nils-herrmann/clean_scirepeval huggingface dataset:
LIDA: A Working Model of Cognition

In this paper we present the LIDA architecture as a working model of cognition. We argue that such working models are broad in scope and address real world problems in comparison to experimentally based models which focus on specific pieces of cognition. While experimentally based models are useful, we need a working model of cognition that integrates what we know from neuroscience, cognitive science and AI. The LIDA architecture provides such a working model. A LIDA based cognitive robot or software agent will be capable of multiple learning mechanisms. With artificial feelings and emotions as primary motivators and learning facilitators, such systems will ‘live’ through a developmental period during which they will learn in multiple ways to act in an effective, human-like manner in complex, dynamic, and unpredictable environments. We discuss the integration of the learning mechanisms into the existing IDA architecture as a working model of cognition.
Dual Polarized Dual Antennas for 1.7–2.1 GHz LTE Base Stations

A novel antenna with both horizontal and vertical polarizations is proposed for 1.7-2.1 GHz LTE band small cell base stations. Horizontal polarization is achieved by using the Vivaldi antennas at the main PCB board in azimuth plane, whereas the vertical polarization is obtained using the rectangular monopole with curved corners in proximity of the horizontal elements. A prototype antenna associated with 8-elements (four horizontal and four vertical) is fabricated on the FR4 substrate with the thickness of 0.2 cm and 0.12 cm for Vivaldi and monopole antennas, respectively. Experimental results have validated the design procedure of the antenna with a volume of 14 × 14 × 4.5 cm3 and indicated the realization of the requirements for the small cell base station applications.
The Impact of Social Media use on Academic Performance among university students : A Pilot Study

There is no doubt that Social media has gained wider acceptability and usability and is also becoming probably the most important communication tools among students especially at the higher level of educational pursuit. As much as social media is viewed as having bridged the gap in communication that existed. Within the social media Facebook, Twitter and others are now gaining more and more patronage. These websites and social forums are way of communicating directly with other people socially. Social media has the potentials of influencing decision-making in a very short time regardless of the distance. On the bases of its influence, benefits and demerits this study is carried out in order to highlight the potentials of social media in the academic setting by collaborative learning and improve the students' academic performance. The results show that collaborative learning positively and significantly with interactive with peers, interactive with teachers and engagement which impact the students’ academic performance.
Gamification and student motivation

Print) 1744-5191 (Online)  http://www.tandfonline.com/loi/nile20 Gamification and student motivation Patrick Buckley & Elaine Doyle To cite this article: Patrick Buckley & Elaine Doyle (2016) Gamification and student motivation, Interactive Learning Environments, 24:6, 1162-1175,  To link to this article: https://doi.org/10.1080/10494820.2014.964263
Natural Language to Structured Query Generation via Meta-Learning

In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In this work, we explore a different learning protocol that treats each example as a unique pseudo-task, by reducing the original learning problem to a few-shot meta-learning scenario with the help of a domain-dependent relevance function.1 When evaluated on the WikiSQL dataset, our approach leads to faster convergence and achieves 1.1%–5.4% absolute accuracy gains over the non-meta-learning counterparts.
Path loss models for 5G millimeter wave propagation channels in urban microcells

Measurements for future outdoor cellular systems at 28 GHz and 38 GHz were conducted in urban microcellular environments in New York City and Austin, Texas, respectively. Measurements in both line-of-sight and non-line-of-sight scenarios used multiple combinations of steerable transmit and receive antennas (e.g. 24.5 dBi horn antennas with 10.9° half power beamwidths at 28 GHz, 25 dBi horn antennas with 7.8° half power beamwidths at 38 GHz, and 13.3 dBi horn antennas with 24.7° half power beamwidths at 38 GHz) at different transmit antenna heights. Based on the measured data, we present path loss models suitable for the development of fifth generation (5G) standards that show the distance dependency of received power. In this paper, path loss is expressed in easy-to-use formulas as the sum of a distant dependent path loss factor, a floating intercept, and a shadowing factor that minimizes the mean square error fit to the empirical data. The new models are compared with previous models that were limited to using a close-in free space reference distance. Here, we illustrate the differences of the two modeling approaches, and show that a floating intercept model reduces the shadow factors by several dB and offers smaller path loss exponents while simultaneously providing a better fit to the empirical data. The upshot of these new path loss models is that coverage is actually better than first suggested by work in , [7] and .
Research on Li-ion Battery Management System

Battery management system (BMS) is an integral part of the electric vehicle (EV) and the hybrid electric vehicle (HEV). The BMS performs the tasks by integrating one or more of the functions, such as sampling the voltages of the battery cells and the temperatures in the battery module, sampling the voltage of the battery, sampling the current of the battery, as well as cells balancing and determining the state of charge (SOC). In this paper the research of the cells voltage sampling, battery temperature sampling, battery voltage sampling and battery current sampling will be discussed. The method of sampling cells voltage, battery temperature and cells balancing using special integrate circuit (IC) will be presented. This method resolves the problems of sampling cells voltage in Li-ion battery, which has hundreds of cells. We discuss two methods about the result of battery current integral, and pick out the better via the data of experiment.
Hierarchical Extreme Learning Machine for unsupervised representation learning

Learning representations from massive unlabeled data is a hot topic for high-level tasks in many applications. The recent great improvements on benchmark data sets, which are achieved by increasingly complex unsupervised learning methods and deep learning models with lots of parameters, usually require many tedious tricks and much expertise to tune. However, filters learned by these complex architectures are quite similar to standard hand-crafted features visually, and training the deep models costs quite long time to fine-tune their weights. In this paper, Extreme Learning Machine-Autoencoder (ELM-AE) is employed as the learning unit to learn local receptive fields at each layer, and the lower layer responses are transferred to the last layer (trans-layer) to form a more complete representation to retain more information. In addition, some beneficial methods in deep learning architectures such as local contrast normalization and whitening are added to the proposed hierarchical Extreme Learning Machine networks to further boost the performance. The obtained trans-layer representations are followed by block histograms with binary hashing to learn translation and rotation invariant representations, which are utilized to do high-level tasks such as recognition and detection. Compared to traditional deep learning methods, the proposed trans-layer representation method with ELM-AE based learning of local receptive filters has much faster learning speed and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101. State-of-the-art performances are achieved on both Caltech 101 15 samples per class task and 4 of 6 MNIST variations data sets, and highly impressive results are obtained on MNIST data set and other tasks.
Secure multi-server-aided data deduplication in cloud computing

Cloud computing enables on-demand and ubiquitous access to a centralized pool of configurable resources such as networks, applications, and services. This makes that huge of enterprises and individual users outsource their data into the cloud server. As a result, the data volume in the cloud server is growing extremely fast. How to efficiently manage the ever-increasing datum is a new security challenge in cloud computing. Recently, secure deduplication techniques have attracted considerable interests in the both academic and industrial communities. It can not only provide the optimal usage of the storage and network bandwidth resources of cloud storage providers, but also reduce the storage cost of users. Although convergent encryption has been extensively adopted for secure deduplication, it inevitably suffers from the off-line brute-force dictionary attacks since the message usually can be predictable in practice. In order to address the above weakness, the notion of DupLESS was proposed in which the user can generate the convergent key with the help of a key server. We argue that the DupLESS does not work when the key server is corrupted by the cloud server. In this paper, we propose a newmulti-server-aided deduplication scheme based on the threshold blind signature, which can effectively resist the collusion attack between the cloud server and multiple key servers. Furthermore, we prove that our construction can achieve the desired security properties.
Clonal evolution in cancer

Cancers evolve by a reiterative process of clonal expansion, genetic diversification and clonal selection within the adaptive landscapes of tissue ecosystems. The dynamics are complex, with highly variable patterns of genetic diversity and resulting clonal architecture. Therapeutic intervention may destroy cancer clones and erode their habitats, but it can also inadvertently provide a potent selective pressure for the expansion of resistant variants. The inherently Darwinian character of cancer is the primary reason for this therapeutic failure, but it may also hold the key to more effective control.
StuffNet: Using ‘Stuff’ to Improve Object Detection

We propose a Convolutional Neural Network (CNN) based algorithm – StuffNet – for object detection. In addition to the standard convolutional features trained for region proposal and object detection , StuffNet uses convolutional features trained for segmentation of objects and 'stuff' (amorphous categories such as ground and water). Through experiments on Pascal VOC 2010, we show the importance of features learnt from stuff segmentation for improving object detection performance. StuffNet improves performance from 18.8% mAP to 23.9% mAP for small objects. We also devise a method to train StuffNet on datasets that do not have stuff segmentation labels. Through experiments on Pascal VOC 2007 and 2012, we demonstrate the effectiveness of this method and show that StuffNet also significantly improves object detection performance on such datasets.
Developing serious games for cultural heritage: a state-of-the-art review

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result, the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented.
Humor Detection in Yelp reviews

We utilize the state-of-the-art in deep learning to show that we can learn by example what constitutes humor in the context of a Yelp review. To the best of the authors knowledge, no systematic study of deep learning for humor exists – thus, we construct a scaffolded study. First, we use “shallow” methods such as Random Forests and Linear Discriminants built on top of bag-of-words and word vector features. Then, we build deep feedforward networks on top of these features – in some sense, measuring how much of an effect basic feedforward nets help. Then, we use recurrent neural networks and convolutional neural networks to more accurately model the sequential nature of a review.
Learning Syntax by Automata Induction

In this paper we propose an explicit computer model for learning natural language syntax based on Angluin's (1982) efficient induction algorithms, using a complete corpus of grammatical example sentences. We use these results to show how inductive inference methods may be applied to learn substantial, coherent subparts of at least one natural language – English – that are not susceptible to the kinds of learning envisioned in linguistic theory. As two concrete case studies, we show how to learn English auxiliary verb sequences (such as could be taking, will have been taking) and the sequences of articles and adjectives that appear before noun phrases (such as the very old big deer). Both systems can be acquired in a computationally feasible amount of time using either positive examples, or, in an incremental mode, with implicit negative examples (examples outside a finite corpus are considered to be negative examples). As far as we know, this is the first computer procedure that learns a full-scale range of noun subclasses and noun phrase structure. The generalizations and the time required for acquisition match our knowledge of child language acquisition for these two cases. More importantly, these results show that just where linguistic theories admit to highly irregular subportions, we can apply efficient automata-theoretic learning algorithms. Since the algorithm works only for fragments of language syntax, we do not believe that it suffices for all of language acquisition. Rather, we would claim that language acquisition is nonuniform and susceptible to a variety of acquisition strategies; this algorithm may be one these.
EvilCoder: automated bug insertion

The art of finding software vulnerabilities has been covered extensively in the literature and there is a huge body of work on this topic. In contrast, the intentional insertion of exploitable, security-critical bugs has received little (public) attention yet. Wanting more bugs seems to be counterproductive at first sight, but the comprehensive evaluation of bug-finding techniques suffers from a lack of ground truth and the scarcity of bugs.
 In this paper, we propose EvilCoder, a system to automatically find potentially vulnerable source code locations and modify the source code to be actually vulnerable. More specifically, we leverage automated program analysis techniques to find sensitive sinks which match typical bug patterns (e.g., a sensitive API function with a preceding sanity check), and try to find data-flow connections to user-controlled sources. We then transform the source code such that exploitation becomes possible, for example by removing or modifying input sanitization or other types of security checks. Our tool is designed to randomly pick vulnerable locations and possible modifications, such that it can generate numerous different vulnerabilities on the same software corpus. We evaluated our tool on several open-source projects such as for example libpng and vsftpd, where we found between 22 and 158 unique connected source-sink pairs per project. This translates to hundreds of potentially vulnerable data-flow paths and hundreds of bugs we can insert. We hope to support future bug-finding techniques by supplying freshly generated, bug-ridden test corpora so that such techniques can (finally) be evaluated and compared in a comprehensive and statistically meaningful way.
Schottky Barrier Carbon Nanotube Transistor: Compact Modeling, Scaling Study, and Circuit Design Applications

This paper presents a computationally efficient physics-based compact model for the Schottky barrier (SB) carbon nanotube field-effect transistor (CNTFET). This compact model includes a new analytical formulation of the channel charge, taking into account the influence of the source and drain SBs. Compact model simulation results (- characteristic and channel density of charge) as well as Monte Carlo simulation results, which are provided by a recent work, will be given and compared to each other and also to experimental data to validate the used approximations. Good agreement is observed over a large range of gate and drain biases. Furthermore, a scaling study is presented to examine the impact of technological parameters on the device figure of merit. Then, for the assessment of the SB on circuit performances, traditional logical circuits are designed using the SB-CNTFET compact model, and results are compared with a conventional CNTFET with zero-SB height. Finally, exploiting the particular properties of SB-CNTFETs, a three-valued static memory that is suitable for high density integration is presented.
HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection

The development of an anomaly-based intrusion detection system (IDS) is a primary research direction in the field of intrusion detection. An IDS learns normal and anomalous behavior by analyzing network traffic and can detect unknown and new attacks. However, the performance of an IDS is highly dependent on feature design, and designing a feature set that can accurately characterize network traffic is still an ongoing research issue. Anomaly-based IDSs also have the problem of a high false alarm rate (FAR), which seriously restricts their practical applications. In this paper, we propose a novel IDS called the hierarchical spatial-temporal features-based intrusion detection system (HAST-IDS), which first learns the low-level spatial features of network traffic using deep convolutional neural networks (CNNs) and then learns high-level temporal features using long short-term memory networks. The entire process of feature learning is completed by the deep neural networks automatically; no feature engineering techniques are required. The automatically learned traffic features effectively reduce the FAR. The standard DARPA1998 and ISCX2012 data sets are used to evaluate the performance of the proposed system. The experimental results show that the HAST-IDS outperforms other published approaches in terms of accuracy, detection rate, and FAR, which successfully demonstrates its effectiveness in both feature learning and FAR reduction.
A framework for post-event timeline reconstruction using neural networks

Post-event timeline reconstruction plays a critical role in forensic investigation and serves as a means of identifying evidence of the digital crime. We present an artificial neural networks based approach for post-event timeline reconstruction using the file system activities. A variety of digital forensic tools have been developed during the past two decades to assist computer forensic investigators undertaking digital timeline analysis, but most of the tools cannot handle large volumes of data efficiently. This paper looks at the effectiveness of employing neural network methodology for computer forensic analysis by preparing a timeline of relevant events occurring on a computing machine by tracing the previous file system activities. Our approach consists of monitoring the file system manipulations, capturing file system snapshots at discrete intervals of time to characterise the use of different software applications, and then using this captured data to train a neural network to recognise execution patterns of the application programs. The trained version of the network may then be used to generate a post-event timeline of a seized hard disk to verify the execution of different applications at different time intervals to assist in the identification of available evidence. a 2008  108 109 U N C O
PSO-SVR: A Hybrid Short-term Traffic Flow Forecasting Method

Accurate short-term flow forecasting is important for the real-time traffic control, but due to its complex nonlinear data pattern, getting a high precision is difficult. The support vector regression model (SVR) has been widely used to solve nonlinear regression and time series predicting problems. This paper presents a Hybrid PSO-SVR forecasting method to get a higher precision with less learning time; this method uses Particle Swarm Optimization (PSO) to search optimal SVR parameters. And to find a PSO that is more proper to SVR parameters searching, this paper proposes three kinds of strategies to handle the particles flow out the searching space, according to comparison, one of the strategies can make PSO get the optimal parameters more quickly, this paper calls the PSO using this strategy as fast PSO. Furthermore, to handle the precision's decline caused by the noises in the original data, this paper proposes a hybrid PSO-SVR method with historical momentum based on the similarity of historical short-term flow data. The forecasting results of extensive comparison experiments indicate that proposed model can get more accurate forecasting result than other state-of-the-art algorithms; and when the data containing noises, the method with historical momentum still deserves accurate forecasting.
POLYGLOT: Multilingual Semantic Role Labeling with Unified Labels

Semantic role labeling (SRL) identifies the predicate-argument structure in text with semantic labels. It plays a key role in understanding natural language. In this paper, we present POLYGLOT, a multilingual semantic role labeling system capable of semantically parsing sentences in 9 different languages from 4 different language groups. The core of POLYGLOT are SRL models for individual languages trained with automatically generated Proposition Banks (Akbik et al., 2015). The key feature of the system is that it treats the semantic labels of the English Proposition Bank as “universal semantic labels”: Given a sentence in any of the supported languages, POLYGLOT applies the corresponding SRL and predicts English PropBank frame and role annotation. The results are then visualized to facilitate the understanding of multilingual SRL with this unified semantic representation.