nils-herrmann/clean_scirepeval huggingface dataset:
The impact on ERP implementation by leadership and organisational culture: a case analysis

In this paper, we seek to investigate Ke and Wei’s (2008) propositions that ERP implementation success is positively related with top management leadership and organisational culture. This is accomplished by analysing the implementation of the ERP systems at a company, HealthSouth Corporation, longitudinally during two periods (1997 to 2003 and 2004 to 2007). During 1997 to 2003, leadership and culture in this organisation enabled accounting fraud to occur via the criminal misuse of a heavily-customised, unsupported ERP system. During 2004 to 2007, the leadership and culture enabled the company to avoid bankruptcy and a plain-vanilla (i.e., uncustomised) version of the ERP system was installed successfully. This paper provides valuable lessons to ERP practitioners on how ERP implementations are influenced by leadership, from the top management team, and organisational culture.
Hierarchical organization in complex networks.

Many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering. We show that these two features are the consequence of a hierarchical organization, implying that small groups of nodes organize in a hierarchical manner into increasingly large groups, while maintaining a scale-free topology. In hierarchical networks, the degree of clustering characterizing the different groups follows a strict scaling law, which can be used to identify the presence of a hierarchical organization in real networks. We find that several real networks, such as the Worldwideweb, actor network, the Internet at the domain level, and the semantic web obey this scaling law, indicating that hierarchy is a fundamental characteristic of many complex systems.
3D Role-Playing Games as Language Learning Tools

Leveraging the experiential cognition and motivational factors of 3D games, we conduct a pilot study that utilizes Ever Quest 2 as pedagogical learning tool for English as a second language (ESL) students. We combine the benefits of massive multiplayer online role-playing games (MMORPGs) and second language methodology to create a digital learning environment for second language acquisition (SLA). Rather than using traditional computerassisted language learning (CALL) software, we explore the immersive, virtual environment of Ever Quest 2 as ESL participants assume virtual identities and engage in social interactions within the game world. We suggest that language becomes a crucial artifact for character development and completion of game tasks in the virtual world. Preliminary results demonstrate that Ever Quest 2 intermediate and advanced ESL students increase their English vocabulary by 40% as result of game play interactions with non-playing characters (NPCs). Furthermore, intermediate and advanced ESL students practice their conversational skills with playing characters (PCs), generating a 100% increase in chat messages during eight sessions of game play. These results lead to the conclusion that MMORPGs can provide motivation and adequate language learning support for intermediate and advanced
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these kernels which makes them suitable for today's multi-core CPUs and GPUs. In the past few years there have been many studies claiming GPUs deliver substantial speedups (between 10X and 1000X) over multi-core CPUs on these kernels. To understand where such large performance difference comes from, we perform a rigorous performance analysis and find that after applying optimizations appropriate for both CPUs and GPUs the performance gap between an Nvidia GTX280 processor and the Intel Core i7-960 processor narrows to only 2.5x on average. In this paper, we discuss optimization techniques for both CPU and GPU, analyze what architecture features contributed to performance differences between the two architectures, and recommend a set of architectural features which provide significant improvement in architectural efficiency for throughput kernels.
License plate detection and character recognition system for commercial vehicles based on morphological approach and template matching

Though license plate detection and recognition system has a lot of applications but a very little work has been done on Bangla license plate recognition. Variations among the license plate patterns and complex background of Bangladeshi license plates make it more difficult to use the existing algorithms. Because of this, we propose a solution for Bangla license plate detection and recognition. Firstly, the position of the vehicle is determined. In our country, commercial License Plates have the unique color (green) of its own. That's why; we select the portions of green color with the matching RGB intensity of the plate. A boundary based contour algorithm, area and aspect ratio have been proposed to track down the license plate in the vehicle region. License plate rows containing registration information have been separated by using horizontal projection with threshold. The characters and the digits of the rows have been segmented using vertical projection with threshold value. Finally, template matching has been used for recognizing the characters and the digits of the Bangla license plate. We tested our algorithms for over 180 still images captured from the road. We achieve93% success in license plate detection, 98.1% success in segmentation and 88.8% success rate in Bangla license plate recognition.
REAL TIME HAND GESTURE RECOGNITION USING SIFT

The objective of the gesture recognition is to identify and distinguish the human gestures and utilizes these identified gestures for applications in specific domain. In this paper we propose a new approach to build a real time system to identify the standard gesture given by American Sign Language, or ASL, the dominant sign language of Deaf Americans, including deaf communities in the United States, in the English-speaking parts of Canada, and in some regions of Mexico. We propose a new method of improvised scale invariant feature transform (SIFT) and use the same to extract the features. The objective of the paper is to decode a gesture video into the appropriate alphabets.
Stability and Generalization

We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out error. The methods we use can be applied in the regression framework as well as in the classification one when the classifier is obtained by thresholding a real-valued function. We study the stability properties of large classes of learning algorithms such as regularization based algorithms. In particular we focus on Hilbert space regularization and Kullback-Leibler regularization. We demonstrate how to apply the results to SVM for regression and classification.
Organic food: buying more safety or just peace of mind? A critical review of the literature.

Consumer concern over the quality and safety of conventional food has intensified in recent years, and primarily drives the increasing demand for organically grown food, which is perceived as healthier and safer. Relevant scientific evidence, however, is scarce, while anecdotal reports abound. Although there is an urgent need for information related to health benefits and/or hazards of food products of both origins, generalized conclusions remain tentative in the absence of adequate comparative data. Organic fruits and vegetables can be expected to contain fewer agrochemical residues than conventionally grown alternatives; yet, the significance of this difference is questionable, inasmuch as actual levels of contamination in both types of food are generally well below acceptable limits. Also, some leafy, root, and tuber organic vegetables appear to have lower nitrate content compared with conventional ones, but whether or not dietary nitrate indeed constitutes a threat to human health is a matter of debate. On the other hand, no differences can be identified for environmental contaminants (e.g. cadmium and other heavy metals), which are likely to be present in food from both origins. With respect to other food hazards, such as endogenous plant toxins, biological pesticides and pathogenic microorganisms, available evidence is extremely limited preventing generalized statements. Also, results for mycotoxin contamination in cereal crops are variable and inconclusive; hence, no clear picture emerges. It is difficult, therefore, to weigh the risks, but what should be made clear is that 'organic' does not automatically equal 'safe.' Additional studies in this area of research are warranted. At our present state of knowledge, other factors rather than safety aspects seem to speak in favor of organic food.
Dual Autoencoder Network for Retinex-Based Low-Light Image Enhancement

This paper presents a dual autoencoder network model based on the retinex theory to perform the low-light enhancement and noise reduction by combining the stacked and convolutional autoencoders. The proposed method first estimates the spatially smooth illumination component which is brighter than an input low-light image using a stacked autoencoder with a small number of hidden units. Next, we use a convolutional autoencoder which deals with 2-D image information to reduce the amplified noise in the brightness enhancement process. We analyzed and compared roles of the stacked and convolutional autoencoders with the constraint terms of the variational retinex model. In the experiments, we demonstrate the performance of the proposed algorithm by comparing with the state-of-the-art existing low-light and contrast enhancement methods.
Learning temporal features using LSTM-CNN architecture for face anti-spoofing

Temporal features is important for face anti-spoofing. Unfortunately existing methods have limitations to explore such temporal features. In this work, we propose a deep neural network architecture combining Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN). Our architecture works well for face anti-spoofing by utilizing the LSTM units' ability of finding long relation from its input sequences as well as extracting local and dense features through convolution operations. Our best model shows significant performance improvement over general CNN architecture (5.93% vs. 7.34%), and hand-crafted features (5.93% vs. 10.00%) on CASIA dataset.
What characterizes a shadow boundary under the sun and sky?

Despite decades of study, robust shadow detection remains difficult, especially within a single color image. We describe a new approach to detect shadow boundaries in images of outdoor scenes lit only by the sun and sky. The method first extracts visual features of candidate edges that are motivated by physical models of illumination and occluders. We feed these features into a Support Vector Machine (SVM) that was trained to discriminate between most-likely shadow-edge candidates and less-likely ones. Finally, we connect edges to help reject non-shadow edge candidates, and to encourage closed, connected shadow boundaries. On benchmark shadow-edge data sets from Lalonde et al. and Zhu et al., our method showed substantial improvements when compared to other recent shadow-detection methods based on statistical learning.
Scaling Optoelectronic-VLSI Circuits into the 21 st Century : A Technology Roadmap

Technologies now exist for implementing dense surface-normal optical interconnections for silicon CMOS VLSI using hybrid integration techniques. The critical factors in determining the performance of the resulting photonic chip are the yield on the transceiver device arrays, the sensitivity and power dissipation of the receiver and transmitter circuits, and the total optical power budget available. The use of GaAs–AlGaAs multiple-quantum-well p-i-n diodes for on-chip detection and modulation is one effective means of implementing the optoelectronic transceivers. We discuss a potential roadmap for the scaling of this hybrid optoelectronic VLSI technology as CMOS linewidths shrink and the characteristics of the hybrid optoelectronic tranceiver technology improve. An important general conclusion is that, unlike electrical interconnects, such dense optical interconnections directly to an electronic circuit will likely be able to scale in capacity to match the improved performance of future CMOS technology.
SMART VOTING SYSTEM WITH FACE RECOGNITION

Manual voting system has been deployed for many yea rs in our country. However in many parts of our cou ntry people cannot attend the voting because of several r asons. In order to solve these problems there is a need of online election voting system in addition to manual voting system. The long-term goal of this project is to g reatly reduce the cost and complexity of running elections and increase th e accuracy of results by removing the direct involv ement of humans in gathering and counting of votes. The voters will be a le to give their votes at any field areas by usi ng the system if they prefer online voting In this system, the user votes using android applic ation which can be downloaded over internet. The us er has to login before giving his vote. Authentication will b e done using SMS confirmation and face recognition. Users can view parties, enter votes, can verify vote via SMS or email and can view result after result is declared.
Transforming GIS Data into Functional Road Models for Large-Scale Traffic Simulation

There exists a vast amount of geographic information system (GIS) data that model road networks around the world as polylines with attributes. In this form, the data are insufficient for applications such as simulation and 3D visualization-tools which will grow in power and demand as sensor data become more pervasive and as governments try to optimize their existing physical infrastructure. In this paper, we propose an efficient method for enhancing a road map from a GIS database to create a geometrically and topologically consistent 3D model to be used in real-time traffic simulation, interactive visualization of virtual worlds, and autonomous vehicle navigation. The resulting representation provides important road features for traffic simulations, including ramps, highways, overpasses, legal merge zones, and intersections with arbitrary states, and it is independent of the simulation methodologies. We test the 3D models of road networks generated by our algorithm on real-time traffic simulation using both macroscopic and microscopic techniques.
A Learning-Based Framework for Velocity Control in Autonomous Driving

We present a framework for autonomous driving which can learn from human demonstrations, and we apply it to the longitudinal control of an autonomous car. Offline, we model car-following strategies from a set of example driving sequences. Online, the model is used to compute accelerations which replicate what a human driver would do in the same situation. This reference acceleration is tracked by a predictive controller which enforces a set of comfort and safety constraints before applying the final acceleration. The controller is designed to be robust to the uncertainty in the predicted motion of the preceding vehicle. In addition, we estimate the confidence of the driver model predictions and use it in the cost function of the predictive controller. As a result, we can handle cases where the training data used to learn the driver model does not provide sufficient information about how a human driver would handle the current driving situation. The approach is validated using a combination of simulations and experiments on our autonomous vehicle.
An evaluation of bipartitioning techniques

Logic partitioning is an important issue in VLSI CAD, and has been an area of active research for at least the last 25 years. Numerous approaches have been developed and many different techniques have been combined for a wide range of applications. In this paper, we examine many of the existing techniques for logic bipartitioning and present a methodology for determining the best mix of approaches. The result is a novel bipartitioning algorithm that includes both new and pre-existing techniques. Our algorithm produces results that are at least 16% better than the state-of-the-art while also being efficient in run-time.
Theory and Applications of Robust Optimization

In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus will be on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying the most prominent theoretical results of RO over the past decade, we will also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we will highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.
Design Challenges and Misconceptions in Named Entity Recognition

We analyze some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system. In particular, we address issues such as the representation of text chunks, the inference approach needed to combine local NER decisions, the sources of prior knowledge and how to use them within an NER system. In the process of comparing several solutions to these challenges we reach some surprising conclusions, as well as develop an NER system that achieves 90.8 F1 score on the CoNLL-2003 NER shared task, the best reported result for this dataset.
Signature-based videos' visual similarity detection and measurement

The quantity of digital videos is huge, due to technological advances in video capture, storage and compression. However, the usefulness of these enormous volumes is limited by the effectiveness of content-based video retrieval systems (CBVR) that still requires time-consuming annotating/tagging to feed the text-based search. Visual similarity is the core of these CBVR systems where videos are matched based on their respective visual features and their evolvement across video frames. Also, it acts as an essential foundational layer to infer semantic similarity at advanced stage, in collaboration with metadata. Furthermore, handling such amounts of video data, especially the compressed-domain, forces certain challenges for CBVR systems: speed, scalability and genericness. The situation is even more challenging with availability of nonpixelated features, due to compression, e.g. DC/AC coefficients and motion vectors, that requires sophisticated processing. Thus, a careful features’ selection is important to realize the visual similarity based matching within boundaries of the aforementioned challenges. Matching speed is crucial, because most of the current research is biased towards the accuracy and leaves the speed lagging behind, which in many cases affect the practical uses. Scalability is the key for benefiting from these enormous available videos amounts. Genericness is an essential aspect to develop systems that is applicable to, both, compressed and uncompressed videos. This thesis presents a signature-based framework for efficient visual similarity based video matching. The proposed framework represents a vital component for search and retrieval systems, where it could be used in three possible different ways: (1)Directly for CBVR systems where a user submits a query video and the system retrieves a ranked list of visually similar ones. (2)For text-based video retrieval systems, e.g. YouTube, when a user submits a textual description and the system retrieves a ranked list of relevant videos. The retrieval in this case works by finding videos that were manually assigned similar textual description (annotations). For this scenario, the framework could be used to enhance the annotation process. This is achievable by suggesting an annotations-set for the newly uploading videos. These annotations are derived from other visually similar videos that can be retrieved by the proposed framework. In this way, the framework could make annotations more relevant to video contents (compared to the manual way) which improves the overall CBVR systems’ performance as well.
Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping

Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due to their nonlinear modeling capabilities. However, it is well known that in general, traditional training methods for ANNs such as back-propagation (BP) approach are normally slow and it could be trapped into local optima. In this paper, a fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome these drawbacks. The new approach also has improved price intervals forecast accuracy by incorporating bootstrapping method for uncertainty estimations. Case studies based on chaos time series and Australian National Electricity Market price series show that the proposed method can effectively capture the nonlinearity from the highly volatile price data series with much less computation time compared with other methods. The results show the great potential of this proposed approach for online accurate price forecasting for the spot market prices analysis.
Investigation of multilingual deep neural networks for spoken term detection

The development of high-performance speech processing systems for low-resource languages is a challenging area. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to use bottleneck features, or hybrid systems, trained on multilingual data for speech-to-text (STT) systems. This paper presents an investigation into the application of these multilingual approaches to spoken term detection. Experiments were run using the IARPA Babel limited language pack corpora (~10 hours/language) with 4 languages for initial multilingual system development and an additional held-out target language. STT gains achieved through using multilingual bottleneck features in a Tandem configuration are shown to also apply to keyword search (KWS). Further improvements in both STT and KWS were observed by incorporating language questions into the Tandem GMM-HMM decision trees for the training set languages. Adapted hybrid systems performed slightly worse on average than the adapted Tandem systems. A language independent acoustic model test on the target language showed that retraining or adapting of the acoustic models to the target language is currently minimally needed to achieve reasonable performance.