nils-herrmann/clean_scirepeval huggingface dataset:
The effects of hotel performance characteristics on customer online ratings

Due to rapid development and consumer adoption of technology, the hospitality industry has dramatically increased online-based consumer reviews for a variety of hospitality services such as those found in foodservice and lodging segments (e.g., Litvin, Goldsmith, & Pan, 2008). This study focused on the lodging industry, and investigated the relationship between characteristics of lodging industry performance and user-generated customer reviews on a popular website, TripAdivsor.com, by analyzing five of the top U.S. lodging US markets. Data from these cities included: (a) the top 100 highest ranked hotel ratings based on online customer reviews; (b) total number of hotels; (c) the average daily rate (ADR) for hotels; and (d) the occupancy percentage of hotels in a selected city. Customer hotel ratings were collected from TripAdvisor.com and city characteristics were collected from Smith Travel Research (STR), a leader in hotel industry data collection and analytics. The results of this study revealed that customer ratings differ by location, which can impact the destination image in the mind of customers and influence their destination travel choices. Results also revealed that ADR, occupancy rate, and the number of total number of hotel properties could be used to predict customer ratings on a lodging property or an overall average rating for a market. The study concludes with a discussion of the findings, implications for practice, and recommendations for future research.
Recurrent neural network language models for keyword search

Recurrent neural network language models (RNNLMs) have becoming increasingly popular in many applications such as automatic speech recognition (ASR). Significant performance improvements in both perplexity and word error rate over standard n-gram LMs have been widely reported on ASR tasks. In contrast, published research on using RNNLMs for keyword search systems has been relatively limited. In this paper the application of RNNLMs for the IARPA Babel keyword search task is investigated. In order to supplement the limited acoustic transcription data, large amounts of web texts are also used in large vocabulary design and LM training. Various training criteria were then explored to improved RNNLMs' efficiency in both training and evaluation. Significant and consistent improvements on both keyword search and ASR tasks were obtained across all languages.
A Brief Survey on 5G Wireless Mobile Network

The new upcoming technology of the fifth generation wireless mobile network is advertised as lightning speed internet, everywhere, for everything, for everyone in the nearest future. There are a lot of efforts and research carrying on many aspects, e.g. millimetre wave (mmW) radio transmission, massive multiple input and multiple output (Massive-MIMO) new antenna technology, the promising technique of SDN architecture, Internet of Thing (IoT) and many more. In this brief survey, we highlight some of the most recent developments towards the 5G mobile network. Keywords—5G; millimetre wave (mmW); Internet of Thing (IoT); SDN; massive multiple input and multiple output (Massive-
Sclerochoroidal calcification: clinical manifestations and systemic associations.

Sclerochoroidal calcification is an unusual ocular condition that is believed to be idiopathic in most cases.



To describe the clinical manifestations of sclerochoroidal calcification and to investigate its possible systemic associations.



This noncomparative consecutive case series included patients diagnosed as having sclerochoroidal calcification based on clinical characteristics and diagnostic test findings. We analyzed the demographic, systemic, and ocular features of 27 such patients. Systemic evaluation included tests for calcium-phosphorus metabolism in 19 patients and renal tubular hypokalemic metabolic alkalosis syndromes (Bartter or Gitelman syndrome) in 13.



All the patients were asymptomatic older (mean age, 70 years) white individuals, incidentally noted as having a choroidal lesion on routine examination. Among 38 eyes, the main referral diagnoses were choroidal metastasis in 10 eyes (26%), choroidal melanoma in 8 (21%), and choroidal nevus in 4 (11%). Sixteen patients (59%) had unilateral clinical findings, while 11 (41%) had bilateral. The Snellen visual acuity was 20/50 or better in 37 eyes (97%). Cogan scleral plaque was visible anterior to the insertion of horizontal rectus muscles in 10 eyes (26%). Among 77 foci, there were a mean of 2 foci of sclerochoroidal calcification in each eye, 41 yellow (53%), 32 yellow-white (42%), 2 white (3%), and 2 orange (3%), measuring a mean 2.6 mm in diameter and 1.1 mm in thickness. The most common locations were postequatorial in 45 (58%), along the temporal vascular arcades in 30 (39%), and in the superotemporal quadrant in 43 (56%). A-scan and B-scan ultrasonography revealed dense echoes compatible with calcium, with orbital shadowing. All the lesions remained stable in size and configuration during a mean follow-up of 38 months. One patient developed a choroidal neovascular membrane over the area of sclerochoroidal calcification. Investigations for abnormal calcium-phosphorus metabolism in 19 patients revealed primary hyperparathyroidism in 1 patient (5%). Clinical and biochemical evaluation of 13 patients demonstrated hypomagnesemia in 6 (46%). Four patients (31%) met the criteria for the diagnosis of Gitelman syndrome.



Sclerochoroidal calcification usually manifests as multiple discrete yellow placoid lesions in the midperipheral fundus of asymptomatic older white individuals. Although most cases may be idiopathic in nature, some patients may have underlying systemic disorders involving abnormal calcium-phosphorus metabolism or renal tubular hypokalemic metabolic alkalosis syndromes. All patients with sclerochoroidal calcification should be tested for these treatable systemic associations.
DISCO: Distributed SDN controllers in a multi-domain environment

Software-Defined Networking (SDN) is now envisioned for Wide Area Networks (WAN) and deployed constrained networks. Such networks require a resilient, scalable and easily extensible SDN control plane. In this paper, we propose DISCO, a DIstributed SDN COntrol plane able to cope with the distributed and heterogeneous nature of modern overlay networks and deployed networks. A DISCO controller manages its own network domain, communicates with other DISCO controllers to provide end-to-end network services and share aggregated network-wide information. This east-west communication is based on a lightweight and highly manageable control channel which can self-adapt to network conditions.
Rib approximation without intercostal nerve compression reduces post-thoracotomy pain: a prospective randomized study.

One of the most important considerations in the care of thoracic surgery patients is the control of pain, which leads to increased morbidity and relevant mortality.



Between February and May 2009, 60 patients undergoing full muscle-sparing posterior minithoracotomy were prospectively randomized into two groups, according to the thoracotomy closure techniques. In the first group (group A), two holes were drilled into the sixth rib using a hand perforator, and sutures were passed through the holes in the sixth rib and were circled from the upper edge of the fifth rib, thereby compressing the intercostal nerve underneath the fifth rib. In the second group (group B), the intercostal muscle underneath the fifth rib was partially dissected along with the intercostal nerve, corresponding to the holes on the sixth rib. Two 1/0 polyglactin (Vicyrl) sutures were passed through the holes in the sixth rib and above the intercostal nerve.



There were 30 patients in each group. The visual analog score, observer verbal ranking scale (OVRS) scores for pain, and Ramsay sedation scores were used to follow-up on postoperative analgesia and sedation. The von Frey hair test was used to evaluate hyperalgesia of the patients. The patients in group B had lower visual analog scores at rest and during coughing. The patients in group B had lower OVRS scores than group A patients. The groups were not statistically different in terms of the Ramsay sedation scores and von Frey hair tests.



Thoracotomy closure by a technique that avoids intercostal nerve compression significantly decreases post-thoracotomy pain.
The type I-E CRISPR-Cas system Biology and applications of an adaptive immune system in bacteria

Amlinger, L. 2017. The type I-E CRISPR-Cas system. Biology and applications of an adaptive immune system in bacteria. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1466. 61  Acta Universitatis Upsaliensis. ISBN 978-91-554-9787-3. CRISPR-Cas systems are adaptive immune systems in bacteria and archaea, consisting of a clustered regularly interspaced short palindromic repeats (CRISPR) array and CRISPR associated (Cas) proteins. In this work, the type I-E CRISPR-Cas system of Escherichia coli was studied. CRISPR-Cas immunity is divided into three stages. In the first stage, adaptation, Cas1 and Cas2 store memory of invaders in the CRISPR array as short intervening sequences, called spacers. During the expression stage, the array is transcribed, and subsequently processed into small CRISPR RNAs (crRNA), each consisting of one spacer and one repeat. The crRNAs are bound by the Cascade multi-protein complex. During the interference step, Cascade searches for DNA molecules complementary to the crRNA spacer. When a match is found, the target DNA is degraded by the recruited Cas3 nuclease. Host factors required for integration of new spacers into the CRISPR array were first investigated. Deleting recD, involved in DNA repair, abolished memory formation by reducing the concentration of the Cas1-Cas2 expression plasmid, leading to decreased amounts of Cas1 to levels likely insufficient for spacer integration. Deletion of RecD has an indirect effect on adaptation. To facilitate detection of adaptation, a sensitive fluorescent reporter was developed where an out-of-frame yfp reporter gene is moved into frame when a new spacer is integrated, enabling fluorescent detection of adaptation. Integration can be detected in single cells by a variety of fluorescence-based methods. A second aspect of this thesis aimed at investigating spacer elements affecting target interference. Spacers with predicted secondary structures in the crRNA impaired the ability of the CRISPR-Cas system to prevent transformation of targeted plasmids. Lastly, in absence of Cas3, Cascade was successfully used to inhibit transcription of specific genes by preventing RNA polymerase access to the promoter. The CRISPR-Cas field has seen rapid development since the first demonstration of immunity almost ten years ago. However, much research remains to fully understand these interesting adaptive immune systems and the research presented here increases our understanding of the type I-E CRISPR-Cas system.
Modeling Split-Ring Resonator (SRR) and Complementary Split-Ring Resonator (CSRR) Loaded Transmission Lines Exhibiting Cross-Polarization Effects

The purpose of this letter is to understand and model the electromagnetic properties of transmission lines loaded with split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) arbitrarily oriented. It is shown that if the slits of these resonators are aligned in a nonorthogonal direction to the line axis, cross-polarization effects arise. Namely, the particles (SRRs or CSRRs) are excited through both magnetic and electric coupling. It is pointed out that the previously reported lumped element equivalent circuit models of SRR- and CSRR-loaded lines (where cross polarization is not considered) are valid as long as the slits are orthogonally oriented to the line axis, and new models that include cross polarization are presented and discussed. The validity and accuracy of the models is demonstrated through parameter extraction and comparison to full-wave electromagnetic simulations and measurements.
Real-time flood water level monitoring system with SMS notification

Metropolitan Manila, Philippines is one of the regions at high risk from flooding. Pandacan, is one of the districts in the city of Manila, located at the south of Pasig River which is prone to flooding. The purpose of this project is to provide a standalone flood water level monitoring system for the community in Kahilom Street Pandacan, Manila. The system is constructed through the use of Arduino Uno, GSM shield and sensors that will be powered by a solar panel with generator. The early warning device will be the three LED that is mounted to a PVC pipe and then the system will send an SMS notification to the people in the community. The functionality of the system was tested by the simulation of flooding. The results provided that the objectives of the design satisfied the needs of the client.
A Machine Learning Based Forwarding Algorithm Over Cognitive Radios in Wireless Mesh Networks

Wireless Mesh Networks improve their capacities by equipping mesh nodes with multi-radios tuned to non-overlapping channels. Hence the data forwarding between two nodes has multiple selections of links and the bandwidth between the pair of nodes varies dynamically. Under this condition, a mesh node adopts machine learning mechanisms to choose the possible best next hop which has maximum bandwidth when it intends to forward data. In this paper, we present a machine learning based forwarding algorithm to let a forwarding node dynamically select the next hop with highest potential bandwidth capacity to resume communication based on learning algorithm. Key to this strategy is that a node only maintains three past status, and then it is able to learn and predict the potential bandwidth capacities of its links. Then, the node selects the next hop with potential maximal link bandwidth. Moreover, a geometrical based algorithm is developed to let the source node figure out the forwarding region in order to avoid flooding. Simulations demonstrate that our approach significantly speeds up the transmission and outperforms other peer algorithms.
Inducing Frame Semantic Verb Classes from WordNet and LDOCE

This paper presents SemFrame, a system that induces frame semantic verb classes from WordNet and LDOCE. Semantic frames are thought to have significant potential in resolving the paraphrase problem challenging many languagebased applications. When compared to the handcrafted FrameNet, SemFrame achieves its best recall-precision balance with 83.2% recall (based on SemFrame's coverage of FrameNet frames) and 73.8% precision (based on SemFrame verbs’ semantic relatedness to frame-evoking verbs). The next best performing semantic verb classes achieve 56.9% recall and 55.0% precision.
Mechanism design for total quality management: Using the bootstrap algorithm for changing the control game

When starting the software process improvement (SPI) journey, there are many SPI standards to select from. Selecting good SPI standards can be a technical problem, from a software engineering point of view, but it can also be a political problem, some standards fitting more with internal political agendas than others. As it is well-known that SPI without management commitment can have disastrous effects on SPI, so can also be the consequence of selecting standards that are technically unfit. The dilemma on how to select SPI standards provides a picture of SPI as a political game played out between managers, software engineers and SPI people. Starting with SPI from the viewpoint of control theory, the paper identifies different conflict situations within the control theory framework, and suggests using game theory and drama theory for finding optimal control strategies. Drama theory is further explored through a SPI case study that illustrates how SPI standards stabilize in spite of conflicts and social disaster. The contribution of the paper consists of introducing the concept of ‘evolutionary drama theory’ (derived from evolutionary game theory, EGT) as a tool for describing and analysing how an artefact like a SPI standard evolves towards equilibrium (evolutionary stable strategy, ESS) by looking at repeated dramas where equilibriums may not necessarily be found or, if found, may not necessarily fit with the ESS.
Maximizing system performance by balancing computation loads in LSTM accelerators

The LSTM is a popular neural network model for modeling or analyzing the time-varying data. The main operation of LSTM is a matrix-vector multiplication and it becomes sparse (spMxV) due to the widely-accepted weight pruning in deep learning. This paper presents a new sparse matrix format, named CBSR, to maximize the inference speed of the LSTM accelerator. In the CBSR format, speed-up is achieved by balancing out the computation loads over PEs. Along with the new format, we present a simple network transformation to completely remove the hardware overhead incurred when using the CBSR format. Also, the detailed analysis on the impact of network size or the number of PEs is performed, which lacks in the prior work. The simulation results show 16∼38% improvement in the system performance compared to the well-known CSC/CSR format. The power analysis is also performed in 65nm CMOS technology to show 9∼22% energy savings.
Crime prediction and forecasting in Tamilnadu using clustering approaches

Crime is one of the most predominant and alarming aspects in our society and its prevention is a vital task. Crime analysis is a systematic way of detecting and investigating patterns and trends in crime. In this work, we use various clustering approaches of data mining to analyse the crime data of Tamilnadu. The crime data is extracted from National Crime Records Bureau (NCRB) of India. It consists of crime information about six cities namely Chennai, Coimbatore, Salem, Madurai, Thirunelvelli and Thiruchirapalli from the year 2000–2014 with 1760 instances and 9 attributes to represent the instances. K-Means clustering, Agglomerative clustering and Density Based Spatial Clustering with Noise (DBSCAN) algorithms are used to cluster crime activities based on some predefined cases and the results of these clustering are compared to find the best suitable clustering algorithm for crime detection. The result of K-Means clustering algorithm is visualized using Google Map for interactive and easy understanding. The K-Nearest Neighbor (KNN) classification is used for crime prediction. The performance of each clustering algorithms are evaluated using the metrics such as precision, recall and F-measure, and the results are compared. This work helps the law enforcement agencies to predict and detect crimes in Tamilnadu with improved accuracy and thus reduces the crime rate.
Reconstructing building mass models from UAV images

We present an automatic reconstruction pipeline for large scale urban scenes from aerial images captured by a camera mounted on an unmanned aerial vehicle. Using state-of-the-art Structure from Motion and Multi-View Stereo algorithms, we first generate a dense point cloud from the aerial images. Based on the statistical analysis of the footprint grid of the buildings, the point cloud is classified into different categories (i.e., buildings, ground, trees, and others). Roof structures are extracted for each individual building using Markov random field optimization. Then, a contour refinement algorithm based on pivot point detection is utilized to refine the contour of patches. Finally, polygonal mesh models are extracted from the refined contours. Experiments on various scenes as well as comparisons with state-of-the-art reconstruction methods demonstrate the effectiveness and robustness of the proposed method.
Mining Students' Data for Prediction Performance

A country's growth is strongly measured by quality of its education system. Education sector, across the globe has witnessed sea change in its functioning. Today it is recognized as an industry and like any other industry it is facing challenges, the major challenges of higher education being decrease in students' success rate and their leaving a course without completion. An early prediction of students' failure may help the management provide timely counseling as well coaching to increase success rate and student retention. We use different classification techniques to build performance prediction model based on students' social integration, academic integration, and various emotional skills which have not been considered so far. Two algorithms J48 (Implementation of C4.5) and Random Tree have been applied to the records of MCA students of colleges affiliated to Guru Gobind Singh Indraprastha University to predict third semester performance. Random Tree is found to be more accurate in predicting performance than J48 algorithm.
An overview of the configuration and manipulation of soft robotics for on-orbit servicing

Soft robots refer to robots that are softer and more flexible when compared with conventional rigid-bodied robots. Soft robots are adapted to unstructured environments due to their flexibility, deformability and energy-absorbing properties. Thus, they have tremendous application prospects in on-orbit servicing (OOS). This study discusses the configuration and manipulation of soft robotics. Usually, learning from living beings is used to develop the configurations of most soft robots. In this study, typical soft robots are introduced based on what they mimic. The discussion of manipulation is divided into two parts, namely actuation and control. The study also involves describing and comparing several types of actuations. Studies on the control of soft robots are also reviewed. In this study, potential application of soft robotics for on-orbit servicing is analyzed. A hybrid configuration and manipulation of space soft robots for future research are proposed based on the current development of soft robotics, and some challenges are discussed.
Understanding brain networks and brain organization.

What is the relationship between brain and behavior? The answer to this question necessitates characterizing the mapping between structure and function. The aim of this paper is to discuss broad issues surrounding the link between structure and function in the brain that will motivate a network perspective to understanding this question. However, as others in the past, I argue that a network perspective should supplant the common strategy of understanding the brain in terms of individual regions. Whereas this perspective is needed for a fuller characterization of the mind-brain, it should not be viewed as panacea. For one, the challenges posed by the many-to-many mapping between regions and functions is not dissolved by the network perspective. Although the problem is ameliorated, one should not anticipate a one-to-one mapping when the network approach is adopted. Furthermore, decomposition of the brain network in terms of meaningful clusters of regions, such as the ones generated by community-finding algorithms, does not by itself reveal "true" subnetworks. Given the hierarchical and multi-relational relationship between regions, multiple decompositions will offer different "slices" of a broader landscape of networks within the brain. Finally, I described how the function of brain regions can be characterized in a multidimensional manner via the idea of diversity profiles. The concept can also be used to describe the way different brain regions participate in networks.