Miracle-dz/UniFAR-FTUs huggingface dataset:
Load balancing in OpenFlow-enabled switches for wireless access traffic aggregation

The immense development in mobile devices and the proliferation of ubiquitous cloud-based services have led to a surge in the utilization of remote data centers. These dramatic advances in wireless based services induce a huge traffic flow within the network. However, consequent high load on the network devices may lead to faults and thus an adverse decrease in the user experience. On the other hand, balancing the aggregated multi-flow traffic on ports of network switches is a challenging issue. This work presents a load balancing algorithm implemented in OpenDaylight controller for managing the load on the switch ports by utilizing Software-Defined Networking (SDN) architecture. The switch acts a connector which aggregates traffic from wireless access nodes. An experiment design is constructed with hardware-based OpenFlow-enabled switch and connected wireless hosts. According to the load on the ports, the developed scheme dynamically load-balances the incoming traffic towards the backhaul connection. It uses OpenFlow for gathering statistics and flow rule installation on the switch in an adaptive manner.
From Ziplock Snakes to VelcroTM SurfacesW

The use of energy minimizing deformable models in various applications has become very popular. The issue of initializing such models, however, has not received much attention although the model's performance depends critically on its initial state. We aim at obtaining good convergence and segmentation properties from a minimum of a priori information. We present a new approach to segmentation of 2{ and 3{Dimensional shapes that initializes and then optimizes a deformable model given only the data and a very small number of 2{D or 3{D seed points respectively. This is a valuable capability for medical, robotic and cartographic applications where such seed points can be naturally supplied. In eeect, the 2{D \snake" and the 3{D surface model are clamped onto the object boundary in manner reminiscent of a ziplock or velcro being closed. We develop the method's mathematic framework and show results using 2{D carto-graphic data. Preliminary results in 3{D using volumetric medical data are shown as well.
Calibration of rectangular atomic force microscope cantilevers

A method to determine the spring constant of a rectangular atomic force microscope cantilever is proposed that relies solely on the measurement of the resonant frequency and quality factor of the cantilever in fluid (typically air), and knowledge of its plan view dimensions. This method gives very good accuracy and improves upon the previous formulation by Sader et al. [Rev. Sci. Instrum. 66, 3789 (1995)] which, unlike the present method, requires knowledge of both the cantilever density and thickness.
Extract Deep Web-Detail Pages with Simple Tree Match

In this paper, we provide a method to extract data from Deep Web-Detail Pages. The method use the Simple Tree Match to compute the max match value between two trees, and use the Hungarian algorithm to trace the result of the STM compute, after this we use tree merge method to generate Wrapper. At last, we use Term Frequency to optimize the Wrapper. In experimental, we use the Wrapper to extract data; the results show that our method compared with other methods is feasible and effective.
Another Way To Go?: Some Implications of Light-duty Diesel Strategy

Conventional wisdom suggests that a large-scale shift from gasoline to diesel light-duty highway vehicles would have an impact on energy consumption, emissions, and infrastructure. Under a relatively modest scenario, based on French experience since 1970, a dieselization strategy could have displaced slightly more than half a quad of petroleum (3.7 percent of the energy consumed by light-duty vehicles) in 1992, while reducing carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NOx) emissions by 6.3, 0.8, and 0.09 million tonnes, and increasing sulfur oxides (SOx) and particulates (PM-10) by 0.03 and 0.2 million tonnes, respectively. As a percentage of pollutants attributable to light-duty vehicles, these values represent decreases of 11 percent, 10 percent, and 2 percent for CO, HC, and NOx and increases of SOx and PM-10 of 36 percent and 39 percent, respectively. The projected oil displacement and emissions impacts would have been achieved by using a diesel technology that is significantly less efficient than what is currently available and what may become available as a result of current research. Energy consumed in refining would also have been marginally reduced, although additional processing could have been required to increase the fraction of distillate and decrease that of gasoline. Finally, a shift to diesel could have broad implications on U.S. and world oil markets, modifying crude oil supply-demand balances and requiring a different mix of unit operations in domestic refineries that, in turn, could change the capital investment path of the industry, which is currently geared to maximizing gasoline production.
A Multi-Microphone Post-Filtering Approach for Speech Enhancement

Multi-microphone post-filtering allows additional noise reduction at a beamformer output. Existing techniques are either restricted to classical delay-andsum beamformers, or are based on single-channel speech enhancement algorithms that are inefficient at attenuating transient noise. In this paper, we introduce a multimicrophone post-filtering approach, applicable to adaptive beamformer, that differentiates transient noise from speech. The ratio between the transient power at the beamformer primary output and the transient power at the reference noise signals is used for indicating whether a transient is desired or interfering. Based on a Gaussian statistical model and combined with an appropriate spectral enhancement technique, a significantly reduced level of non-stationary noise is achieved without further distorting speech components. Experimental results demonstrate the effectiveness of the proposed method.
Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation

In this paper, we propose a color-based segmentation method that uses the K-means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color-based segmentation algorithm with K-means is to convert a given gray-level MR image into a color space image and then separate the position of tumor objects from other items of an MR image by using K-means clustering and histogram-clustering. Experiments demonstrate that the method can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region.
Belief Revision in Propositional Knowledge Bases

A most crucial problem in knowledge representation is the revision of knowledge when new, possibly contradictory, information is obtained (belief revision). In this paper, we address this problem when the knowledge base is a set of expressions in propositional logic. We introduce a new representation of propositional expressions using 2-dimensional tables and show why this representation is more expressive than classical propositional logic. We exploit this increased expressiveness to devise a solution to the problem of belief revision in propositional knowledge bases and describe how to represent data and knowledge with tables, as well as a simple method to perform revisions under this new notion. Finally, we provide a theoretical foundation of our method and compare it with proposed algorithms from the literature.
On delay-range-dependent stability for linear systems with time-varying delays

This note concerns with the stability problem of linear systems with time-varying delay in a given range. A modified augmented Lyapunov-Krasovskii functional (LKF) is constructed where the information on the lower, upper bounds of the delay is fully exploited. By some ingenious techniques and different tackle from the literature, the derivative of the LKF is enlarged as an affine function on the delay. According to the property of an affine function, new delay-range-dependent criteria with less conservatism are derived in terms of linear matrix inequalities (LMIs). Numerical examples authenticate the effectiveness of the proposed method.
Memory efficient and low complexity scalable soft VLC decoding for the video transmission

In this paper, we propose a scalable soft VLC decoder to significantly reduce the complexity. Generally, the soft VLC decoder needs to maintain many states for the correct decoding. Our approach reduces the complexity by reducing the table size. We reduce the table size by merging two symbols with the same prefix into one. Experimental results show that our proposed adaptive scheme can averagely save 15% of memory access compared with the state-of-the-art algorithms. Furthermore, our proposed scalable soft VLC decoder has more than 1 dB PSNR gain as compared with hard decoding
Comparative analysis of three dc-dc non-isolated buck converters

This paper presents a comparative analysis of three dc-dc non-isolated buck converters. For this purpose, a classical buck converter, an interleaved buck converter, and a three-state switching cell (3SSC) buck converter were analyzed. In order to best compare the performance of these topologies, the switching frequency, processed power, current ripple before the filter capacitor, and specification were considered equals for all tested configurations. A modular prototype of 1 kW, 200 Vdc / 60 Vdc was implemented, allowing to easily configure all topologies by just changing the magnetic components. From the experimental results, it was found that all studied converters present some current unbalance, being the less critical in the converter based on 3SSC.
Cartesian Genetic Programming

Cartesian Genetic Programming (CGP) is a well-known form of Genetic Programming developed by Julian Miller in 1999-2000. In its classic form, it uses a very simple integer address-based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). It can handle cyclic or acyclic graphs. In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program. The classical form of CGP has undergone a number of developments which have made it more useful, efficient and flexible in various ways. These include self-modifying CGP (SMCGP), cyclic connections (recurrent-CGP), encoding artificial neural networks and automatically defined functions (modular CGP). SMCGP uses functions that cause the evolved programs to change themselves as a function of time. This makes it possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on). Recurrent-CGP allows evolution to create programs which contain cyclic, as well as acyclic, connections. This enables application to tasks which require internal states or memory. It also allows CGP to create recursive equations. CGP encoded artificial neural networks represent a powerful training method for neural networks. This is because CGP is able to simultaneously evolve the networks connections weights, topology and neuron transfer functions. It is also compatible with Recurrent-CGP enabling the evolution of recurrent neural networks. The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains. It will present a live demo of how the open source cgplibrary can be used.
Effects of Robotic Social Cues on Interpersonal Attributions and Assessments of Robot Interaction Behaviors

We discuss an experiment investigating the influence of social cues expressed by a robot on human attributions of interpersonal characteristics towards a robot and assessments of its interaction behaviors. During a hallway navigation scenario, participants were exposed to varying expressions of proxemic behavior and gaze cues over repeated interactions with a robot. Analysis of participant perceptions of the robot’s personality revealed that cues indicative of socially mindful behavior expressed by the robot promote positive interpersonal attributions and perceptions of safe robot behavior. Results of the present study contribute to the scholarly discussion on robotic design for encouraging natural and effective interactions between humans and robots.
Association of Pesticide Exposure with Neurologic Dysfunction and Disease

Poisoning by acute high-level exposure to certain pesticides has well-known neurotoxic effects, but whether chronic exposure to moderate levels of pesticides is also neurotoxic is more controversial. Most studies of moderate pesticide exposure have found increased prevalence of neurologic symptoms and changes in neurobehavioral performance, reflecting cognitive and psychomotor dysfunction. There is less evidence that moderate exposure is related to deficits in sensory or motor function or peripheral nerve conduction, but fewer studies have considered these outcomes. It is possible that the most sensitive manifestation of pesticide neurotoxicity is a general malaise lacking in specificity and related to mild cognitive dysfunction, similar to that described for Gulf War syndrome. Most studies have focused on organophosphate insecticides, but some found neuro-toxic effects from other pesticides, including fungicides, fumigants, and organochlorine and carbamate insecticides. Pesticide exposure may also be associated with increased risk of Parkinson disease; several classes of pesticides, including insecticides, herbicides, and fungicides, have been implicated. Studies of other neurodegenerative diseases are limited and inconclusive. Future studies will need to improve assessment of pesticide exposure in individuals and consider the role of genetic susceptibility. More studies of pesticides other than organophosphates are needed. Major unresolved issues include the relative importance of acute and chronic exposure, the effect of moderate exposure in the absence of poisoning, and the relationship of pesticide-related neurotoxicity to neurodegenerative disease.
From theory to practice: critical thinking as a multifaceted concept

In this article I reflect on the problem that critical thinking skills are simultaneously seen as important in higher education, and yet remain difficult to define. I draw on a small pilot study to explore the various dimensions to critical thinking and the extent to which students and their tutors identify similar characteristics as being of equal importance. Participants were also asked to reflect on the attainment level of students in critical thinking. The results of this pilot study suggest that, whilst all participants agree on the importance of critical thinking skills, there are some variations in how they understand the term and the extent to which they judge that students employ these skills.
sing multi-temporal remote sensor imagery to detect arthquake-triggered landslides

Landslides are a major type of geohazards claiming thousands of casualties and billions of dollars in property damages every year. Catastrophic landslide activities are often triggered by some extreme events such as earthquakes, excessive precipitations, or volcanic eruptions. Quickly identifying the spatial distribution of landslides induced by these extreme events is crucial for coordinating rescue efforts and planning in situ investigations. In this study, we propose an automated method for detecting the spatial distribution of earthquake-triggered landslides by examining after-event vegetation changes. Central to this method is the use of preand post-event remote sensor images covering the same area. Geometric correction and radiometric normalization are performed before deriving a vegetation index from each image. Then, an image differencing procedure is applied to the two derived indices. With the resultant difference image, an initial landslide distribution map is generated by highlighting the pixels with a threshold percentage decrease in the brightness values as a direct result of the image subtraction. The threshold percentage value is interactively determined by using a visual interpretation method. The final landslide distribution map is produced after using a modal filter to suppress boundary errors in the initial map. This method has been implemented in a test site, approximately 30 km from the epicenter of the Sichuan earthquake (7.9 Ms) that struck on 12 May 2008. A pre-event Thematic Mapper image and a post-event Advanced Spaceborne Thermal Emission and Reflection Radiometer scene are used. The thematic accuracy assessment indicates that 90% of the landslides have correctly been mapped. Given the relatively simple procedures and the good mapping accuracy, the image processing and change detection study method identified in this
Single-exposure digital holography by a generalized wave-splitting phase-shifting method

A single-exposure generalized phase-shifting digital holography based on a wave-splitting method. A phase-encoded reference wave is used for phase-shifting. Using the known amplitude and phase distributions of the reference wave, the fully complex amplitude of the object wave is obtained. The proposed method requires not devised optical systems but ordinary imaging devices, such as CCD cameras. A preliminary experimental results with a spherical wave and a random phase wave as reference waves are given to confirm the proposed method.
PATZ1 Is a DNA Damage-Responsive Transcription Factor That Inhibits p53 Function

ABSTRACT Insults to cellular health cause p53 protein accumulation, and loss of p53 function leads to tumorigenesis. Thus, p53 has to be tightly controlled. Here we report that the BTB/POZ domain transcription factor PATZ1 (MAZR), previously known for its transcriptional suppressor functions in T lymphocytes, is a crucial regulator of p53. The novel role of PATZ1 as an inhibitor of the p53 protein marks its gene as a proto-oncogene. PATZ1-deficient cells have reduced proliferative capacity, which we assessed by transcriptome sequencing (RNA-Seq) and real-time cell growth rate analysis. PATZ1 modifies the expression of p53 target genes associated with cell proliferation gene ontology terms. Moreover, PATZ1 regulates several genes involved in cellular adhesion and morphogenesis. Significantly, treatment with the DNA damage-inducing drug doxorubicin results in the loss of the PATZ1 transcription factor as p53 accumulates. We find that PATZ1 binds to p53 and inhibits p53-dependent transcription activation. We examine the mechanism of this functional inhibitory interaction and demonstrate that PATZ1 excludes p53 from DNA binding. This study documents PATZ1 as a novel player in the p53 pathway.
Developing the Leisure Education Scale

ABSTRACT The primary goal of this study is to reveal the structures that explain the concept of leisure education for individuals in Turkey and develop an instrument to directly assess these structures. The study group consists of 711 students who receive formal education in Anadolu University for the pilot study and 1104 students who receive formal education in Anadolu University for the ultimate application. The procedure stages of the study embraced four topics: specify domain of constructs, generate a sample of items, collect data and purify the measure (pilot study), and assess reliability and construct validity with the new data. Experimental evidence concerning psychometric qualities was revealed via these procedure stages and the Leisure Education Scale was developed (36 items and 7 dimensions). Being named as awareness, intrinsic motivation, extrinsic motivation, social interaction skills, boredom, time management, and problem-solving, this seven-factor structure also forms the leisure education structural model, which is peculiar to Turkish society.
Rate Matrices for Analyzing Large Families of Protein Sequences

We propose and study a new approach for the analysis of families of protein sequences. This method is related to the LogDet distances used in phylogenetic reconstructions; it can be viewed as an attempt to embed these distances into a multidimensional framework. The proposed method starts by associating a Markov matrix to each pairwise alignment deduced from a given multiple alignment. The central objects under consideration here are matrix-valued logarithms L of these Markov matrices, which exist under conditions that are compatible with fairly large divergence between the sequences. These logarithms allow us to compare data from a family of aligned proteins with simple models (in particular, continuous reversible Markov models) and to test the adequacy of such models. If one neglects fluctuations arising from the finite length of sequences, any continuous reversible Markov model with a single rate matrix Q over an arbitrary tree predicts that all the observed matrices L are multiples of Q. Our method exploits this fact, without relying on any tree estimation. We test this prediction on a family of proteins encoded by the mitochondrial genome of 26 multicellular animals, which include vertebrates, arthropods, echinoderms, molluscs, and nematodes. A principal component analysis of the observed matrices L shows that a single rate model can be used as a rough approximation to the data, but that systematic deviations from any such model are unmistakable and related to the evolutionary history of the species under consideration.
Improve availability of fault-tolerant computing: Optimal multi-task allocation in MapReduce

MapReduce emerges as a popular programming model for data-intensive scalable computing. As one of core components, task schedule comes to be a very hot topic in recent studies. However, the computing on fault-tolerant applications has not been covered yet. In the paper, we at first point out the importance of fault-tolerant computing and propose a novel model to find out an optimal task allocation scheme, which allows us to obtain the optimal job availability. To analyze the properties of task allocation, we proof several theorems and evaluate them with analysis and experiments. Our experiments show that the reduction of job unavailability by our method is about one order of magnitude as compared to the systematical allocation.
Participants’ physical activity levels and evaluations of a school sport programme in Papua New Guinea

In its contemporary form, sport for development has been positioned as an important part of strategic policy for a variety of governmental and sporting organisations. One area that has been targeted by governments and a variety of sporting partners has been the impact of sport on non-communicable diseases (NCDs, sometimes colloquially referred to as ‘lifestyle diseases’ such as obesity). However, relatively little evidence exists for the impact of sport programmes on the reduction of NCDs and future health burden. This study sought to examine the physical activity levels of school-aged children in Papua New Guinea (PNG) during their participation in a targeted sport programme operating during school hours. Data collection methods employed were physical activity recall and pedometer counts, as well as perceptions, intentions, and knowledge of participants. Children in this study were found to have lower physical activity levels during the after-school period than has been reported in the literature for children in other countries. Overall, children were active during the sport programme, completing 1179 steps on average (which constitutes 10% of the suggested daily values for children of a similar age). No profound differences were observed in either the qualitative or quantitative data across different schools, regions, or genders. The responses from children regarding the sport programme were notably positive.