nils-herrmann/clean_scirepeval huggingface dataset:
Parallel Concatenated Trellis Coded Modulation1

In this paper, we propose a new solution to parallel concatenation of trellis codes with multilevel amplitude/phase modulations and a suitable bit by bit iterative decoding structure. Examples are given for throughput 2 and 4 bits/sec/Hz with 8PSK, 16QAM, and 64QAM modulations. For parallel concatenated trellis codes in the examples, rate 2/3 and 4/5, 8, and 16-state binary convolutional codes with Ungerboeck mapping by set partitioning (natural mapping), a reordered mapping, and Gray code mapping are used. The performance of these codes is within 1 dB from the Shannon limit at a bit error probability of 10 −7 for a given throughput, which outperforms the performance of all codes reported in the past for the same throughput.
Data Fusion Algorithms for Multiple Inertial Measurement Units

A single low cost inertial measurement unit (IMU) is often used in conjunction with GPS to increase the accuracy and improve the availability of the navigation solution for a pedestrian navigation system. This paper develops several fusion algorithms for using multiple IMUs to enhance performance. In particular, this research seeks to understand the benefits and detriments of each fusion method in the context of pedestrian navigation. Three fusion methods are proposed. First, all raw IMU measurements are mapped onto a common frame (i.e., a virtual frame) and processed in a typical combined GPS-IMU Kalman filter. Second, a large stacked filter is constructed of several IMUs. This filter construction allows for relative information between the IMUs to be used as updates. Third, a federated filter is used to process each IMU as a local filter. The output of each local filter is shared with a master filter, which in turn, shares information back with the local filters. The construction of each filter is discussed and improvements are made to the virtual IMU (VIMU) architecture, which is the most commonly used architecture in the literature. Since accuracy and availability are the most important characteristics of a pedestrian navigation system, the analysis of each filter's performance focuses on these two parameters. Data was collected in two environments, one where GPS signals are moderately attenuated and another where signals are severely attenuated. Accuracy is shown as a function of architecture and the number of IMUs used.
The Construction of Firm's IT Capability and Its Impact on IT Assimilation: An Empirical Investigation in China

The paper’s research purpose is to discuss the key firm-specific IT capability and its impact on the business value of IT. In the context of IT application in China, the paper builds research model based on Resource-Based View, this model describes how the partnership between business and IT management partially mediates the effects of IT infrastructure capability and managerial IT skills on the organization-level of IT assimilation(as proxy for business value of IT ). This research releases 105 questionnaires to part-time MBA in the Renmin University of China and gets 70 valid questionnaires, then analyzed the measurement and structural research model by PLS method. The result of the structural model shows the investment in infrastructure capability and managerial IT skills should be transformed into the partnership between IT and business, and then influence the IT assimilation. The paper can give suggestions to the firms about how to identify and improve IT capability, which will help organization to get superior business value from IT investment.
A neural network based predictive mechanism for available bandwidth

Most recent developments of computer sciences, such as Web services, grid, peer-to-peer, and mobile computing, are network-based computing. Their applicability depends on the availability of the underlying network bandwidth. However, network resources are shared and the available network bandwidth varies with time. There is no satisfactory solution available for network performance predictions. This lack of prediction limits the applicability of network-based computing, especially for grid computing where concurrent remote processing is essential. In this study, we propose an artificial neural network (ANN) based approach for network performance prediction. The ANN mechanism has been tested on classical trace files and compared with the well-known system NWS (network weather service) for performance. Experimental results show the ANN approach always provides an improved prediction over that of NWS. ANN has a real potential in network computing.
METIS-II: low resource machine translation

METIS-II was an EU-FET MT project running from October 2004 to September 2007, which aimed at translating free text input without resorting to parallel corpora. The idea was to use “basic” linguistic tools and representations and to link them with patterns and statistics from the monolingual target-language corpus. The METIS-II project has four partners, translating from their “home” languages Greek, Dutch, German, and Spanish into English. The paper outlines the basic ideas of the project, their implementation, the resources used, and the results obtained. It also gives examples of how METIS-II has continued beyond its lifetime and the original scope of the project. On the basis of the results and experiences obtained, we believe that the approach is promising and offers the potential for development in various directions.
Simulation optimization in Discrete Event Logistics Systems: The challenge of operational control

Simulation optimization tools have the potential to provide an unprecedented level of support for the design and execution of operational control in Discrete Event Logistics Systems (DELS). While much of the simulation optimization literature has focused on developing and exploiting integration and syntactical interoperability between simulation and optimization tools, maximizing the effectiveness of these tools to support the design and execution of control behavior requires an even greater degree of interoperability than the current state of the art. In this paper, we propose a modeling methodology for operational control decision-making that can improve the interoperability between these two analysis methods and their associated tools in the context of DELS control. This methodology establishes a standard definition of operational control for both simulation and optimization methods and defines a mapping between decision variables (optimization) and execution mechanisms (simulation / base system). The goal is a standard for creating conforming simulation and optimization tools that are capable of meeting the functional needs of operational control decision making in DELS.
A high voltage, constant current stimulator for electrocutaneous stimulation through small electrodes

A high-voltage stimulator has been designed to allow transcutaneous stimulation of tactile fibers of the fingertip. The stimulator's output stage was based upon an improved Howland current pump topology, modified to allow high load impedances and small currents, The compliance voltage of approximately 800 V is achieved using commercially available high-voltage operational amplifiers. The output current accuracy is better than /spl plusmn/5% over the range of 1 to 25 mA for 30 /spl mu/s or longer pulses. The rise time for square pulses is less than 1 /spl mu/s. High-voltage, common-mode, latch-up power supply problems and solutions are discussed. The stimulator's input stage is optically coupled to the controlling computer and complies with applicable safety standards for use in a hospital environment. The design presented here is for monophasic stimulation only, but could be modified for biphasic stimulation.
Throughput and Delay Estimator for 2.4GHz WiFi APs: A Machine Learning-Based Approach

This paper reports our recent result in designing a function for autonomous APs to estimate throughput and delay of its clients in 2.4GHz WiFi channels to support those APs' dynamic channel selection. Our function takes as inputs the traffic volume and strength of signals emitted from nearby interference APs as well as the target AP's traffic volume. By this function, the target AP can estimate throughput and delay of its clients without actually moving to each channel, it is just required to monitor IEEE802.11 MAC frames sent or received by the interference APs. The function is composed of an SVM-based classifier to estimate capacity saturation and a regression function to estimate both throughput and delay in case of saturation in the target channel. The training dataset for the machine learning is created by a highly-precise network simulator. We have conducted over 10,000 simulations to train the model, and evaluated using additional 2,000 simulation results. The result shows that the estimated throughput error is less than 10%.
Technology integration in education in developing countries : Guidelines to policy makers 1

Technology such as Information and Communication Technology (ICT) is a potent force in driving economic, social, political and educational reforms. Countries, particularly developing ones, cannot afford to stay passive to ICT if they are to compete and strive in the global economy. The health of the economy of any country, poor or rich, developed or developing, depends substantially on the level and quality of the education it provides to its workforce. Education reform is occurring throughout the world and one of the tenets of the reform is the introduction and integration of ICT in the education system. The successful integration of any technology, thus ICT, into the classroom warrants careful planning and depends largely on how well policy makers understand and appreciate the dynamics of such integration. This paper offers a set of guidelines to policy makers for the successful integration of ICT into the classroom.
Global rank-invariant set normalization (GRSN) to reduce systematic distortions in microarray data

Microarray technology has become very popular for globally evaluating gene expression in biological samples. However, non-linear variation associated with the technology can make data interpretation unreliable. Therefore, methods to correct this kind of technical variation are critical. Here we consider a method to reduce this type of variation applied after three common procedures for processing microarray data: MAS 5.0, RMA, and dChip®. We commonly observe intensity-dependent technical variation between samples in a single microarray experiment. This is most common when MAS 5.0 is used to process probe level data, but we also see this type of technical variation with RMA and dChip® processed data. Datasets with unbalanced numbers of up and down regulated genes seem to be particularly susceptible to this type of intensity-dependent technical variation. Unbalanced gene regulation is common when studying cancer samples or genetically manipulated animal models and preservation of this biologically relevant information, while removing technical variation has not been well addressed in the literature. We propose a method based on using rank-invariant, endogenous transcripts as reference points for normalization (GRSN). While the use of rank-invariant transcripts has been described previously, we have added to this concept by the creation of a global rank-invariant set of transcripts used to generate a robust average reference that is used to normalize all samples within a dataset. The global rank-invariant set is selected in an iterative manner so as to preserve unbalanced gene expression. Moreover, our method works well as an overlay that can be applied to data already processed with other probe set summary methods. We demonstrate that this additional normalization step at the "probe set level" effectively corrects a specific type of technical variation that often distorts samples in datasets. We have developed a simple post-processing tool to help detect and correct non-linear technical variation in microarray data and demonstrate how it can reduce technical variation and improve the results of downstream statistical gene selection and pathway identification methods.
Design and control of a steerable screw in-pipe inspection robot

An in-pipe inspection robot is designed in this paper for which its pitch rate is controllable and an optimal control is implemented for it subject to input minimization. In-pipe inspection robots are requisite mobile robots to investigate the pipelines. Most of the in-pipe inspection robots are supposed to move with constant pitch of rate. An in-pipe inspection robot is proposed in this paper based on screw locomotion which is steerable in order to handle the pitch rate of the movement and bypass the probable obstacles. Considering the fact that for this robot the number of actuators of the system is more than the Degrees of Freedom (DOFs) of the system, optimization of its control inputs is performed using optimal control approach. In this paper the dynamic model of the mentioned steerable screw in-pipe inspection robot is extracted and it is controlled within a predefined trajectory in an optimal way. The proper mechanism is designed and its related kinematics and kinetics are derived. Then the objective function is defined to optimize the controlling input error simultaneously. The nonlinear state space is linearized around its operating point and optimization is implemented using Linear Quadratic Regulator (LQR). Validity and efficiency of the designed robot and controller are verified using MATLAB simulations.
Multivariate Bernoulli distribution

In this paper, we consider the multivariate Bernoulli distribution as a model to estimate the structure of graphs with binary nodes. This distribution is discussed in the framework of the exponential family, and its statistical properties regarding independence of the nodes are demonstrated. Importantly the model can estimate not only the main effects and pairwise interactions among the nodes but also is capable of modeling higher order interactions, allowing for the existence of complex clique effects. We compare the multivariate Bernoulli model with existing graphical inference models – the Ising model and the multivariate Gaussian model, where only the pairwise interactions are considered. On the other hand, the multivariate Bernoulli distribution has an interesting property in that independence and uncorrelatedness of the component random variables are equivalent. Both the marginal and conditional distributions of a subset of variables in the multivariate Bernoulli distribution still follow the multivariate Bernoulli distribution. Furthermore, the multivariate Bernoulli logistic model is developed under generalized linear model theory by utilizing the canonical link function in order to include covariate information on the nodes, edges and cliques. We also consider variable selection techniques such as LASSO in the logistic model to impose sparsity structure on the graph. Finally, we discuss extending the smoothing spline ANOVA approach to the multivariate Bernoulli logistic model to enable estimation of non-linear effects of the predictor variables.
VideoReach: an online video recommendation system

This paper presents a novel online video recommendation system called VideoReach, which alleviates users' efforts on finding the most relevant videos according to current viewings without a sufficient collection of user profiles as required in traditional recommenders. In this system, video recommendation is formulated as finding a list of relevant videos in terms of multimodal relevance (i.e. textual, visual, and aural relevance) and user click-through. Since different videos have different intra-weights of relevance within an individual modality and inter-weights among different modalities, we adopt relevance feedback to automatically find optimal weights by user click-though, as well as an attention fusion function to fuse multimodal relevance. We use 20 clips as the representative test videos, which are searched by top 10 queries from more than 13k online videos, and report superior performance compared with an existing video site.
Multimodal nonlinear imaging of the human cornea.

To evaluate the potential of third-harmonic generation (THG) microscopy combined with second-harmonic generation (SHG) and two-photon excited fluorescence (2PEF) microscopies for visualizing the microstructure of the human cornea and trabecular meshwork based on their intrinsic nonlinear properties.



Fresh human corneal buttons and corneoscleral discs from an eye bank were observed under a multiphoton microscope incorporating a titanium-sapphire laser and an optical parametric oscillator for the excitation, and equipped with detection channels in the forward and backward directions.



Original contrast mechanisms of THG signals in cornea with physiological relevance were elucidated. THG microscopy with circular incident polarization detected microscopic anisotropy and revealed the stacking and distribution of stromal collagen lamellae. THG imaging with linear incident polarization also revealed cellular and anchoring structures with micrometer resolution. In edematous tissue, a strong THG signal around cells indicated the local presence of water. Additionally, SHG signals reflected the distribution of fibrillar collagen, and 2PEF imaging revealed the elastic component of the trabecular meshwork and the fluorescence of metabolically active cells.



The combined imaging modalities of THG, SHG, and 2PEF provide key information about the physiological state and microstructure of the anterior segment over its entire thickness with remarkable contrast and specificity. This imaging method should prove particularly useful for assessing glaucoma and corneal physiopathologies.
e Sayana: A web based clinic appointment system for MOH clinics in Sri Lanka

MOH Office is the preventive side Primary Health Care unit in Sri Lanka. It conduct varies clinics and health programs to reduce mortality and morbidity. Health promotion and preventive programs along with early and rapid access to treatment are all key factors to improve health sector. In MOH clinics the waiting time can be divided in to 3 types. Which are waiting time for registration, before consultation and after consultation. These are the indicators for one part of the service quality. Long waiting time for registration is more common feature in MOH clinics in SL. Waiting time after consultation is less in MOH clinics. This study mainly focuses on reducing the waiting time for registration.
Proximate , Functional , Antinutrient and Antimicrobial Properties of Avocado Pear ( Persea americana ) Seeds

Avocado plant (Persea americana), a plant belonging to the family of Lauraceae and genus, persea bears fruit known as avocado pear or alligator pear that contains the avocado pear seed. Reported uses of avocado pear seed include use in the management of hypertension, diabetes, cancer and inflammation [7-9]. The fruit is known as ube oyibo (loosely translated to ‘foreign pear’) in Ojoto and neighboring Igbo speaking communities south east Nigeria . Different parts of avocado pear were used in traditional medications for various purposes including as an antimicrobial [11,12]. That not withstanding, the avocado pear seeds are essentially discarded as agro-food wastes hence underutilized. Exploring the possible dietary and therapeutic potentials of especially underutilized agro-food wastes will in addition reduce the possible environmental waste burden [1315]. Thus, this study was warranted and aimed at assessing the proximate, functional, anti-nutrients and antimicrobial properties of avocado pear seed to provide basis for its possible dietary use and justification for its ethno-medicinal use. The objectives set to achieving the study aim as stated were by determining the proximate, functional, antinutrient and antimicrobial properties of avocado pear (Persea americana) seeds using standard methods as in the study design.
A Sex Pheromone in the Desert Tenebrionid Beetle Parastizopus armaticeps

Males of the desert beetle Parastizopus armaticeps (Pér.) (Coleoptera: Tenebrionidae) exhibit a characteristic calling behavior that attracts females by raising the tip of the abdomen, exposing the aedeagus, and remaining in this posture for a few seconds while emitting a pheromone. We collected the pheromone by holding a solid phase microextraction fiber (100 μm polydimethylsiloxane) close to the aedeagus for 5 s and analyzed the volatiles collected by gas chromatography/mass spectrometry. The volatiles consisted of 3-methylphenol (52%), ethyl-1,4-benzoquinone (48%), and 3-ethylphenol (2%). The pheromone originated from the aedeagal glands. In the gland reservoirs, these compounds (2.1%) were mixed with ethyl, isopropyl, and propyl esters of fatty acids (24.2%), and a mixture of hydrocarbons (69.1%). The mean amount of volatiles extracted from gland reservoirs was 0.92 ± 0.83 μg. Chemo-orientation experiments with a servosphere show that females responded only to the ternary volatile mixture. Females stopped walking, elevated the front parts of their bodies with erected antennae, turned slowly on their own axis, and walked upwind toward the odor source. Single components or binary mixtures did not elicit responses from females. Males did not respond to the pheromone. Evolutionary aspects of this pheromone system are discussed.
Common electronic origin of superconductivity in (Li,Fe)OHFeSe bulk superconductor and single-layer FeSe/SrTiO3 films

The mechanism of high-temperature superconductivity in the iron-based superconductors remains an outstanding issue in condensed matter physics. The electronic structure plays an essential role in dictating superconductivity. Recent revelation of distinct electronic structure and high-temperature superconductivity in the single-layer FeSe/SrTiO3 films provides key information on the role of Fermi surface topology and interface in inducing or enhancing superconductivity. Here we report high-resolution angle-resolved photoemission measurements on the electronic structure and superconducting gap of an FeSe-based superconductor, (Li0.84Fe0.16)OHFe0.98Se, with a Tc at 41 K. We find that this single-phase bulk superconductor shows remarkably similar electronic behaviours to that of the superconducting single-layer FeSe/SrTiO3 films in terms of Fermi surface topology, band structure and the gap symmetry. These observations provide new insights in understanding high-temperature superconductivity in the single-layer FeSe/SrTiO3 films and the mechanism of superconductivity in the bulk iron-based superconductors.
Integrating support vector machines in a hierarchical output space decomposition framework

This paper presents a new approach called Hierarchical Support Vector Machines (HSVM), to address multiclass problems. The method solves a series of maxcut problems to hierarchically and recursively partition the set of classes into two-subsets, till pure leaf nodes that have only one class label, are obtained. The SVM is applied at each internal node to construct the discriminant function for a binary metaclass classifier. Because maxcut unsupervised decomposition uses distance measures to investigate the natural class groupings. HSVM has a fast and intuitive SVM training process that requires little tuning and yields both high accuracy levels and good generalization. The HSVM method was applied to Hyperion hyperspectral data collected over the Okavango Delta of Botswana. Classification accuracies and generalization capability are compared to those achieved by the Best Basis Binary Hierarchical Classifier, a Random Forest CART binary decision tree classifier and Binary Hierarchical Support Vector Machines.