nils-herrmann/clean_scirepeval huggingface dataset:
Natural Compounds as Regulators of the Cancer Cell Metabolism

Even though altered metabolism is an "old" physiological mechanism, only recently its targeting became a therapeutically interesting strategy and by now it is considered an emerging hallmark of cancer. Nevertheless, a very poor number of compounds are under investigation as potential modulators of cell metabolism. Candidate agents should display selectivity of action towards cancer cells without side effects. This ideal favorable profile would perfectly overlap the requisites of new anticancer therapies and chemopreventive strategies as well. Nature represents a still largely unexplored source of bioactive molecules with a therapeutic potential. Many of these compounds have already been characterized for their multiple anticancer activities. Many of them are absorbed with the diet and therefore possess a known profile in terms of tolerability and bioavailability compared to newly synthetized chemical compounds. The discovery of important cross-talks between mediators of the most therapeutically targeted aberrancies in cancer (i.e., cell proliferation, survival, and migration) and the metabolic machinery allows to predict the possibility that many anticancer activities ascribed to a number of natural compounds may be due, in part, to their ability of modulating metabolic pathways. In this review, we attempt an overview of what is currently known about the potential of natural compounds as modulators of cancer cell metabolism.
A Review of Data Quality Assessment Methods for Public Health Information Systems

High quality data and effective data quality assessment are required for accurately evaluating the impact of public health interventions and measuring public health outcomes. Data, data use, and data collection process, as the three dimensions of data quality, all need to be assessed for overall data quality assessment. We reviewed current data quality assessment methods. The relevant study was identified in major databases and well-known institutional websites. We found the dimension of data was most frequently assessed. Completeness, accuracy, and timeliness were the three most-used attributes among a total of 49 attributes of data quality. The major quantitative assessment methods were descriptive surveys and data audits, whereas the common qualitative assessment methods were interview and documentation review. The limitations of the reviewed studies included inattentiveness to data use and data collection process, inconsistency in the definition of attributes of data quality, failure to address data users' concerns and a lack of systematic procedures in data quality assessment. This review study is limited by the coverage of the databases and the breadth of public health information systems. Further research could develop consistent data quality definitions and attributes. More research efforts should be given to assess the quality of data use and the quality of data collection process.
Stereo Matching Using Belief Propagation

In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, and a binary process for occlusion. After eliminating the line process and the binary process by introducing two robust functions, we apply the belief propagation algorithm to obtain the maximum a posteriori (MAP) estimation in the Markov network. Other low-level visual cues (e.g., image segmentation) can also be easily incorporated in our stereo model to obtain better stereo results. Experiments demonstrate that our methods are comparable to the state-of-the-art stereo algorithms for many test cases.
Blob extraction based character segmentation method for automatic license plate recognition system

A character segmentation algorithm for automatic license plate recognition is presented in this paper. Character regions are selected through binarization, connected component analysis, and character recognition. A blob analysis operation excludes noisy blobs, merges fragmented blobs, and splits clumped blobs. A character segmentation module achieved an accuracy rate of 97.2%. The recognition accuracy of the complete system with license plate localization was 90.9%. In depth analysis of failure cases is also provided for better understanding of the algorithm and a future development direction.
Adaptive event detection with time-varying poisson processes

Time-series of count data are generated in many different contexts, such as web access logging, freeway traffic monitoring, and security logs associated with buildings. Since this data measures the aggregated behavior of individual human beings, it typically exhibits a periodicity in time on a number of scales (daily, weekly,etc.) that reflects the rhythms of the underlying human activity and makes the data appear non-homogeneous. At the same time, the data is often corrupted by a number of bursty periods of unusual behavior such as building events, traffic accidents, and so forth. The data mining problem of finding and extracting these anomalous events is made difficult by both of these elements. In this paper we describe a framework for unsupervised learning in this context, based on a time-varying Poisson process model that can also account for anomalous events. We show how the parameters of this model can be learned from count time series using statistical estimation techniques. We demonstrate the utility of this model on two datasets for which we have partial ground truth in the form of known events, one from freeway traffic data and another from building access data, and show that the model performs significantly better than a non-probabilistic, threshold-based technique. We also describe how the model can be used to investigate different degrees of periodicity in the data, including systematic day-of-week and time-of-day effects, and make inferences about the detected events (e.g., popularity or level of attendance). Our experimental results indicate that the proposed time-varying Poisson model provides a robust and accurate framework for adaptively and autonomously learning how to separate unusual bursty events from traces of normal human activity.
Power aware scheduling and power control techniques for multiuser detection enabled wireless mobile ad-hoc networks

Multiuser Detection (MUD) based receivers theoretically require no power control (PC) as they have the ability to separate signals regardless of their relative power levels as long as these signals achieve a suitable SNR. In practice, receiver designs have finite dynamic range. In this paper, power aware scheduling (PAS) and power control (PC) algorithms are investigated to address the finite MUD dynamic range and performance results are shown. The final PAS algorithm and motivating factors behind the design selections made on the DARPA Interference Multiple Access (DIMA) program are highlighted as well as different approaches involving both scheduling and PC. The techniques selected on the DIMA program are currently operating as part of the DEVIA mobile real-time experiments.
Robust dual control MPC with application to soft-landing control

Dual control frameworks for systems subject to uncertainties aim at simultaneously learning the unknown parameters while controlling the system dynamics. We propose a robust dual model predictive control algorithm for systems with bounded uncertainty with application to soft landing control. The algorithm exploits a robust control invariant set to guarantee constraint enforcement in spite of the uncertainty, and a constrained estimation algorithm to guarantee admissible parameter estimates. The impact of the control input on parameter learning is accounted for by including in the cost function a reference input, which is designed online to provide persistent excitation. The reference input design problem is non-convex, and here is solved by a sequence of relaxed convex problems. The results of the proposed method in a soft-landing control application in transportation systems are shown.
Selective Database Projections Based Approach for Mining High-Utility Itemsets

High-utility itemset mining (HUIM) is an emerging area of data mining and is widely used. HUIM differs from the frequent itemset mining (FIM), as the latter considers only the frequency factor, whereas the former has been designed to address both quantity and profit factors to reveal the most profitable products. The challenges of generating the HUI include exponential complexity in both time and space. Moreover, the pruning techniques of reducing the search space, which is available in FIM because of their monotonic and anti-monotonic properties, cannot be used in HUIM. In this paper, we propose a novel selective database projection-based HUI mining algorithm (SPHUI-Miner). We introduce an efficient data format, named HUI-RTPL, which is an optimum and compact representation of data requiring low memory. We also propose two novel data structures, viz, selective database projection utility list and Tail-Count list to prune the search space for HUI mining. Selective projections of the database reduce the scanning time of the database making our proposed approach more efficient. It creates unique data instances and new projections for data having less dimensions thereby resulting in faster HUI mining. We also prove upper bounds on the amount of memory consumed by these projections. Experimental comparisons on various benchmark data sets show that the SPHUI-Miner algorithm outperforms the state-of-the-art algorithms in terms of computation time, memory usage, scalability, and candidates generation.
Predictive Analysis of Water Quality Parameters using Deep Learning

Lakes and reservoirs are important water resources. Reservoirs are vital water resources to support all living organism. They provide clean water and habitat for a complex variety of aquatic life. Water from such resources can be used for diverse purposes such as, industry usage, agriculture and supplies for drinking water and recreation and aesthetic value. Apart from this, reservoirs also helpful to get hydro-electric power, flood control and scenic beauty. Water collected in such resources can be utilized in drought situation also. Unfortunately, these important resources are being polluted and the quality of water is being influenced by numerous factors. The quality of water is deteriorated by anthropogenic activities, indiscriminate disposal of sewage, human activities and also industry waste. Water quality monitoring of reservoirs is essential in exploitation of aquatic resources conservation. The quality of water helps in regulating the biotic diversity and biomass, energy and rate of
Investigating whether hyperNEAT produces modular neural networks

HyperNEAT represents a class of neuroevolutionary algorithms that captures some of the power of natural development with a computationally efficient high-level abstraction of development. This class of algorithms is intended to provide many of the desirable properties produced in biological phenotypes by natural developmental processes, such as regularity, modularity and hierarchy. While it has been previously shown that HyperNEAT produces regular artificial neural network (ANN) phenotypes, in this paper we investigated the open question of whether HyperNEAT can produce modular ANNs. We conducted such research on problems where modularity should be beneficial, and found that HyperNEAT failed to generate modular ANNs. We then imposed modularity on HyperNEAT's phenotypes and its performance improved, demonstrating that modularity increases performance on this problem. We next tested two techniques to encourage modularity in HyperNEAT, but did not observe an increase in either modularity or performance. Finally, we conducted tests on a simpler problem that requires modularity and found that HyperNEAT was able to rapidly produce modular solutions that solved the problem. We therefore present the first documented case of HyperNEAT producing a modular phenotype, but our inability to encourage modularity on harder problems where modularity would have been beneficial suggests that more work is needed to increase the likelihood that HyperNEAT and similar algorithms produce modular ANNs in response to challenging, decomposable problems.
A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs †

We recently presented a new model for singing synthesis based on a modified version of the WaveNet architecture. Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre. This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times. Nonetheless, compared to modeling waveform directly, ways of effectively handling higher-dimensional outputs, multiple feature streams and regularization become more important with our approach. In this work, we extend our proposed system to include additional components for predicting F0 and phonetic timings from a musical score with lyrics. These expression-related features are learned together with timbrical features from a single set of natural songs. We compare our method to existing statistical parametric, concatenative, and neural network-based approaches using quantitative metrics as well as listening tests.
A Survey on Platoon-Based Vehicular Cyber-Physical Systems

Vehicles on the road with some common interests can cooperatively form a platoon-based driving pattern, in which a vehicle follows another vehicle and maintains a small and nearly constant distance to the preceding vehicle. It has been proved that, compared with driving individually, such a platoon-based driving pattern can significantly improve road capacity and energy efficiency. Moreover, with the emerging vehicular ad hoc network (VANET), the performance of a platoon in terms of road capacity, safety, energy efficiency, etc., can be further improved. On the other hand, the physical dynamics of vehicles inside the platoon can also affect the performance of a VANET. Such a complex system can be considered a platoon-based vehicular cyber-physical system (VCPS), which has attracted significant attention recently. In this paper, we present a comprehensive survey on a platoon-based VCPS. We first review the related work of a platoon-based VCPS. We then introduce two elementary techniques involved in a platoon-based VCPS, i.e., the vehicular networking architecture and standards, and traffic dynamics, respectively. We further discuss the fundamental issues in a platoon-based VCPS, including vehicle platooning/clustering, cooperative adaptive cruise control, platoon-based vehicular communications, etc., all of which are characterized by the tightly coupled relationship between traffic dynamics and VANET behaviors. Since system verification is critical to VCPS development, we also give an overview of VCPS simulation tools. Finally, we share our view on some open issues that may lead to new research directions.
Diagnosis of Coupled Resonator Bandpass Filters Using VF and Optimization Method

This paper presents a hybrid method combining a vector fitting (VF) and a global optimization for diagnosing coupled resonator bandpass filters. The method can extract coupling matrix from the measured or electromagnetically simulated admittance parameters (Y -parameters) of a narrow band coupled resonator bandpass filter with losses. The optimization method is used to remove the phase shift effects of the measured or the EM simulated Y -parameters caused by the loaded transmission lines at the input/output ports of a filter. VF is applied to determine the complex poles and residues of the Y -parameters without phase shift. The coupling matrix can be extracted (also called the filter diagnosis) by these complex poles and residues. The method can be used to computer-aided tuning (CAT) of a filter in the stage of this filter design and/or product process to accelerate its physical design. Three application examples illustrate the validity of the proposed method.
#mytweet via Instagram: Exploring user behaviour across multiple social networks

We study how users of multiple online social networks (OSNs) employ and share information by studying a common user pool that use six OSNs -- Flickr, Google+, Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical signature of users' sharing behaviour, showing how they exhibit distinct behaviorial patterns on different networks. We also examine cross-sharing (i.e., the act of user broadcasting their activity to multiple OSNs near-simultaneously), a previously-unstudied behaviour and demonstrate how certain OSNs play the roles of originating source and destination sinks.
Comparison of aligned Friedman rank and parametric methods for testing interactions in split-plot designs

Parametric methods are commonly used despite evidence that model assumptions are often violated. Various statistical procedures have been suggested for analyzing data from multiple-group repeated measures (i.e., split-plot) designs when parametric model assumptions are violated (e.g., Akritas and Arnold ( (1994) 336); Brunner and Langer (Biometrical J. 42 (2000) 663)), including the use of Friedman ranks. The e8ects of Friedman ranking on data and the resultant test statistics for single sample repeated measures designs have been examined (e.g., Harwell and Serlin ( (1994) 35;  (1997) 605); Zimmerman and Zumbo ( (1993) 75)). However, there have been fewer investigations concerning Friedman ranks applied to multiple groups of repeated measures data (e.g., Beasley ( (2000) 20); Rasmussen (British  (1989) 91)). We investigate the use of Friedman ranks for testing the interaction in a split-plot design as a robust alternative to parametric procedures. We demonstrated that the presence of a repeated measures main e8ect may reduce the power of interaction tests performed on Friedman ranks. Aligning the data before applying Friedman ranks was shown to produce more statistical power than simply analyzing Friedman ranks. Results from a simulation study showed that aligning the data (i.e., removing main e8ects) before applying Friedman ranks and then performing either a univariate or multivariate test can provide more statistical power than parametric tests if the error distributions are skewed.
Development of a Digitalization Maturity Model for the Manufacturing Sector

An Industry 4.0 framework, showing every possible action towards the complete digitalization of a company both considering strategic and technological aspects, is proposed. The state of the art analysis and re-elaboration allows to coherently integrating many valuable but scattered and narrow contributions into a holistic view covering the following dimensions: value drivers, levers of action, processes and enabling technologies. Based on this framework the consequences of digitalization in shaping a new working environment are analyzed focusing on workforce impact, looking at the modification of both performed tasks and required competences as well as to more generic social consequences (job satisfaction, work-life balance, new forms of employment). To support the application of the proposed roadmap a Digitalization Maturity Model is developed to assess the state of a company journey towards Industry 4.0 considering the following dimensions: Strategy, Processes, Technologies, Products & Services and People.
Characterization of chromosomal abnormalities in prostate cancer cell lines by spectral karyotyping.

Human prostate cancer is characterized by multiple gross chromosome alterations involving several chromosome regions. However, the specific genes involved in the development of prostate tumors are still largely unknown. Here we have studied the chromosome composition of the three established prostate cancer cell lines, LNCaP, PC-3, and DU145, by spectral karyotyping (SKY). SKY analysis showed complex karyotypes for all three cell lines, with 87, 58/113, and 62 chromosomes, respectively. All cell lines were shown to carry structural alterations of chromosomes 1, 2, 4, 6, 10, 15, and 16; however, no recurrent breakpoints were detected. Compared to previously published findings on these cell lines using comparative genomic hybridization, SKY revealed several balanced translocations and pinpointed rearrangement breakpoints. The SKY analysis was validated by fluorescence in situ hybridization using chromosome-specific, as well as locus-specific, probes. Identification of chromosome alterations in these cell lines by SKY may prove to be helpful in attempts to clone the genes involved in prostate cancer tumorigenesis.
Quick response in fast fashion retail: An optimization supply chain responsiveness model

Fast fashion is a highly competitive field of fashion, where: frequent new trends, unexpected customer demand, and a large inventory of items are the main features of the field. From operations perspective, decision makers should demonstrate a high responsiveness flexibility, so that they can be in line with the fast fashion speed. In this context, our main goal in this article is to present a quick response business model: it is an operation mathematical model, that allows international retailer to correct the initial replenishment decisions, according to the market responsiveness and the latest sales data, in order to re-define continuously and dynamically the replenishment policies, in line with the fast fashion market speed. Our mathematical model is an integer linear program, that we resolved using IBM Cplex optimizer. The model parameters are explained; experimental results within several instances are established and presented.
SATIN: a toolkit for informal ink-based applications

Software support for making effective pen-based applications is currently rudimentary. To facilitate the creation of such applications, we have developed SATIN, a Java-based toolkit designed to support the creation of applications that leverage the informal nature of pens. This support includes a scenegraph for manipulating and rendering objects; support for zooming and rotating objects, switching between multiple views of an object, integration of pen input with interpreters, libraries for manipulating ink strokes, widgets optimized for pens, and compatibility with Java's Swing toolkit. SATIN includes a generalized architecture for handling pen input, consisting of recognizers, interpreters, and multi-interpreters. In this paper, we describe the functionality and architecture of SATIN, using two applications built with SATIN as examples.
Critical factors in the performance of novelty search

Novelty search is a recently proposed method for evolutionary computation designed to avoid the problem of deception, in which the fitness function guides the search process away from global optima. Novelty search replaces fitness-based selection with novelty-based selection, where novelty is measured by comparing an individual's behavior to that of the current population and an archive of past novel individuals. Though there is substantial evidence that novelty search can overcome the problem of deception, the critical factors in its performance remain poorly understood. This paper helps to bridge this gap by analyzing how the behavior function, which maps each genotype to a behavior, affects performance. We propose the notion of descendant fitness probability (DFP), which describes how likely a genotype's descendants are to have a certain fitness, and formulate two hypotheses about when changes to the behavior function will improve novelty search's performance, based on the effect of those changes on behavior and DFP. Experiments in both artificial and deceptive maze domains provide substantial empirical support for these hypotheses.