nils-herrmann/clean_scirepeval huggingface dataset:
Initial Access in 5G mmWave Cellular Networks

The massive amounts of bandwidth available at millimeter-wave frequencies (above 10 GHz) have the potential to greatly increase the capacity of fifth generation cellular wireless systems. However, to overcome the high isotropic propagation loss experienced at these frequencies, highly directional antennas will be required at both the base station and the mobile terminal to achieve sufficient link budget in wide area networks. This reliance on directionality has important implications for control layer procedures. In particular, initial access can be significantly delayed due to the need for the base station and the user to find the proper alignment for directional transmission and reception. This article provides a survey of several recently proposed techniques for this purpose. A coverage and delay analysis is performed to compare various techniques including exhaustive and iterative search, and context-information-based algorithms. We show that the best strategy depends on the target SNR regime, and provide guidelines to characterize the optimal choice as a function of the system parameters.
Improving Vector Space Word Representations Using Multilingual Correlation

The distributional hypothesis of Harris (1954), according to which the meaning of words is evidenced by the contexts they occur in, has motivated several effective techniques for obtaining vector space semantic representations of words using unannotated text corpora. This paper argues that lexico-semantic content should additionally be invariant across languages and proposes a simple technique based on canonical correlation analysis (CCA) for incorporating multilingual evidence into vectors generated monolingually. We evaluate the resulting word representations on standard lexical semantic evaluation tasks and show that our method produces substantially better semantic representations than monolingual techniques.
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject-verb dependencies. We probe the architecture’s grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1% errors), but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language-modeling setting. We conclude that LSTMs can capture a non-trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax-sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured.
Design and Characterization of High-Voltage 4H-SiC p-IGBTs

High-voltage p-channel 4H-SiC insulated gate bipolar transistors (IGBTs) have been fabricated and characterized. The devices have a forward voltage drop of 7.2 V at 100 A/cm2 and a -16 V gate bias at 25degC, corresponding to a specific on-resistance of 72 mOmega ldr cm2 and a differential on-resistance of 26 mmOmega ldr cm2. Hole mobility of 12 cm2/V ldr s in the inversion channel with a threshold voltage of -6 V was achieved by optimizing the n+ well doping profile and gate oxidation process. A novel current enhancement layer was adopted to reduce the JFET resistance and enhance conductivity modulation by improving hole current spreading and suppressing the electron current conduction through the top n-p-n transistor. Inductive switching results have shown that the p-IGBT exhibited a turn-off time of ~1 mus and a turn-off energy loss of 12 m J at 4-kV dc-link voltage and 6-A load current at 25degC. The turn-off trajectory from the measured inductive load switching waveforms and numerical simulations shows that the p-IGBT had a near-square reverse bias safe operating area. Numerical simulations have been conducted to achieve an improved tradeoff between forward voltage drop and switching off energy by investigating the effects of drift layer lifetime and p-buffer layer parameters. The advantages of SiC p-IGBTs, such as the potential of very low ON-state resistance, slightly positive temperature coefficient, high switching speed, small switching losses, and large safe operating area, make them suitable and attractive for high-power high-frequency applications.
A Nonparametric Approach for Mild Cognitive Impairment to AD Conversion Prediction: Results on Longitudinal Data

The goal of this study is to introduce a nonparametric technique for predicting conversion from Mild Cognitive impairment (MCI)-to-Alzheimer's disease (AD). Progression of a slowly progressing disease such as AD benefits from the use of longitudinal data; however, research till now is limited due to the insufficient patient data and short follow-up time. A small dataset size invalidates the estimation of underlying disease progression model; hence, a supervised nonparametric method is proposed. While depicting a real-world setting, longitudinal data of three years are employed for training, whereas only the baseline visit's data is used for validation. The train set is preprocessed for extraction of two dense clusters representing the subjects who remain stable at MCI or progress to AD after three years of the baseline visit. Similarity between these clusters and the test point is calculated in Euclidean space. Multiple features from two modalities of biomarkers, i.e., neuropsychological measures (NM) and structural magnetic resonance imaging (MRI) morphometry are also analyzed. Due to the limited MCI dataset size (NM: 145, MRI: 52, NM+MRI: 29), leave-one-out cross validation setup is employed for performance evaluation. The algorithm performance is noted for both unimodal case and bimodal cases. Superior performance (accuracy: 89.66%, sensitivity: 87.50%, specificity: 92.31%, precision: 93.33%) is delivered by multivariate predictors. Three notable conclusions of this study are: 1) Longitudinal data are more powerful than the temporal data, 2) MRI is a better predictor of MCI-to-AD conversion than NM, and 3) multivariate predictors outperform single predictor models
EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering

A critical task for question answering is the final answer selection stage, which has to combine multiple signals available about each answer candidate. This paper proposes EviNets: a novel neural network architecture for factoid question answering. EviNets scores candidate answer entities by combining the available supporting evidence, e.g., structured knowledge bases and unstructured text documents. EviNets represents each piece of evidence with a dense embeddings vector, scores their relevance to the question, and aggregates the support for each candidate to predict their final scores. Each of the components is generic and allows plugging in a variety of models for semantic similarity scoring and information aggregation. We demonstrate the effectiveness of EviNets in experiments on the existing TREC QA and WikiMovies benchmarks, and on the new Yahoo! Answers dataset introduced in this paper. EviNets can be extended to other information types and could facilitate future work on combining evidence signals for joint reasoning in question answering.
Scale-invariant learning and convolutional networks

The conventional classification schemes — notably multinomial logistic regression — used in conjunction with convolutional networks (convnets) are classical in statistics, designed without consideration for the usual coupling with convnets, stochastic gradient descent, and backpropagation. In the specific application to supervised learning for convnets, a simple scale-invariant classification stage turns out to be more robust than multinomial logistic regression, appears to result in slightly lower errors on several standard test sets, has similar computational costs, and features precise control over the actual rate of learning. “Scale-invariant” means that multiplying the input values by any nonzero scalar leaves the output unchanged.
Provisioning servers in the application tier for e-commerce systems

Server providers that support e-commerce applications as a service for multiple e-commerce Web sites traditionally use a tiered server architecture. This architecture includes an application tier to process requests for dynamically generated content. How this tier is provisioned can significantly impact a provider's profit margin. In this article we study methods to provision servers in the application serving tier that increase a server provider's profits. First, we examine actual traces of request arrivals to the application tier of an e-commerce site, and show that the arrival process is effectively Poisson. Next, we construct an optimization problem in the context of a set of application servers modeled as M/G/1/PS queueing systems, and derive three simple methods that approximate the allocation that maximizes profits. Simulation results demonstrate that our approximation methods achieve profits that are close to optimal, and are significantly higher than those achieved via simple heuristics.
The identification of Noteworthy Hotel Reviews for Hotel Management

The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features.
How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models

Generative conversational systems are attracting increasing attention in natural language processing (NLP). Recently, researchers have noticed the importance of context information in dialog processing, and built various models to utilize context. However, there is no systematic comparison to analyze how to use context effectively. In this paper, we conduct an empirical study to compare various models and investigate the effect of context information in dialog systems. We also propose a variant that explicitly weights context vectors by context-query relevance, outperforming the other baselines.
Climate change threats to plant diversity in Europe.

Climate change has already triggered species distribution shifts in many parts of the world. Increasing impacts are expected for the future, yet few studies have aimed for a general understanding of the regional basis for species vulnerability. We projected late 21st century distributions for 1,350 European plants species under seven climate change scenarios. Application of the International Union for Conservation of Nature and Natural Resources Red List criteria to our projections shows that many European plant species could become severely threatened. More than half of the species we studied could be vulnerable or threatened by 2080. Expected species loss and turnover per pixel proved to be highly variable across scenarios (27-42% and 45-63% respectively, averaged over Europe) and across regions (2.5-86% and 17-86%, averaged over scenarios). Modeled species loss and turnover were found to depend strongly on the degree of change in just two climate variables describing temperature and moisture conditions. Despite the coarse scale of the analysis, species from mountains could be seen to be disproportionably sensitive to climate change (approximately 60% species loss). The boreal region was projected to lose few species, although gaining many others from immigration. The greatest changes are expected in the transition between the Mediterranean and Euro-Siberian regions. We found that risks of extinction for European plants may be large, even in moderate scenarios of climate change and despite inter-model variability.
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.
The Impact of E-Media on Customer Purchase Intention

-In this research paper, authors investigated the social media (e-discussion, websites, online chat, email etc) parameters that have effect over the customers buying decisions. The research focused on the development of research model to test the impact of social media on the customer purchase intention. The literature review done to explore the work done on social media. The authors identify the problem and defined the objectives of the studies. In order to achieve them, a research model is proposed that followed by the development of research hypotheses to testify the model.
Design of network security projects using honeypots

Honeypots are closely monitored decoys that are employed in a network to study the trail of hackers and to alert network administrators of a possible intrusion. Using honeypots provides a cost-effective solution to increase the security posture of an organization. Even though it is not a panacea for security breaches, it is useful as a tool for network forensics and intrusion detection. Nowadays, they are also being extensively used by the research community to study issues in network security, such as Internet worms, spam control, DoS attacks, etc. In this paper, we advocate the use of honeypots as an effective educational tool to study issues in network security. We support this claim by demonstrating a set of projects that we have carried out in a network, which we have deployed specifically for running distributed computer security projects. The design of our projects tackles the challenges in installing a honeypot in academic institution, by not intruding on the campus network while providing secure access to the Internet. In addition to a classification of honeypots, we present a framework for designing assignments/projects for network security courses. The three sample honeypot projects discussed in this paper are presented as examples of the framework.
POCO-framework for Pareto-optimal resilient controller placement in SDN-based core networks

Recently, Software Defined Networking (SDN) has gained a lot of attention, even for the use in core communication networks. When deploying SDN in large core networks, the number and location of controllers must be carefully planned. A first study on this topic by Heller et al.  was followed by more detailed publications that included resilience or dynamic controller provisioning. In our previous work , we provide an overview over related work and include different resilience issues in the controller placement process. We argued that it is not sufficient to look only at node-to-controller latencies but a controller placement should also fulfill certain resilience constraints especially for the control plane.
Implementation of Vector Field Histogram based obstacle avoidance wheeled robot

This paper describes the navigation of mobile robots in a dynamic environment with obstacles, providing guaranteed reach to the target. Ultrasonic Sensor mounted on the Mobile Robot is used for obstacle detection. VFH (Vector Field Histogram method is used to avoid obstacles and make the navigation of the robot smoother. Spartan 3 FPGA is used for controlling the robot. The FPGA is used because it can operate with low power and it can provide parallelism operation, so the robot can do its work in a faster manner. Experimental results show the robot navigation and obstacle detection implemented in the FPGA processor.
Safety impacts of bicycle infrastructure: A critical review.

This paper takes a critical look at the present state of bicycle infrastructure treatment safety research, highlighting data needs. Safety literature relating to 22 bicycle treatments is examined, including findings, study methodologies, and data sources used in the studies. Some preliminary conclusions related to research efficacy are drawn from the available data and findings in the research.



While the current body of bicycle safety literature points toward some defensible conclusions regarding the safety and effectiveness of certain bicycle treatments, such as bike lanes and removal of on-street parking, the vast majority treatments are still in need of rigorous research. Fundamental questions arise regarding appropriate exposure measures, crash measures, and crash data sources.



This research will aid transportation departments with regard to decisions about bicycle infrastructure and guide future research efforts toward understanding safety impacts of bicycle infrastructure.
Exendin-4 Enhances Motor Function Recovery via Promotion of Autophagy and Inhibition of Neuronal Apoptosis After Spinal Cord Injury in Rats

Autophagy occurs prior to apoptosis and plays an important role in cell death regulation during spinal cord injury (SCI). This study aimed to determine the effects and potential mechanism of the glucagon-like peptide-1 (GLP-1) agonist extendin-4 (Ex-4) in SCI. Seventy-two male Sprague Dawley rats were randomly assigned to sham, SCI, 2.5 μg Ex-4, and 10 μg Ex-4 groups. To induce SCI, a 10-g iron rod was dropped from a 20-mm height to the spinal cord surface. Ex-4 was administered via intraperitoneal injection immediately after surgery. Motor function evaluation with the Basso Beattie Bresnahan (BBB) locomotor rating scale indicated significantly increased scores (p < 0.01) in the Ex-4-treated groups, especially 10 μg, which demonstrated the neuroprotective effect of Ex-4 after SCI. The light chain 3-II (LC3-II) and Beclin 1 protein expression determined via western blot and the number of autophagy-positive neurons via immunofluorescence double labeling were increased by Ex-4, which supports promotion of autophagy (p < 0.01). The caspase-3 protein level and neuronal apoptosis via transferase UTP nick end labeling (TUNEL)/NeuN/DAPI double labeling were significantly reduced in the Ex-4-treated groups, which indicates anti-apoptotic effects (p < 0.01). Finally, histological assessment via Nissl staining demonstrated the Ex-4 groups exhibited a significantly greater number of surviving neurons and less cavity (p < 0.01). To our knowledge, this is the first study to indicate that Ex-4 significantly enhances motor function in rats after SCI, and these effects are associated with the promotion of autophagy and inhibition of apoptosis.
Single Switched Capacitor Battery Balancing System Enhancements

Battery management systems (BMS) are a key element in electric vehicle energy storage systems. The BMS performs several functions concerning to the battery system, its key task being balancing the battery cells. Battery cell unbalancing hampers electric vehicles’ performance, with differing individual cell voltages decreasing the battery pack capacity and cell lifetime, leading to the eventual failure of the total battery system. Quite a lot of cell balancing topologies have been proposed, such as shunt resistor, shuttling capacitor, inductor/transformer based and DC energy converters. The shuttling capacitor balancing systems in particular have not been subject to much research efforts however, due to their perceived low balancing speed and high cost. This paper tries to fill this gap by briefly discussing the shuttling capacitor cell balancing topologies, focusing on the single switched capacitor (SSC) cell balancing and proposing a novel procedure to improve the SSC balancing system performance. This leads to a new control strategy for the SSC system that can decrease the balancing system size, cost, balancing time and that can improve the SSC balancing system efficiency.
Fraud Data Analytics Tools and Techniques in Big Data Era

Fraudulent activities (e.g., suspicious credit card transaction, financial reporting fraud, and money laundering) are critical concerns to various entities including bank, insurance companies, and public service organizations. Typically, these activities lead to detrimental effects on the victims such as a financial loss. Over the years, fraud analysis techniques underwent a rigorous development. However, lately, the advent of Big data led to vigorous advancement of these techniques since Big Data resulted in extensive opportunities to combat financial frauds. Given that the massive amount of data that investigators need to sift through, massive volumes of data integrated from multiple heterogeneous sources (e.g., social media, blogs) to find fraudulent patterns is emerging as a feasible approach.