introduce a novel architecture for MCN. Simulation results show that the proposed access mechanism achieves better end-to-end throughput and bit error rate (BER) performance as compared to standard access mechanisms like CDMA and OFDM-FDMA. Furthermore the proposed access mechanism has considerably higher BER performance in the presence of multiple transmit sources.
Reputation Based Trust Management forWireless Sensor Networks and Its Applicationto Secure Routing

Using delay tolerant networks it can be characterized by high-end-to-end latency to secure routing . It consist of frequent disconnection and communication over a unreliable wireless links . The methods used for the protocol to validate extensive simulation, and also the design and validation of dynamic trust management protocols for mobile networks . The application used to optimize and gain the quality of service. It is based on trust-based (Bayesian) and non-trust based (ProPHET and Epidemic). When we compare the both protocols the nontrust based provides high incurring message of trusted nodes with gives message delivery ratio. The parameter occurred as healthiness, unslefiness, connectivity and energy. The outperforms deals with the selfish behavior and is resilient against trust related attacks.
Observational Study on the Occurrence of Muscle Spindles in Human Digastric and Mylohyoideus Muscles

Although the occurrence of muscle spindles (MS) is quite high in most skeletal muscles of humans, few MS, or even absence, have been reported in digastric and mylohyoideus muscles. Even if this condition is generally accepted and quoted in many papers and books, observational studies are scarce and based on histological sections of a low number of specimens. The aim of the present study is to confirm previous data, assessing MS number in a sample of digastric and mylohyoideus muscles. We investigated 11 digastric and 6 mylohyoideus muscles from 13 donors. Muscle samples were embedded in paraffin wax, cross-sectioned in a rostrocaudal direction, and stained using haematoxylin-eosin. A mean of 5.1 ± 1.1 (range 3–7) MS was found in digastric muscles and mean of 0.5 ± 0.8 (range 0–2) in mylohyoideus muscles. A significant difference (P < 0.001) was found with the control sample, confirming the correctness of the histological procedure. Our results support general belief that the absolute number of spindles is sparse in digastric and mylohyoideus muscles. External forces, such as food resistance during chewing or gravity, do not counteract jaw-opening muscles. It is conceivable that this condition gives them a limited proprioceptive importance and a reduced need for having specific receptors as MS.
Utilizing Cytokines to Function-Enable Human NK Cells for the Immunotherapy of Cancer

Natural killer (NK) cells are innate lymphoid cells important for host defense against pathogens and mediate antitumor immunity. Cytokine receptors transduce important signals that regulate proliferation, survival, activation status, and trigger effector functions. Here, we review the roles of major cytokines that regulate human NK cell development, survival, and function, including IL-2, IL-12, IL-15, IL-18, and IL-21, and their translation to the clinic as immunotherapy agents. We highlight a recent development in NK cell biology, the identification of innate NK cell memory, and focus on cytokine-induced memory-like (CIML) NK cells that result from a brief, combined activation with IL-12, IL-15, and IL-18. This activation results in long lived NK cells that exhibit enhanced functionality when they encounter a secondary stimulation and provides a new approach to enable NK cells for enhanced responsiveness to infection and cancer. An improved understanding of the cellular and molecular aspects of cytokine-cytokine receptor signals has led to a resurgence of interest in the clinical use of cytokines that sustain and/or activate NK cell antitumor potential. In the future, such strategies will be combined with negative regulatory signal blockade and enhanced recognition to comprehensively enhance NK cells for immunotherapy.
Spatial uplink mobile data offloading leveraging store-carry-forward paradigm

The growing popularity of mobile data communication has been leading a growing lack of radio resource on a cellular network. Therefore, to avoid the data traffic from exceeding the capacity limit of a cellular base station (BS), cellular operators have been trying to install cellular femto cells (local BSs) or wireless LAN access points (APs) in restaurants, cafes, user's houses, etc., to divert data traffic from the cellular radio access link to the fixed-line such as x-DSL and FTTH. However, the traffic levels extremely change in a day, even locally, due to the mobility of users. While peak data traffic at certain times of a day appears locally, cellular BSs as well as the femto cells and the APs would sit underutilized for the rest of the day. Thus, we have started designing a robust cellular network (RoCNet) that combines infrastructure-based network (e.g., cellular network) and opportunistic networking for spatial mobile data offloading, which focuses on the difference of the data traffic level among areas (e.g., business district and residential area). The RoCNet facilitates the data traffic offloading leveraging the store-carry-forward feature considering a degree of traffic congestion. In this paper, we give an overview of the RoCNet and show a simulation result. The result shows RoCNet can spatially offload uplink traffic in a traffic concentration area to non-congested areas. In other words, RoCNet can suppress peak traffic in a traffic-congested base station by distributing traffic to vicinity base stations.
Effects of verum acupuncture compared to placebo acupuncture on quantitative EEG and heart rate variability in healthy volunteers.

OBJECTIVES The aim of this single-blind randomized crossover study was to evaluate specific effects of manual acupuncture on central and vegetative nervous system activity measured by quantitative electroencephalography (qEEG) and heart rate variability (HRV). DESIGN Twenty (20) healthy volunteers (mean: 25.2 +/- 3.6 years) were monitored simultaneously using a qEEG system and a 12-channel electrocardiogram recorder during verum acupuncture (VA) at acupuncture point Large Intestine 4 (Hegu) (LI4) or placebo acupuncture (PA) at a sham point. RESULTS In the EEG conduction of the occipital area, needle stimulation in VA increased alpha1-frequency significantly, and the ratio alpha1/theta was shifted to the benefit of alpha1 over all electrodes. The HRV parameters showed a significant increase of the low frequency/high frequency (HF) ratio during the first minute of stimulation in VA, indicating an initial increase of sympathetic activation. However, an increase of HF power in the minute after stimulation followed by a decrease in heart rate suggests delayed vagal activation. De qi (a sensation that is typical of acupuncture needling) occurred in 16 subjects during VA and in 9 volunteers during PA (80% versus 45%). CONCLUSIONS Manual stimulation on LI4 seems to lead to specific changes in alpha EEG-frequency and in HRV parameters. A linear relationship between the HRV parameters and the alpha EEG band might point to a specific modulation of cerebral function by vegetative effects during acupuncture.
Privacy Preserving CART Algorithm overVertically Partitioned Data

Data mining classification algorithms are centralized algorithm and works on centralized database. In this information age, organizations uses distributed database. Since data mining of private data is one of the keys to success for an organization, it is a challenging task to implement data mining in distributed database. Collaboration of different organization brings mutual benefits to the party involved. So different organizations wants to collaborate and execute efficient data mining algorithm. This arises privacy issues. Organizations are unable to collaborate because the privacy of private data is not fully preserved. In this paper CART algorithm is implemented over vertically partitioned data. For efficient privacy preservation of private data, privacy preserving protocols such as scalar product, x (ln x) protocols are used.
Predictive publish/subscribe matching

A new publish/subscribe capability is presented: the ability to predict the likelihood that a subscription will be matched at some point in the future. Knowing that some phenomenon of interest is about to take place, applications can take proactive steps to prevent the situation from occurring altogether, or speculatively begin reacting to the event even before it has transpired. A publish/subscribe matching algorithm is developed in which composite subscriptions consisting of temporal and logical operators are efficiently represented by a set of finite state machines and rules. The algorithm trains a Markov model to an application's event workload, and predicts the probability that a given subscription will match within a window in the future event stream. Evaluations demonstrate that the memory and processing costs of the algorithm scale well with the number of subscriptions, and the prediction precision is high, especially when the workload characteristics do not change rapidly. Furthermore, a comparison with a hand-crafted Markov model using real data traces shows that the algorithm consumes much less memory and processing power, yet still delivers prediction precision that approaches that of the hand-crafted model. This is especially impressive since the algorithms lack any of the domain expertise embedded in the hand-crafted model.
Supporting Self-Regulation by Personal Learning Environments

In this paper we attempt to address the issue of supporting self-regulation by Personal Learning Environments (PLE), which provide the learner with a freedom to design and compose his or her learning environment according to personal preferences and context demands. Psychology and neuroscience offer a lot of highly relevant results that should be taken into account. We report on our experiments mainly with PhD students, which represent a community of advanced learners. This needs to be considered when interpreting their feedback, as for most of the learners is self-regulation even more challenging. Even students at this high level appreciate pedagogical support in the learning process.
Robust Low-Rank LCMV Beamforming Algorithms Based on Joint Iterative Optimization Strategies

This chapter presents reduced-rank linearly constrained minimum variance (LCMV) algorithms based on the concept of joint iterative optimization of parameters. The proposed reduced-rank scheme is based on a constrained robust joint iterative optimization (RJIO) of parameters according to the minimum variance criterion. The robust optimization procedure adjusts the parameters of a rank-reduction matrix, a reduced-rank beamformer and the diagonal loading in an alternating manner. LCMV expressions are developed for the design of the rank-reduction matrix and the reduced-rank beamformer. Stochastic gradient and recursive least-squares adaptive algorithms are then devised for an efficient implementation of the RJIO robust beamforming technique. Simulations for a application in the presence of uncertainties show that the RJIO scheme and algorithms outperform in convergence and tracking performances existing algorithms while requiring a comparable complexity.
RELEVANCE OF INTERNATIONAL ACCOUNTING STANDARDS TO A DEVELOPING COUNTRY – FIJI: AN ARCHIVAL-EMPIRICAL INVESTIGATION

The quest for the international harmonization of accounting standards and practices are becoming increasingly important because of the globalization of the world economy. At the same time, investors have broadened their horizons beyond national boundaries. The leaders in the business community tend to advocate harmonization in order to facilitate world trade and economic growth. Even the developing nations are being enjoined to apply International Accounting Standards (IASs). The assertion being that by adopting IASs they can save themselves the time, effort and money required to formulate their own standards. However, a closer look at the development and application of IASs tends to raise doubts about the validity of such arguments. This paper analyses the relevance of a global system of financial reporting in a developing country – Fiji.
On the Reduction of Dublin Core Metadata Application Profiles to Description Logics and OWL

This paper gives an account of how traditional metadata application profiles are related to Web ontologies and Description Logics. It is shown that metadata profiles can be reduced to Description Logics; oddly enough though, OWL 2, with its current expressivity, is proven to be inadequate for this purpose. First, we give a brief overview of the recent proposal for representing metadata profiles using the notion of Description Set Profiles and XML Schema. We point out the necessity of an additional representation scheme, using also ontological languages. Following, we introduce the reduction of Description Set Profiles to Description Logics and identify the required expressivity characteristics that are essential for this reduction. Finally, we discuss what expense do these characteristics put on to the complexity of reasoning.
Learning to Classify Psychiatric Disorders based on fMR Images : Autism vs Healthy and ADHD vs Healthy

A clinical tool that can diagnose psychiatric illness using functional magnetic resonance brain images would greatly assist physicians. Here, we propose a learning algorithm that uses the histogram of oriented gradients (HOG) features of fMRI images as the input to support vector machines, then show that this system can produce such classifiers when run on two large public datasets: able to diagnose ADHD with hold-out accuracy of 0.626 (over baseline = 0.550) when trained on the ADHD-200 global competition dataset, and to diagnose autism with hold-out accuracy of 0.619 (over baseline = 0.516) when trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset. While these results are not yet to clinical relevance, they outperform all previously presented methods on both datasets. These results suggest that our learning approach may lead to diagnostic classifiers (from functional images) for yet other psychiatric disorders.
A model of theory-practice relations in mathematics teacher education

The paper presents and discusses an ATD based (Chevallard, 2012) model of theory-practice relations in mathematics teacher education. The notions of didactic transposition and praxeology are combined and concretized in order to form a comprehensive model for analysing the theory-practice problematique. It is illustrated how the model can be used both as a descriptive tool to analyse interactions between and interviews with student teachers and teachers and as a normative tool to design and redesign learning environments in teacher education, in this case a lesson study context.
Evolution Using Clone Detection

In modern software engineering, researchers regard a software system as an organic life form that must continue to evolve to remain successful. Unfortunately, little is known about how successful software systems have evolved, and consequently little has been learned from previous experience. In this paper, we demonstrate a heuristic to reconstruct evolution processes of existing software systems by exploiting techniques to detect duplication in large amounts of data. Based on two/three small experiments, we conclude that it helps to acquire a better understanding of the evolution processes of successful software, which may help to improve current software development methods. 1 Re-engineering Context According to several scientific studies concerning large scale software systems, more than 80% of the total budget of a software project is spent during system maintenance. This percentage is, how surprising it may seem at first, even increasing when modern development methods are used [6]. The explanation for this observation lies in the fact that modern software – more than their traditional counterparts– undergoes continual change. Software systems that don’t evolve become progressively less useful and perish, a phenomenon quite similar to Darwin’s survival of the fittest. Not surprisingly, the recent trend with agile software development processes is to recognise change as the only constant factor in software development [8, 3]. Today however, little is known about how changes affect software systems. Of course, there is the principle of software entropy, stating that an existing welldesigned program gradually looses its structure and eventually turns into chaos [11]. But on the other hand, experienced software engineers are well aware of this entropy phenomenon, and take the appropriate counter measures in the form of refactoring [5]. Other re–engineering techniques like code visualisation, also aid in reducing the systems complexity. Unfortunately, these counter measures as well as their use are seldom documented and we currently lack concrete information about how successful software systems avoid the software entropy chaos. A community of researchers tries to answer these whyandwhat questions of software evolution[12]. Based on the idea that a better insight in the evolution phenomenon must lead to improved methods, they address fundamental evolution issues as for example the nature of software evolution and its impact. Our evolution research fits in this point of view on software evolution research.
Metabolism and Excretion of Rivaroxaban, an Oral, Direct Factor Xa Inhibitor, in Rats, Dogs, and Humans

Rivaroxaban is a novel, oral, direct factor Xa inhibitor for the prevention and treatment of thromboembolic disorders. The objective of this study was to investigate the in vivo metabolism and excretion of rivaroxaban in rats, dogs, and humans. Single doses of [14C]rivaroxaban (3 and 1 mg/kg) were administered to rats (orally/intravenously) and dogs (orally), respectively. A single oral dose of [14C]rivaroxaban (10 mg) was administered to healthy human males (n = 4). Plasma and excreta were collected and profiled for radioactivity. Recovery of total radioactivity was high and ≥92% in all species. Unchanged rivaroxaban was the major compound in plasma at all time points investigated, across all species. No major or pharmacologically active circulating metabolites were detected. Rivaroxaban and its metabolites were rapidly excreted; urinary excretion of radioactivity was 25 and 52%, and fecal excretion was 67 and 43% of the dose in rats and dogs, respectively. In humans, 66% of the dose was excreted renally (36% unchanged drug) and 28% in the feces. Radioactivity profiles in excreta were similar across species. Three metabolic pathways were identified: oxidative degradation of the morpholinone moiety (major pathway) and hydrolysis of the central amide bond and of the lactam amide bond in the morpholinone ring (minor pathways). M-1, the main metabolite in excreta of all species, was eliminated via both renal and fecal/biliary routes. In total, 82 to 89% of the dose administered was assigned to unchanged rivaroxaban and its metabolites in the excreta of rats, dogs, and humans.