nils-herrmann/clean_scirepeval huggingface dataset:
An Experimental Evaluation of the Best-of-Many Christofides’ Algorithm for the Traveling Salesman Problem

Recent papers on approximation algorithms for the traveling salesman problem (TSP) have given a new variant of the well-known Christofides’ algorithm for the TSP, called the Best-of-Many Christofides’ algorithm. The algorithm involves sampling a spanning tree from the solution to the standard LP relaxation of the TSP, subject to the condition that each edge is sampled with probability at most its value in the LP relaxation. One then runs Christofides’ algorithm on the tree by computing a minimum-cost matching on the odd-degree vertices in the tree, and shortcutting a traversal of the resulting Eulerian graph to a tour. In this paper we perform an experimental evaluation of the Best-of-Many Christofides’ algorithm to see if there are empirical reasons to believe its performance is better than that of Christofides’ algorithm. Furthermore, several different sampling schemes have been proposed; we implement several different schemes to determine which ones might be the most promising for obtaining improved performance guarantees over that of Christofides’ algorithm. In our experiments, all of the implemented methods perform significantly better than Christofides’ algorithm; an algorithm that samples from a maximum entropy distribution over spanning trees seems to be particularly good, though there are others that perform almost as well.
OCR Post-Processing Text Correction using Simulated Annealing (OPTeCA)

This paper describes the system details and results of team “EOF” from the University of Melbourne for the shared task of ALTA 2017, which addresses the problem of text correction for post-processed Optical Character Recognition (OCR) based systems. We developed a two stage system which first detects errors in the given OCR post-processed text with the help of a support vector machine trained using given training dataset, followed by rectifying the errors by employing a confidencebased mechanism using simulated annealing to obtain an optimal correction from a pool of candidate corrections. Our system achieved a F1-score of 32.98% on the private leaderboard1, which is the best score among all the participating systems.
Active Complex Event Processing infrastructure: Monitoring and reacting to event streams

State-of-the-art Complex Event Processing technology (CEP), while effective for pattern matching on event streams, is limited in its capability of reacting in real-time to opportunities and risks detected when monitoring the physical or virtual world. We propose to tackle this problem by embedding active rule support within the CEP engine, henceforth called Active Complex Event Processing technology, or short, ACEP. We design an ACEP infrastructure that integrates the active rule component into the CEP kernel. This not only allows fine-grained but also more efficient rule processing. Based on the infrastructure we develop optimization techniques to improve the responsiveness of our system. We demonstrate the power of ACEP technology by applying it to the development of our real-time healthcare system being deployed in Univ. of Massachusetts Medical School hospital. Through performance experiments using real-world workloads collected from the hospital, we show that our ACEP solution is effective and efficient at supporting business process in event-based systems compared to possible alternatives.
Study on grain loss detecting sensor of combine harvester

A floating raft structure sensor using piezoelectric polyvinylidene fluoride (PVDF) film as sensitive material for grain loss detecting of combine harvester was presented in this paper. Double-layer vibration isolator was proposed to eliminate the vibration influence of combine harvester. Signal processing circuit which composed of a charge amplifier, band-pass filter, envelope detector, absolute value amplifier and square wave generator was constructed to detect the grain impact signal. According to the impact duration of grain on PVDF film, critical frequencies of band-pass filter were determined and the impact responses of filter under different impact durations were numerical simulated. Then, grain detecting experiments were carried out by assembling the sensor on the rear of vibrating cleaning sieve, the results showed that the grain impact can be identified effectively from vibrating noise and the sensor can output a standard square voltage signal while a grain impact is detected.
Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization

Automated multi-document extractive text summarization is a widely studied research problem in the field of natural language understanding. Such extractive mechanisms compute in some form the worthiness of a sentence to be included into the summary. While the conventional approaches rely on human crafted document-independent features to generate a summary, we develop a data-driven novel summary system called HNet, which exploits the various semantic and compositional aspects latent in a sentence to capture document independent features. The network learns sentence representation in a way that, salient sentences are closer in the vector space than non-salient sentences. This semantic and compositional feature vector is then concatenated with the documentdependent features for sentence ranking. Experiments on the DUC benchmark datasets (DUC-2001, DUC-2002 and DUC2004) indicate that our model shows significant performance gain of around 1.5-2 points in terms of ROUGE score compared with the state-of-the-art baselines.
A review of the recent advances in antimicrobial coatings for urinary catheters.

More than 75% of hospital-acquired or nosocomial urinary tract infections are initiated by urinary catheters, which are used during the treatment of 15-25% of hospitalized patients. Among other purposes, urinary catheters are primarily used for draining urine after surgeries and for urinary incontinence. During catheter-associated urinary tract infections, bacteria travel up to the bladder and cause infection. A major cause of catheter-associated urinary tract infection is attributed to the use of non-ideal materials in the fabrication of urinary catheters. Such materials allow for the colonization of microorganisms, leading to bacteriuria and infection, depending on the severity of symptoms. The ideal urinary catheter is made out of materials that are biocompatible, antimicrobial, and antifouling. Although an abundance of research has been conducted over the last forty-five years on the subject, the ideal biomaterial, especially for long-term catheterization of more than a month, has yet to be developed. The aim of this review is to highlight the recent advances (over the past 10years) in developing antimicrobial materials for urinary catheters and to outline future requirements and prospects that guide catheter materials selection and design.



This review article intends to provide an expansive insight into the various antimicrobial agents currently being researched for urinary catheter coatings. According to CDC, approximately 75% of urinary tract infections are caused by urinary catheters and 15-25% of hospitalized patients undergo catheterization. In addition to these alarming statistics, the increasing cost and health related complications associated with catheter associated UTIs make the research for antimicrobial urinary catheter coatings even more pertinent. This review provides a comprehensive summary of the history, the latest progress in development of the coatings and a brief conjecture on what the future entails for each of the antimicrobial agents discussed.
Outdoor Downward-Facing Optical Flow Odometry with Commodity Sensors

Positioning is a key task in most field robotics applications but can be very challenging in GPS-denied or high-slip environments. A common tactic in such cases is to position visually, and we present a visual odometry implementation with the unusual reliance on optical mouse sensors to report vehicle velocity. Using multiple kilometers of data from a lunar rover prototype, we demonstrate that, in conjunction with a moderate-grade inertial measurement unit, such a sensor can provide an integrated pose stream that is at times more accurate than that achievable by wheel odometry and visibly more desirable for perception purposes than that provided by a high-end GPS-INS system. A discussion of the sensor’s limitations and several drift mitigating strategies attempted are presented.
Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization

This paper presents a novel binary monarch butterfly optimization (BMBO) method, intended for addressing the 0–1 knapsack problem (0–1 KP). Two tuples, consisting of real-valued vectors and binary vectors, are used to represent the monarch butterfly individuals in BMBO. Real-valued vectors constitute the search space, whereas binary vectors form the solution space. In other words, monarch butterfly optimization works directly on real-valued vectors, while solutions are represented by binary vectors. Three kinds of individual allocation schemes are tested in order to achieve better performance. Toward revising the infeasible solutions and optimizing the feasible ones, a novel repair operator, based on greedy strategy, is employed. Comprehensive numerical experimentations on three types of 0–1 KP instances are carried out. The comparative study of the BMBO with four state-of-the-art classical algorithms clearly points toward the superiority of the former in terms of search accuracy, convergent capability and stability in solving the 0–1 KP, especially for the high-dimensional instances.
Self-adaptation in software-intensive cyber-physical systems: From system goals to architecture configurations

Design of self-adaptive software-intensive Cyber-Physical Systems (siCPS) operating in dynamic environments is a significant challenge when a sufficient level of dependability is required. This stems partly from the fact that the concerns of selfadaptivity and dependability are to an extent contradictory. In this paper, we introduce IRM-SA (Invariant Refinement Method for Self-Adaptation) – a design method and associated formally grounded model targeting siCPS – that addresses self-adaptivity and supports dependability by providing traceability between system requirements, distinct situations in the environment, and predefined configurations of system architecture. Additionally, IRM-SA allows for architecture self-adaptation at runtime and integrates the mechanism of predictive monitoring that deals with operational uncertainty. As a proof of concept, it was implemented in DEECo, a component framework that is based on dynamic ensembles of components. Furthermore, its feasibility was evaluated in experimental settings assuming decentralized system operation.
Language Generation with Recurrent Generative Adversarial Networks without Pre-training

Generative Adversarial Networks (GANs) have shown great promise recently in image generation. Training GANs for language generation has proven to be more difficult, because of the non-differentiable nature of generating text with recurrent neural networks. Consequently, past work has either resorted to pre-training with maximum-likelihood or used convolutional networks for generation. In this work, we show that recurrent neural networks can be trained to generate text with GANs from scratch using curriculum learning, by slowly teaching the model to generate sequences of increasing and variable length. We empirically show that our approach vastly improves the quality of generated sequences compared to a convolutional baseline.
Lrrk2 pathogenic substitutions in Parkinson's disease

Leucine-rich repeat kinase 2 (LRRK2) mutations have been implicated in autosomal dominant parkinsonism, consistent with typical levodopa-responsive Parkinson's disease. The gene maps to chromosome 12q12 and encodes a large, multifunctional protein. To identify novel LRRK2 mutations, we have sequenced 100 affected probands with family history of parkinsonism. Semiquantitative analysis was also performed in all probands to identify LRRK2 genomic multiplication or deletion. In these kindreds, referred from movement disorder clinics in many parts of Europe, Asia, and North America, parkinsonism segregates as an autosomal dominant trait. All 51 exons of the LRRK2 gene were analyzed and the frequency of all novel sequence variants was assessed within controls. The segregation of mutations with disease has been examined in larger, multiplex families. Our study identified 26 coding variants, including 15 nonsynonymous amino acid substitutions of which three affect the same codon (R1441C, R1441G, and R1441H). Seven of these coding changes seem to be pathogenic, as they segregate with disease and were not identified within controls. No multiplications or deletions were identified.
pARnorama: 360 degree interactive video for augmented reality prototyping

Designing novel and meaningful interactions in the domain of Augmented Reality (AR) requires an efficient and appropriate methodology. A user centered design process requires the construction and evaluation of several prototypes with increasing technical fidelity. Although the main content of the application can already be conveyed with prerendered video, one of the main interactions in AR - the user-selected viewpoint - is only available in a very late stage. We propose the use of panoramic 360° video for scenario based user evaluation, where the user can select his point of view during playback. Initial users report a high degree of immersion in the constructed scenario, even for handheld AR.
SSC 13-WK-7 CUBESAT COMMUNICATION DIRECTION AND CAPABILITIES AT MOREHEAD STATE UNIVERSITY AND NASA GODDARD SPACE FLIGHT CENTER , WALLOPS FLIGHT FACILITY

The Wallops 18-Meter diameter UHF-Band and the Morehead State 21-Meter diameter current S-band and future XBand and UHF-Band CubeSat Groundstations answer a growing need for high data rate from CubeSats over government licensed frequencies. Ten years ago, when CubeSats began, they were nothing more than simple science experiments, typically consisting of a camera and a low data rate radio. The success and wide community support for the National Science Foundation (NSF) CubeSat Program combined with the increasing number of NASA proposals that utilize CubeSats, and other large government organizations that have started funding CubeSats, demonstrates the maturation of the CubeSat platform. The natural gain provided by the large diameter UHF-, Xand SBand Groundstations enables high data rates (e.g. 3.0 Mbit, 300 times the typical 9.6 Kbit for CubeSats over UHF). Government funded CubeSats using amateur radio frequencies may violate the intent of the amateur radio service and it is a violation of National Telecommunications Information Administration (NTIA) rules for a government funded ground station to use amateur radio frequencies to communicate with CubeSats. The NSF has led the charge in finding a suitable government frequency band for CubeSats. Although amateur frequency licensing has historically been easy and fast to obtain, it limits downlink data rate capability due to narrow spectrum bandwidth allocation. In addition to limited bandwidth allocation, using unencrypted and published downlink telemetry data, easily accessible by any receiver, has not satisfied the needs of universities, industry and government agencies. After completing a decade mainly operating at the amateur radio frequency and using inexpensive but unreliable amateur commercial off-the-shelf (COTS) space and ground hardware, the CubeSat community is looking for different CubeSat and ground system communication solutions to support their current and future needs.
(Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research

Time series data is perhaps the most frequently encountered type of data examined by the data mining community. Clustering is perhaps the most frequently used data mining algorithm, being useful in it’s own right as an exploratory technique, and also as a subroutine in more complex data mining algorithms such as rule discovery, indexing, summarization, anomaly detection, and classification. Given these two facts, it is hardly surprising that time series clustering has attracted much attention. The data to be clustered can be in one of two formats: many individual time series, or a single time series, from which individual time series are extracted with a sliding window. Given the recent explosion of interest in streaming data and online algorithms, the latter case has received much attention. In this work we make a surprising claim. Clustering of streaming time series is completely meaningless. More concretely, clusters extracted from streaming time series are forced to obey a certain constraint that is pathologically unlikely to be satisfied by any dataset, and because of this, the clusters extracted by any clustering algorithm are essentially random. While this constraint can be intuitively demonstrated with a simple illustration and is simple to prove, it has never appeared in the literature. An important implication of our findings is that widely used, time series rule discovery algorithms are producing random results!
Toward a student information system for Sebha University, Libya

A student information system provides a simple interface for the easy collation and maintenance of all manner of student information. The creation and management of accurate, up-to-date information regarding students' academic careers is critical students and for the faculties and administration ofSebha University in Libya and for any other educational institution. A student information system deals with all kinds of data from enrollment to graduation, including program of study, attendance record, payment of fees and examination results to name but a few. All these dataneed to be made available through a secure, online interface embedded in auniversity's website. To lay the groundwork for such a system, first we need to build the student database to be integrated with the system. Therefore we proposed and implementedan online web-based system, which we named the student data system (SDS),to collect and correct all student data at Sebha University. The output of the system was evaluated by using a similarity (Euclidean distance) algorithm. The results showed that the new data collected by theSDS can fill the gaps and correct the errors in the old manual data records.
Ordinal Regression Analysis : Using Generalized Ordinal Logistic Regression Models to Estimate Educational Data

The proportional odds (PO) assumption for ordinal regression analysis is often violated because it is strongly affected by sample size and the number of covariate patterns. To address this issue, the partial proportional odds (PPO) model and the generalized ordinal logit model were developed. However, these models are not typically used in research. One likely reason for this is the restriction of current statistical software packages: SPSS cannot perform the generalized ordinal logit model analysis and SAS requires data restructuring. This article illustrates the use of generalized ordinal logistic regression models to predict mathematics proficiency levels using Stata and compares the results from fitting PO models and generalized ordinal logistic regression models.
Sudhausia aristotokia n. gen., n. sp. and S. crassa n. gen., n. sp. (Nematoda: Diplogastridae): viviparous new species with precocious gonad development

Sudhausia aristotokia n. gen., n. sp. and S. crassa n. gen., n. sp. (Nematoda: Diplogastridae): viviparous new species with precocious gonad development Matthias HERRMANN 1, Erik J. RAGSDALE 1, Natsumi KANZAKI 2 and Ralf  Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan
Ovarian Cancer Metastatic to the Breast Presenting as Inflammatory Breast Cancer: A Case Report and Literature Review

Primary ovarian carcinoma with metastasis to the breast is rare, with only 39 cases reported in the current literature. Ovarian metastasis to the breast presenting as inflammatory breast carcinoma is even more infrequent, with only 6 cases reported. We present a patient who developed metastatic inflammatory cancer of the breast from a stage IIIC papillary serous ovarian adenocarcinoma approximately 1 year after the original diagnosis. Pathologic analysis confirmed the origin of the tumor: a high-grade adenocarcinoma morphologically similar to the previously diagnosed ovarian cancer. In addition, the tumor was strongly positive on immunohistochemistry for CA-125, identical to the ovarian primary. The patient died of diffuse metastasis 5 months after the breast tumor was noted. Although ovarian metastasis to the breast presenting as inflammatory breast cancer is rare, it should be included in the differential diagnosis for any patient with a personal history of ovarian cancer. Accurate differentiation is necessary because treatment differs significantly for patients with ovarian metastasis to the breast, as compared with patients with primary inflammatory breast cancer. Ovarian metastasis to the breast confers a poor prognosis: patient survival ranged from 3 to 18 months, with a median survival of 6 months after the diagnosis of the breast metastasis.
First report of Trichodorus primitivus, T. sparsus and T. viruliferus (Nematoda: Trichodoridae) from the Czech Republic

A survey has been carried out to study the occurrence and distribution of Trichodorus primitivus, T. sparsus and T. viruliferus in the Czech Republic under the rhizosphere of orchards, forests, vineyards and strawberry. Total 208 sites were surveyed and only 29 sites were found positive for these species. All three species are reported in the Czech Republic for the first time.
Antioxidant activity of Moringa oleifera seed extracts

The present research evaluates the phytochemical and antioxidant activity of Moringa oleifera Lam. seed kernel grown in Bangladesh. M. oleifera seed kernel was extracted with methanol, acetone and water individually. The phytochemical content was evaluated through the determination of total phenolic, total flavonoid and total tannin contents. In vitro antioxidant capacity was determined following four complementary methods: DPPH (2,2-diphenyl-1-picrylhydrazyl, ABTS{2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid)}, and NO (nitric oxide) free radical scavenging and reducing power tests. The total phenolic contents 60.99 ± 0.153, 30.78 ± 0.101 and 90.97 ± 0.134 mg gallic acid eq./g dry extract, total flavonoids contents 10.13 ± 0.171, 13.32 ± 0.101 and 221.76 ± 0.221 mg quercetin eq./g and total tannins contents 97.10 ± 0.153, 73.91 ± 0.107 and 21.74 ± 0.086 mg gallic acid eq./g dry extract were found to be present in methanol, acetone and water extracts respectively. Among the three extracts, the water extract exhibited significant activities for scavenging DPPH, ABTS and NO free radicals with EC50 values 36.89 ± 0.154, 13.20 ± 0.049 and 217.95 ± 0.327 μg/mL respectively against the standard antioxidant compound ascorbic acid and butylated hydroxy anisole. The results of this research revealed that among the three extracts of M. oleifera seed kernel the water extract exhibited significant free radical scavenging activity. Reducing power, total phenolic and total flavonoid contents were also observed to be significant for the water extract. So in contrast of all the results of this study it can be concluded that among the three extracts (methanol, acetone and water) the water extract possessed potent antioxidant activity which support the use of M. oleifera seed kernel as natural antioxidant.