nils-herrmann/clean_scirepeval huggingface dataset:
Using Generative Adversarial Networks to Design Shoes : The Preliminary Steps

In this paper, we envision a Conditional Generative Adversarial Network (CGAN) designed to generate shoes according to an input vector encoding desired features and functional type. Though we do not build the CGAN, we lay the foundation for its completion by exploring 3 areas. Our dataset is the UT-Zap50K dataset, which has 50,025 images of shoes categorized by functional type and with relative attribute comparisons. First, we experiment with several models to build a stable Generative Adversarial Network (GAN) trained on just athletic shoes. Then, we build a classifier based on GoogLeNet that is able to accurately categorize shoe images into their respective functional types. Finally, we explore the possibility of creating a binary classifier for each attribute in our dataset, though we are ultimately limited by the quality of the attribute comparisons provided. The progress made by this study will provide a robust base to create a conditional GAN that generates customized shoe designs.
A survey of automated material handling systems in 300-mm SemiconductorFabs

The fast-paced developments and technological breakthroughs in the semiconductor manufacturing industry elevates the importance of optimum utilization of resources. The newer 300-mm wafers fabs place a high level of emphasis on increasing yield and reducing cycle times. Automated material handling systems are importanttools that help us achieve these objectives. In addition, due to the increased weight and size of 300-mm wafers, an automated material handling system isa must for a 300-mm manufacturing facility. This paper discusses various approaches for automated materials handling in semiconductor manufacturing industries.
Adjusting Mixture Weights of Gaussian Mixture Model via Regularized Probabilistic Latent Semantic Analysis

Mixture models, such as Gaussian Mixture Model, have been widely used in many applications for modeling data. Gaussian mixture model (GMM) assumes that data points are generated from a set of Gaussian models with the same set of mixture weights. A natural extension of GMM is the probabilistic latent semantic analysis (PLSA) model, which assigns different mixture weights for each data point. Thus, PLSA is more flexible than the GMM method. However, as a tradeoff, PLSA usually suffers from the overfitting problem. In this paper, we propose a regularized probabilistic latent semantic analysis model (RPLSA), which can properly adjust the amount of model flexibility so that not only the training data can be fit well but also the model is robust to avoid the overfitting problem. We conduct empirical study for the application of speaker identification to show the effectiveness of the new model. The experiment results on the NIST speaker recognition dataset indicate that the RPLSA model outperforms both the GMM and PLSA models substantially. The principle of RPLSA of appropriately adjusting model flexibility can be naturally extended to other applications and other types of mixture models.
Software Reuse: Metrics and Models

As organizations implement systematic software reuse programs to improve productivity and quality, they must be able to measure their progress and identify the most effective reuse strategies. This is done with reuse metrics and models. In this article we survey metrics and models of software reuse and reusability, and provide a classification structure that will help users select them. Six types of metrics and models are reviewed: cost-benefit models, maturity assessment models, amount of reuse metrics, failure modes models, reusability assessment models, and reuse library metrics.
A Diffusion Model Account of Criterion Shifts in the Lexical Decision Task.

Performance in the lexical decision task is highly dependent on decision criteria. These criteria can be influenced by speed versus accuracy instructions and word/nonword proportions. Experiment 1 showed that error responses speed up relative to correct responses under instructions to respond quickly. Experiment 2 showed that that responses to less probable stimuli are slower and less accurate than responses to more probable stimuli. The data from both experiments support the diffusion model for lexical decision (Ratcliff, Gomez, & McKoon, 2004). At the same time, the data provide evidence against the popular deadline model for lexical decision. The deadline model assumes that "nonword" responses are given only after the "word" response has timed out - consequently, the deadline model cannot account for the data from experimental conditions in which "nonword" responses are systematically faster than "word" responses.
Factors for success in customer relationship management ( CRM ) systems Journal Item

The importance of effective customer relationships as a key to customer value and hence shareholder value is widely emphasised. In order to enhance these relationships, the application of IT to marketing through customer relationship management (CRM) software, e-commerce and other initiatives is growing rapidly. This study examines the factors that influence the successful deployment of CRM applications, with particular emphasis on those factors which are distinct from other areas of application. Using the analytic induction method, success factors were derived from five in-depth case studies. Resulting factors underemphasised in previous literature include: the need for project approval procedures which allow for uncertainty; the need to leverage models of best practice; the importance of prototyping new processes, not just IT; and the need to manage for the delivery of the intended benefits, rather than just implementing the original specification.
Fingerprint classification using convolutional neural networks and ridge orientation images

Deep learning is currently popular in the field of computer vision and pattern recognition. Deep learning models such as Convolutional Neural Networks (CNNs) have been shown to be extremely effective at solving computer vision problems with state-of-the-art results. This work uses the CNN model to achieve a high degree of accuracy on the problem of fingerprint image classification. It will also be shown that effective image preprocessing can greatly reduce the dimensionality of the problem, allowing fast training times without compromising classification accuracy, even in networks of moderate depth. The proposed approach has achieved 95.9% classification accuracy on the NIST-DB4 dataset with zero sample rejection.
A Survey on Cloud Computing Scheduling Algorithms

Cloud computing is a distributed computing modelwhich enables developers to automatically deploy applicationsduring task allocation and storage distribution. Cloud computingintends to share a pool of virtualized computer resources andequipment's of computation, storage and information. In cloudcomputing environment, task scheduling is a key problem whichneeds to consider various factors restricting the user tasks and isresponsible for selecting the most suitable resources of cloudcomputing for user task. Task scheduling algorithm is an NPcompleteproblem which places an important role in cloudcomputing. This article surveys the literature on job schedulingalgorithms as they apply to cloud computing environment. Itsummarizes keys of performance of each algorithm in different loadstates from conducted simulation.
A comprehensive study of hybrid neural network hidden Markov model for offline handwritten Chinese text recognition

This paper proposes an effective segmentation-free approach using a hybrid neural network hidden Markov model (NN-HMM) for offline handwritten Chinese text recognition (HCTR). In the general Bayesian framework, the handwritten Chinese text line is sequentially modeled by HMMs with each representing one character class, while the NN-based classifier is adopted to calculate the posterior probability of all HMM states. The key issues in feature extraction, character modeling, and language modeling are comprehensively investigated to show the effectiveness of NN-HMM framework for offline HCTR. First, a conventional deep neural network (DNN) architecture is studied with a well-designed feature extractor. As for the training procedure, the label refinement using forced alignment and the sequence training can yield significant gains on top of the frame-level cross-entropy criterion. Second, a deep convolutional neural network (DCNN) with automatically learned discriminative features demonstrates its superiority to DNN in the HMM framework. Moreover, to solve the challenging problem of distinguishing quite confusing classes due to the large vocabulary of Chinese characters, NN-based classifier should output 19900 HMM states as the classification units via a high-resolution modeling within each character. On the ICDAR 2013 competition task of CASIA-HWDB database, DNN-HMM yields a promising character error rate (CER) of 5.24% by making a good trade-off between the computational complexity and recognition accuracy. To the best of our knowledge, DCNN-HMM can achieve a best published CER of 3.53%.
Don't Decay the Learning Rate, Increase the Batch Size

It is common practice to decay the learning rate. Here we show one can usually obtain the same learning curve on both training and test sets by instead increasing the batch size during training. This procedure is successful for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum, and Adam. It reaches equivalent test accuracies after the same number of training epochs, but with fewer parameter updates, leading to greater parallelism and shorter training times. We can further reduce the number of parameter updates by increasing the learning rate and scaling the batch size B ∝ . Finally, one can increase the momentum coefficient m and scale B ∝ 1/(1 −m), although this tends to slightly reduce the test accuracy. Crucially, our techniques allow us to repurpose existing training schedules for large batch training with no hyper-parameter tuning. We train ResNet-50 on ImageNet to 76.1% validation accuracy in under 30 minutes.
Evaluation of communication technologies for IEC 61850 based distribution automation system with distributed energy resources

This paper presents the study of different communication systems between IEC 61850 based distribution substation and distributed energy resources (DERs). Communication networks have been simulated for a typical distribution automation system (DAS) with DERs using OPNET software. The simulation study shows the performance of wired and wireless communication systems for different messages, such as GOOSE and measured (metered) values between DAS and DERs. A laboratory set-up has been implemented using commercial relay and communication devices for evaluating the performance of GOOSE messages, using wired and wireless physical medium. Finally, simulation and laboratory results are discussed in detail.
Reading Comprehension using Entity-based Memory Network

This paper introduces a novel neural network model for question answering, the entity-based memory networks. It enhances neural networks’ ability of representing and calculating information over a long period by keeping records of entities contained in text. The core component is a memory pool which comprises entities’ states. These entities’ states are continuously updated according to the input text. Questions with regard to the input text are used to search the memory pool for related entities and answers are further predicted based on the states of retrieved entities. Compared with previous memory network models, the proposed model is capable of handling fine-grained information and more sophisticated relations based on entities. We formulated several different tasks as question answering problems and tested the proposed model. Experiments reported satisfying results.
Dual coordinate descent methods for logistic regression and maximum entropy models

Most optimization methods for logistic regression or maximum entropy solve the primal problem. They range from iterative scaling, coordinate descent, quasi-Newton, and truncated Newton. Less efforts have been made to solve the dual problem. In contrast, for linear support vector machines (SVM), methods have been shown to be very effective for solving the dual problem. In this paper, we apply coordinate descent methods to solve the dual form of logistic regression and maximum entropy. Interestingly, many details are different from the situation in linear SVM. We carefully study the theoretical convergence as well as numerical issues. The proposed method is shown to be faster than most state of the art methods for training logistic regression and maximum entropy.
Semantic Foggy Scene Understanding with Synthetic Data

This work addresses the problem of semantic foggy scene understanding (SFSU). Although extensive research has been performed on image dehazing and on semantic scene understanding with clear-weather images, little attention has been paid to SFSU. Due to the difficulty of collecting and annotating foggy images, we choose to generate synthetic fog on real images that depict clear-weather outdoor scenes, and then leverage these partially synthetic data for SFSU by employing state-of-the-art convolutional neural networks (CNN). In particular, a complete pipeline to add synthetic fog to real, clear-weather images using incomplete depth information is developed. We apply our fog synthesis on the Cityscapes dataset and generate Foggy Cityscapes with 20,550 images. SFSU is tackled in two ways: (1) with typical supervised learning, and (2) with a novel type of semi-supervised learning, which combines (1) with an unsupervised supervision transfer from clear-weather images to their synthetic foggy counterparts. In addition, we carefully study the usefulness of image dehazing for SFSU. For evaluation, we present Foggy Driving, a dataset with 101 real-world images depicting foggy driving scenes, which come with ground truth annotations for semantic segmentation and object detection. Extensive experiments show that (1) supervised learning with our synthetic data significantly improves the performance of state-of-the-art CNN for SFSU on Foggy Driving; (2) our semi-supervised learning strategy further improves performance; and (3) image dehazing marginally advances SFSU with our learning strategy. The datasets, models and code are made publicly available.
The neurobiology of psychedelic drugs: implications for the treatment of mood disorders

After a pause of nearly 40 years in research into the effects of psychedelic drugs, recent advances in our understanding of the neurobiology of psychedelics, such as lysergic acid diethylamide (LSD), psilocybin and ketamine have led to renewed interest in the clinical potential of psychedelics in the treatment of various psychiatric disorders. Recent behavioural and neuroimaging data show that psychedelics modulate neural circuits that have been implicated in mood and affective disorders, and can reduce the clinical symptoms of these disorders. These findings raise the possibility that research into psychedelics might identify novel therapeutic mechanisms and approaches that are based on glutamate-driven neuroplasticity.
Longitudinal study of symptoms and cognitive function in chronic schizophrenia

There is conflicting evidence of a relationship between changes in symptoms and cognitive functioning in schizophrenia. This study investigated longitudinal changes in psychopathology and cognitive functioning in chronic schizophrenia utilising three different dimensional models of symptomatology. Sixty-two patients diagnosed with DSM-IV schizophrenia or schizoaffective disorder were examined on two occasions over a period of 6 months for symptom improvement, measured by Positive and Negative Syndrome Scale (PANSS) [Kay et al.,  (1987) 261]. Participants also completed a comprehensive battery of neuropsychological tasks designed to assess attention, verbal and non-verbal memory, psychomotor processing and executive/frontal functioning on both occasions. Twenty-five control subjects were assessed for comparison purposes. Severity of negative symptoms predicted poor neuropsychological performance on IQ, verbal fluency and memory measures at occasion one. However, using regression analyses, significant improvements in symptom ratings over time using two-, three- or five-dimensional models did not predict improvements in any aspect of cognitive functioning measured, except motor speed. The results do not suggest a causal relationship between the course of symptoms and neuropsychological functioning in chronic schizophrenia.
Shallow Semantic Parsing using Support Vector Machines

In this paper, we propose a machine learning algorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algorithm is based on Support Vector Machines which we show give an improvement in performance over earlier classifiers. We show performance improvements through a number of new features and measure their ability to generalize to a new test set drawn from the AQUAINT corpus.
Comparison of Question Answering Systems Based on Ontology and Semantic Web in Different Environment

Question Answering (QA) system is taking an importa nt role in current search engine optimization concept. Natural language processing technique is mostly implemented in QA system for asking user’s question and several steps are also followed for conversion of questions to query form for getting a n exact answer.  This paper surveys different types of question answering system based on ontology and semantic web model with different query format. For comparison, the types of input, q ery processing method, input and output format of each system and the performance metrics with its li mitations are analyzed and discussed. Our question answering for automatic learning system architectur e is used to overcome the difficulties raised from the different QA models.  The semantic search methodology is implemented by u sing RDF graph in the application of data structure domain a nd the performance is also analyzed. Answers are retrieved from ontology using Semantic Search appro ach and question-to-query algorithm is evaluated in our system for analyzing performance evaluation.  Performance of question answering system of getting exact result can be improved by u sing semantic search methodology for retrieving answers from ontology model. Our system successfull y implements this technique and the system is also used in intelligent manner for automatic learn ing method.
BranchGAN: Branched Generative Adversarial Networks for Scale-Disentangled Learning and Synthesis of Images.

We introduce BranchGAN , a novel training method that enables unconditioned generative adversarial networks (GANs) to learn image manifolds at multiple scales. The key novel feature of BranchGAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features. Specifically, each noise vector, as input to the generator network, is explicitly split into several sub-vectors, each corresponding to, and is trained to learn, image representations at a particular scale. During training, we progressively “defreeze” the sub-vectors, one at a time, as a new set of higher-resolution images is employed for training and more network layers are added. A consequence of such an explicit sub-vector designation is that we can directly manipulate and even combine latent (sub-vector) codes which model different feature scales. Experiments demonstrate the effectiveness of our training method in scale-disentangled learning of image manifolds and synthesis, without any extra labels and without compromising quality of the synthesized high-resolution images. We further demonstrate three applications enabled or improved by BranchGAN.
Research issues in symbiotic simulation

Symbiotic simulation is a paradigm in which a simulation system and a physical system are closely associated with each other. This close relationship can be mutually beneficial. The simulation system benefits from real-time measurements about the physical system which are provided by corresponding sensors. The physical system, on the other side, may benefit from the effects of decisions made by the simulation system. An important concept in symbiotic simulation is that of the what-if analysis process which is concerned with the evaluation of a number of what-if scenarios by means of simulation. Symbiotic simulation and related paradigms have become popular in recent years because of their ability to dynamically incorporate real-time sensor data. In this paper, we explain different types of symbiotic simulation and give an overview of the state of the art. In addition, we discuss common research issues that have to be addressed when working with symbiotic simulation. While some issues have been adequately addressed, there are still research issues that remain open.
Study of bi-directional buck-boost converter with different control methods

In this paper, four types control methods have been proposed for buck-boost cascade topology. This converter used in hybrid electric vehicle can step the battery voltage level either up or down. The target is to find the most appropriate control method for the converter with certain specifications. Critical conduction mode control, fixed frequency control, the buck/boost control and the buck + boost control have been mentioned. The inductor design is presented according to the given parameters. The relationship among the parameters for buck/boost mode, buck mode and boost mode in critical conduction mode are proposed. The efficiency comparison for four control methods is given. The loss breakdown of different control methods is also presented by mathematical calculation.
CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification

This paper describes a system developed for a shared sentiment analysis task and its subtasks organized by SemEval-2017. A key feature of our system is the embedded ability to detect sarcasm in order to enhance the performance of sentiment classification. We first constructed an affect-cognition-sociolinguistics sarcasm features model and trained a SVM-based classifier for detecting sarcastic expressions from general tweets. For sentiment prediction, we developed CrystalNest—a two-level cascade classification system using features combining sarcasm score derived from our sarcasm classifier, sentiment scores from Alchemy, NRC lexicon, n-grams, word embedding vectors, and part-of-speech features. We found that the sarcasm detection derived features consistently benefited key sentiment analysis evaluation metrics, in different degrees, across four subtasks A-D.
NEED FOR HUMAN DIRECTION OF DATA MINING CROSS-INDUSTRY STANDARD PROCESS: CRISP–DM CASE STUDY 1: ANALYZING AUTOMOBILE WARRANTY CLAIMS: EXAMPLE OF THE CRISP–DM INDUSTRY STANDARD PROCESS IN ACTION FALLACIES OF DATA MINING

EXAMPLE OF THE CRISP–DM INDUSTRY STANDARD PROCESS IN ACTION FALLACIES OF DATA MINING WHAT TASKS CAN DATA MINING ACCOMPLISH?  PREDICTING ABNORMAL STOCK MARKET RETURNS USING NEURAL NETWORKS CASE  PREDICTING CORPORATE BANKRUPTCIES USING DECISION  PROFILING THE TOURISM MARKET USING k-MEANS CLUSTERING ANALYSIS