nils-herrmann/clean_scirepeval huggingface dataset:
Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images

Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.
Group Decision Making with Dual Hesitant Fuzzy Preference Relations

Due to the complexity and uncertainty of socioeconomic environments and cognitive diversity of group members, the cognitive information over alternatives provided by a decision organization consisting of several experts is usually uncertain and hesitant. Hesitant fuzzy preference relations provide a useful means to represent the hesitant cognitions of the decision organization over alternatives, which describe the possible degrees that one alternative is preferred to another by using a set of discrete values. However, in order to depict the cognitions over alternatives more comprehensively, besides the degrees that one alternative is preferred to another, the decision organization would give the degrees that the alternative is non-preferred to another, which may be a set of possible values. To effectively handle such common cases, in this paper, the dual hesitant fuzzy preference relation (DHFPR) is introduced and the methods for group decision making (GDM) with DHFPRs are investigated. Firstly, a new operator to aggregate dual hesitant fuzzy cognitive information is developed, which treats the membership and non-membership information fairly, and can generate more neutral results than the existing dual hesitant fuzzy aggregation operators. Since compatibility is a very effective tool to measure the consensus in GDM with preference relations, then two compatibility measures for DHFPRs are proposed. After that, the developed aggregation operator and compatibility measures are applied to GDM with DHFPRs and two GDM methods are designed, which can be applied to different decision making situations. Each GDM method involves a consensus improving model with respect to DHFPRs. The model in the first method reaches the desired consensus level by adjusting the group members’ preference values, and the model in the second method improves the group consensus level by modifying the weights of group members according to their contributions to the group decision, which maintains the group members’ original opinions and allows the group members not to compromise for reaching the desired consensus level. In actual applications, we may choose a proper method to solve the GDM problems with DHFPRs in light of the actual situation. Compared with the GDM methods with IVIFPRs, the proposed methods directly apply the original DHFPRs to decision making and do not need to transform them into the IVIFPRs, which can avoid the loss and distortion of original information, and thus can generate more precise decision results.
Investment using technical analysis and fuzzy logic

Deploy fuzzy logic engineering tools in the 0nance arena, speci0cally in the technical analysis 0eld. Since technical analysis theory consists of indicators used by experts to evaluate stock prices, the new proposed method maps these indicators into new inputs that can be fed into a fuzzy logic system. The only required inputs to these indicators are past sequence of stock prices. This method relies on fuzzy logic to formulate a decision making when certain price movements or certain price formations occur. The success of the system is measured by comparing system output versus stock price movement. The new stock evaluation method is proven to exceed market performance and it can be an excellent tool in the technical analysis 0eld. The 5exibility of the system is also demonstrated.
Lyapunov-Based Adaptive Control for the Permanent Magnet Synchronous Motor Driving a Robotic Load

This paper is inspired on the structure of the ̄eld-oriented control by presenting an analytical and practical study of an adaptive nested controller for trajectory tracking control of a permanent magnet synchronous motor (PMSM) driving a single-link arm. The originality of the new approach relies in the use of adaptive control to compensate the electrical and mechanical dynamics and in the presentation of a rigorous closed-loop system stability analysis based on Lyapunov theory. It is worthwhile to notice that the new controller resembles the pure ̄eldoriented control except for the adaptive terms. The new scheme is compared with other methodologies as with the classical nonadaptive ̄eld-oriented control algorithm and with an adaptive controller proposed in the literature. A better tracking accuracy is obtained with the proposed adaptive scheme.
On the Discrepancy Between Kleinberg's Clustering Axioms and k-Means Clustering Algorithm Behavior

This paper investigates the validity of Kleinberg’s axioms for clustering functions with respect to the quite popular clustering algorithm called k-means.We suggest that the reason why this algorithm does not fit Kleinberg’s axiomatic system stems from missing match between informal intuitions and formal formulations of the axioms. While Kleinberg’s axioms have been discussed heavily in the past, we concentrate here on the case predominantly relevant for k-means algorithm, that is behavior embedded in Euclidean space. We point at some contradictions and counter intuitiveness aspects of this axiomatic set within R that were evidently not discussed so far. Our results suggest that apparently without defining clearly what kind of clusters we expect we will not be able to construct a valid axiomatic system. In particular we look at the shape and the gaps between the clusters. Finally we demonstrate that there exist several ways to reconcile the formulation of the axioms with their intended meaning and that under this reformulation the axioms stop to be contradictory and the real-world k-means algorithm conforms to this axiomatic system.
RELEVANCE OF ARTIFICIAL BEE COLONY ALGORITHM OVER OTHER SWARM INTELLIGENCE ALGORITHMS

A new population-based search algorithm called the Bees Algorithm (BA) is presented in this paper. The algorithm mimics the food foraging behavior of swarms of honey bees. This algorithm performs a kind of neighborhood search combined with random search and can be used for both combinatorial optimization and functional optimization and with good numerical optimization results. ABC is a meta-heuristic optimization technique inspired by the intelligent foraging behavior of honeybee swarms. This paper demonstrates the efficiency and robustness of the ABC algorithm to solve MDVRP (Multiple depot vehicle routing problems). KeywordsSwarm intelligence, ant colony optimization, Genetic Algorithm, Particle Swarm optimization, Artificial Bee Colony optimization.
A simulation study of SiC MOSFET characteristics and design of gate drive card using TLP250

From small voltage regulators to large motor drives, power electronics play a very important role in present day technology. The power electronics market is currently dominated by silicon based devices. However due to inherent limitations of silicon material they are approaching thermal limit in terms of high power and high temperature operation. Performance can only be improved with the development of new power devices with better material properties. Silicon Carbide devices are now gaining popularity as next generation semiconductor devices. Due to its inherent material properties such as high breakdown field, wide band gap, high electron saturation velocity, and high thermal conductivity, they serve as a better alternative to the silicon counterparts. Here an attempt is made to study the unique properties of SiC MOSFET and requirements for designing a gate drive circuit for the same. The switching characteristics of SCH2080KE are analyzed using LTspice by performing double pulse test. Also driver circuit is designed for SiC MOSFET SCH2080KE and its performance is tested by implementing a buck converter.
Towards Requirements Reuse by Implementing Traceability in Agile Development

Requirements reusability within agile development improves software quality and team productivity. One method to implement requirements reusability is traceability, in which relations and dependencies between requirements and artifacts are identified and linked. In this paper, we propose a semiautomated methodology to implement traceability in the agile development process in order to achieve requirements reusability. The main feature of our methodology is the coupling of semi-automated semantic trace generation with the outputs of the agile development process, thus facilitating requirements and artifact reuse. In contrast to previous work, this methodology is specifically designed for practical agile processes and artifacts. Our methodology will be implemented as a component within an existing open source agile tool in order to have minimal impact on the development process. This paper fills a current gap in the area of requirements reusability through traceability and contributes to the limited existing work in agile traceability methodologies.
DIOPHANTINE ANALYSIS AND WORDS

There is no explicitly known example of a triple (g, a, x), where g ≥ 3 is an integer, a a digit in {0, . . . , g − 1} and x a real algebraic irrational number, for which one can claim that the digit a occurs infinitely often in the g–ary expansion of x. In 1909 and later in 1950, É. Borel considered such questions and suggested that the g–ary expansion of any algebraic irrational number in any base g ≥ 2 satisfies some of the laws that are satisfied by almost all numbers. For instance, the frequency where a given finite sequence of digits occurs should depend only on the base and on the length of the sequence. Hence there is a huge gap between the established theory and the expected state of the art. However, some progress have been made recently, mainly thanks to clever use of the Schmidt’s subspace Theorem. We review some of these results.
Design of a Fully Differential Two-Stage CMOS Op-Amp for High Gain , High Bandwidth Applications

In this paper design and implementation of a two stage fully differential, RC Miller compensated CMOS operational amplifier is presented. High gain enables this circuit to operate efficiently in a closed loop feedback system, whereas high bandwidth makes it suitable for high speed applications. The design is also able to address any fluctuation in supply or dc input voltages and stabilizes the operation by nullifying the effects due to perturbations. Implementation has been done in 0.18 um technology using libraries from tsmc with the help of tools from Mentor Graphics and Cadence. Op-amp designed here exhibits >95 dB DC differential gain, ~135 MHz unity gain bandwidth, phase margin of ~53, and ~132 V/uS slew rate for typical 1 pF differential capacitive load. The power dissipation for 3.3V supply voltage at 27C temperature under other nominal conditions is 2.29mW. Excellent output differential swing of 5.9V and good liner range of operation are some of the additional features of design.
Bayesian nonparametric sparse seemingly unrelated regression model ( SUR ) ∗

Seemingly unrelated regression (SUR) models are useful in studying the interactions among different variables. In a high dimensional setting or when applied to large panel of time series, these models require a large number of parameters to be estimated and suffer of inferential problems. To avoid overparametrization and overfitting issues, we propose a hierarchical Dirichlet process prior for SUR models, which allows shrinkage of SUR coefficients toward multiple locations and identification of group of coefficients. We propose a two-stage hierarchical prior distribution, where the first stage of the hierarchy consists in a Lasso conditionally independent prior distribution of the NormalGamma family for the SUR coefficients. The second stage is given by a random mixture distribution for the Normal-Gamma hyperparameters, which allows for parameter parsimony through two components: the first one is a random Dirac point-mass distribution, which induces sparsity in the SUR coefficients; the second is a Dirichlet process prior, which allows for clustering of the SUR coefficients. Our sparse SUR model with multiple locations, scales and shapes includes the Vector autoregressive models (VAR) and dynamic panel models as special cases. We consider an international business cycle applications to show the effectiveness of our model and inference approach. Our new multiple shrinkage prior model allows us to better understand shock transmission phenomena, to extract coloured networks and to classify the linkages strenght. The empirical results represent a different point of view on international business cycles providing interesting new findings in the relationship between core and pheriphery countries.
Listener-Aware Music Recommendation from Sensor and Social Media Data

Music recommender systems are lately seeing a sharp increase in popularity due to many novel commercial music streaming services. Most systems, however, do not decently take their listeners into account when recommending music items. In this note, we summarize our recent work and report our latest findings on the topics of tailoring music recommendations to individual listeners and to groups of listeners sharing certain characteristics. We focus on two tasks: context-aware automatic playlist generation (also known as serial recommendation) using sensor data and music artist recommendation using social media data.
The Chemokine Receptor CX3CR1 Defines Three Antigen-Experienced CD8 T Cell Subsets with Distinct Roles in Immune Surveillance and Homeostasis.

Infections induce pathogen-specific T cell differentiation into diverse effectors (Teff) that give rise to memory (Tmem) subsets. The cell-fate decisions and lineage relationships that underlie these transitions are poorly understood. Here, we found that the chemokine receptor CX3CR1 identifies three distinct CD8+ Teff and Tmem subsets. Classical central (Tcm) and effector memory (Tem) cells and their corresponding Teff precursors were CX3CR1- and CX3CR1high, respectively. Viral infection also induced a numerically stable CX3CR1int subset that represented ∼15% of blood-borne Tmem cells.  Tmem cells underwent more frequent homeostatic divisions than other Tmem subsets and not only self-renewed, but also contributed to the expanding CX3CR1- Tcm pool. Both Tcm and CX3CR1int cells homed to lymph nodes, but CX3CR1int cells, and not Tem cells, predominantly surveyed peripheral tissues. As CX3CR1int Tmem cells present unique phenotypic, homeostatic, and migratory properties, we designate this subset peripheral memory (tpm) cells and propose that tpm cells are chiefly responsible for the global surveillance of non-lymphoid tissues.
Some norms and reliability data for the State--Trait Anxiety Inventory and the Zung Self-Rating Depression scale.

Means and standard deviations are reported for the State-Trait Anxiety Inventory and the Zung Self-Rating Depression scale, collected during the course of a general health survey. Data for different age samples and for both sexes are presented for use in the evaluation of the significance of anxiety and depression levels in patients presenting with these symptoms. High estimates of reliability based on internal consistency statistics were found for all scales. Females scored more highly on both the measures and scores were inversely correlated with age, indicating the importance of specific and appropriate norms in assessing affective states.
HOT: A Height Optimized Trie Index for Main-Memory Database Systems

We present the Height Optimized Trie (HOT), a fast and space-efficient in-memory index structure. The core algorithmic idea of HOT is to dynamically vary the number of bits considered at each node, which enables a consistently high fanout and thereby good cache efficiency. The layout of each node is carefully engineered for compactness and fast search using SIMD instructions. Our experimental results, which use a wide variety of workloads and data sets, show that HOT outperforms other state-of-the-art index structures for string keys both in terms of search performance and memory footprint, while being competitive for integer keys. We believe that these properties make HOT highly useful as a general-purpose index structure for main-memory databases.
Deep Stacked Stochastic Configuration Networks for Non-Stationary Data Streams

The concept of stochastic configuration networks (SCNs) offers a solid framework for fast implementation of feedforward neural networks through randomized learning. Unlike conventional randomized approaches, SCNs provide an avenue to select appropriate scope of random parameters to ensure the universal approximation property. In this paper, a deep version of stochastic configuration networks, namely deep stacked stochastic configuration network (DSSCN), is proposed for modeling non-stationary data streams. As an extension of evolving stochastic configuration networks (eSCNs), this work contributes a way to grow and shrink the structure of deep stochastic configuration networks autonomously from data streams. The performance of DSSCN is evaluated by six benchmark datasets. Simulation results, compared with prominent data stream algorithms, show that the proposed method is capable of achieving comparable accuracy and evolving compact and parsimonious deep stacked network architecture.
Identification and Evaluation of Security Activities in Agile Projects

We compare four high-profile waterfall security-engineering processes (CLASP, Microsoft SDL, Cigital Touchpoints and Common Criteria) with the available preconditions within agile processes. Then, using a survey study, agile security activities are identified and evaluated by practitioners from large companies, e.g. software and telecommunication companies. Those activities are compared and a specific security engineering process is suggested for an agile process setting that can provide high benefit with low integration cost.
Donnai-Barrow syndrome (DBS/FOAR) in a child with a homozygous LRP2 mutation due to complete chromosome 2 paternal isodisomy.

Donnai-Barrow syndrome [Faciooculoacousticorenal (FOAR) syndrome; DBS/FOAR] is a rare autosomal recessive disorder resulting from mutations in the LRP2 gene located on chromosome 2q31.1. We report a unique DBS/FOAR patient homozygous for a 4-bp LRP2 deletion secondary to paternal uniparental isodisomy for chromosome 2. The propositus inherited the mutation from his heterozygous carrier father, whereas the mother carried only wild-type LRP2 alleles. This is the first case of DBS/FOAR resulting from uniparental disomy (UPD) and the fourth published case of any paternal UPD 2 ascertained through unmasking of an autosomal recessive disorder. The absence of clinical symptoms above and beyond the classical phenotype in this and the other disorders suggests that paternal chromosome 2 is unlikely to contain imprinted genes notably affecting either growth or development. This report highlights the importance of parental genotyping in order to give accurate genetic counseling for autosomal recessive disorders.
A 100 MHz two-phase four-segment DC-DC converter with light load efficiency enhancement in 0.18 μm CMOS technology

A two-phase four-segment DC-DC converter with novel coupled-inductors output network utilizing phase shedding and phase segmentation is presented for light load efficiency enhancement. The coupled inductor network increases the effective inductance value and reduces inductor current ripple. To improve light load efficiency, resonant gate drivers are employed to reduce driver losses. The DC-DC converter is implemented in 0.18 μm six-metal CMOS technology with 5 V devices, and occupies a total area of 7.77 mm2. The converter achieves a peak efficiency of 77.8% at 6 W output with 5% efficiency improvement at 1 V output due to the use of resonant gate drivers. Furthermore, with phase shedding, the converter maintains peak efficiency as the output current varies from 0.1 A to 1.86 A.
Combining SAX and Piecewise Linear Approximation to Improve Similarity Search on Financial Time Series

Efficient and accurate similarity searching on a large time series data set is an important but non- trivial problem. In this work, we propose a new approach to improve the quality of similarity search on time series data by combining symbolic aggregate approximation (SAX) and piecewise linear approximation. The approach consists of three steps: transforming real valued time series sequences to symbolic strings via SAX, pattern matching on the symbolic strings and a post-processing via Piecewise Linear Approximation.
Knowledge management capability, customer relationship management, and service quality

Knowledge management capability, customer relationship management, and service quality Shu-Mei Tseng Article information: To cite this document: Shu-Mei Tseng , (2016),"Knowledge management capability, customer relationship management, and service quality", Journal of Enterprise Information Management, Vol. 29 Iss 2 pp. Permanent link to this document: http://dx.doi.org/10.1108/JEIM-04-2014-0042
Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters

We propose a novel approach for instance segmentation given an image of homogeneous object cluster (HOC). Our learning approach is one-shot because a single video of an object instance is captured and it requires no human annotation. Our intuition is that images of homogeneous objects can be effectively synthesized based on structure and illumination priors derived from real images. A novel solver is proposed that iteratively maximizes our structured likelihood to generate realistic images of HOC. Illumination transformation scheme is applied to make the real and synthetic images share the same illumination condition. Extensive experiments and comparisons are performed to verify our method. We build a dataset consisting of pixel-level annotated images of HOC. The dataset and code will be published with the paper.