nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive PID Control Strategy Based on RBF Neural Network Identification

Radial basis function (RBF) neural network (NN) is powerful computational tools that have been used extensively in the areas of pattern recognition, systems modeling and identification. This paper proposes an adaptive PID control method based on RBF neural network identification. This approach can on-line identify the controlled plant with the RBF neural network identifier and the weights of the adaptive PID controller are adjusted timely based-on the identification of the plant and self-learning capability of RBFNN. Simulation result shows that the proposed controller has the adaptability, strong robustness and satisfactory control performance in the nonlinear and time varying system
Dynamic Supply and Threshold Voltage Scaling for CMOS Digital Circuits Using In-Situ Power Monitor

A generalized power tracking algorithm that minimizes power consumption of digital circuits by dynamic control of supply voltage and the body bias is proposed. A direct power monitoring scheme is proposed that does not need any replica and hence can sense total power consumed by load circuit across process, voltage, and temperature corners. Design details and performance of power monitor and tracking algorithm are examined by a simulation framework developed using UMC 90-nm CMOS triple well process. The proposed algorithm with direct power monitor achieves a power savings of 42.2% for activity of 0.02 and 22.4% for activity of 0.04. Experimental results from test chip fabricated in AMS 350 nm process shows power savings of 46.3% and 65% for load circuit operating in super threshold and near sub-threshold region, respectively. Measured resolution of power monitor is around 0.25 mV and it has a power overhead of 2.2% of die power. Issues with loop convergence and design tradeoff for power monitor are also discussed in this paper.
A Dense-Depth Representation for VLAD Descriptors in Content-Based Image Retrieval

The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks. Through the many convolutional layers, available in a Convolutional Neural Network (CNN), it is possible to obtain a hierarchy of features from the evaluated image. At every step, the patches extracted are smaller than the previous levels and more representative. Following this idea, this paper introduces a new detector applied on the feature maps extracted from pre-trained CNN. Specifically, this approach lets to increase the number of features in order to increase the performance of the aggregation algorithms like the most famous and used VLAD embedding. The proposed approach is tested on different public datasets: Holidays, Oxford5k, Paris6k and UKB.
Practical CFD Applications to Design of a Wave Cancellation Multihull Ship

Four methods of analysis | a nonlinear method based on Euler's equations and three linear potential ow methods | are used to determine the optimal location of the outer hulls for a wave cancellation multihull ship that consists of a main center hull and two outer hulls. The three potential ow methods correspond to a hierarchy of simple approximations based on the Fourier-Kochin representation of ship waves and the slender-ship approximation.
Deep Learning using Support Vector Machines

Recently, fully-connected and convolutional neural networks have been trained to reach state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics data. For classification tasks, much of these “deep learning” models employ the softmax activation functions to learn output labels in 1-of-K format. In this paper, we demonstrate a small but consistent advantage of replacing softmax layer with a linear support vector machine. Learning minimizes a margin-based loss instead of the cross-entropy loss. In almost all of the previous works, hidden representation of deep networks are first learned using supervised or unsupervised techniques, and then are fed into SVMs as inputs. In contrast to those models, we are proposing to train all layers of the deep networks by backpropagating gradients through the top level SVM, learning features of all layers. Our experiments show that simply replacing softmax with linear SVMs gives significant gains on datasets MNIST, CIFAR-10, and the ICML 2013 Representation Learning Workshop’s face expression recognition challenge.
Time series forecasting with neural networks : a comparative study using the airline data

This case-study ®ts a variety of neural network (NN) models to the well-known airline data and compares the resulting forecasts with those obtained from the Box±Jenkins and Holt± Winters methods. Many potential problems in ®tting NN models were revealed such as the possibility that the ®tting routine may not converge or may converge to a local minimum. Moreover it was found that an NN model which ®ts well may give poor out-of-sample forecasts. Thus we think it is unwise to apply NN models blindly in `black box' mode as has sometimes been suggested. Rather, the wise analyst needs to use traditional modelling skills to select a good NN model, e.g. to select appropriate lagged variables as the `inputs'. The Bayesian information criterion is preferred to Akaike's information criterion for comparing different models. Methods of examining the response surface implied by an NN model are examined and compared with the results of alternative nonparametric procedures using generalized additive models and projection pursuit regression. The latter imposes less structure on the model and is arguably easier to understand.
Visual Twitter Analytics (Vista): Temporally changing sentiment and the discovery of emergent themes within sport event tweets

Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues.  Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparklines showing the distribution of the top terms, hashtags, user mentions, and authors in each of the positive, neutral, and negative classes. Interactive tools are provided to support sub-querying and the examination of emergent themes.  A case study of using Vista to analyze sport fan engagement within a mega-sport event (2013 Le Tour de France) is provided. The authors illustrate how emergent themes can be identified and isolated from the large collection of data, without the need to identify these a priori.  Vista provides mechanisms that support the interactive exploration among Twitter data. By combining automatic data processing and machine learning methods with interactive visualization software, researchers are relieved of tedious data processing tasks, and can focus on the analysis of high-level features of the data. In particular, patterns of Twitter use can be identified, emergent themes can be isolated, and purposeful samples of the data can be selected by the researcher for further analysis.
Ferroelectric thin films for micro-sensors and actuators : a review

This paper reviews deposition, integration, and device fabrication of ferroelectric PbZrxTi1−xO3 (PZT) films for applications in microelectromechanical systems. As examples, a piezoelectric ultrasonic micromotor and pyroelectric infrared detector array are presented. A summary of the published data on the piezoelectric properties of PZT thin films is given. The figures of merit for various applications are discussed. Some considerations and results on operation, reliability, and depolarization of PZT thin films are presented.
Using metrics in Agile and Lean Software Development - A systematic literature review of industrial studies

Software industry has widely adopted Agile software development methods. Agile literature proposes a few key metrics but little is known of the actual metrics use in Agile teams.  The objective of this paper is to increase knowledge of the reasons for and effects of using metrics in industrial Agile development. We focus on the metrics that Agile teams use, rather than the ones used from outside by software engineering researchers. In addition, we analyse the influence of the used metrics.  This paper presents a systematic literature review (SLR) on using metrics in industrial Agile software development. We identified 774 papers, which we reduced to 30 primary studies through our paper selection process.  The results indicate that the reasons for and the effects of using metrics are focused on the following areas: sprint planning, progress tracking, software quality measurement, fixing software process problems, and motivating people. Additionally, we show that although Agile teams use many metrics suggested in the Agile literature, they also use many custom metrics. Finally, the most influential metrics in the primary studies are Velocity and Effort estimate.  The use of metrics in Agile software development is similar to Traditional software development. Projects and sprints need to be planned and tracked. Quality needs to be measured. Problems in the process need to be identified and fixed. Future work should focus on metrics that had high importance but low prevalence in our study, as they can offer the largest impact to the software industry.
Deep Neural Nets with Interpolating Function as Output Activation

We replace the output layer of deep neural nets, typically the softmax function, by a novel interpolating function. And we propose end-to-end training and testing algorithms for this new architecture. Compared to classical neural nets with softmax function as output activation, the surrogate with interpolating function as output activation combines advantages of both deep and manifold learning. The new framework demonstrates the following major advantages: First, it is better applicable to the case with insufficient training data. Second, it significantly improves the generalization accuracy on a wide variety of networks. The algorithm is implemented in PyTorch, and the code is available at https://github.com/ BaoWangMath/DNN-DataDependentActivation.
Life Cycle Analysis of Distributed Recycling of Post-consumer High Density Polyethylene for 3-D Printing Filament

The growth of desktop 3-D printers is driving an interest in recycled 3-D printer filament to reduce costs of distributed production. Life cycle analysis studies were performed on the recycling of high density polyethylene into filament suitable for additive layer manufacturing with 3-D printers. The conventional centralized recycling system for high population density and low population density rural locations was compared to the proposed in home, distributed recycling system. This system would involve shredding and then producing filament with an open-source plastic extruder from postconsumer plastics and then printing the extruded filament into usable, value-added parts and products with 3-D printers such as the open-source self replicating rapid prototyper, or RepRap. The embodied energy and carbon dioxide emissions were calculated for high density polyethylene recycling using SimaPro 7.2 and the database EcoInvent v2.0. The results showed that distributed recycling uses less embodied energy than the best-case scenario used for centralized recycling. For centralized recycling in a low-density population case study involving substantial embodied energy use for transportation and collection these savings for distributed recycling were found to extend to over 80%. If the distributed process is applied to the U.S. high density polyethylene currently recycled, more than 100 million MJ of energy could be conserved per annum along with the concomitant significant reductions in greenhouse gas emissions. It is concluded that with the open-source 3-D printing network expanding rapidly the potential for widespread adoption of in-home recycling of post-consumer plastic represents a novel path to a future of distributed manufacturing appropriate for both the developed and developing world with lower environmental impacts than the current system.
Semantic Technology

In this paper, we propose Knowledge Graph (KG), an articulated underlying semantic structure, to be a semantic bridge between human and systems. To illustrate our proposal, we focus on KG based intelligent survey systems. In state of the art systems, knowledge is hardcoded or implicit in these systems, making it hard for researchers to reuse, customise, link, or transmit the structured knowledge. Furthermore, such systems do not facilitate dynamic interaction based on the semantic structure. We design and implement a knowledge-driven intelligent survey system which is based on knowledge graph, a widely used technology that facilitates sharing and querying hypotheses, survey content, results, and analyses. The approach is developed, implemented, and tested in the field of Linguistics. Syntacticians and morphologists develop theories of grammar of natural languages. To evaluate theories, they seek intuitive grammaticality (well-formedness) judgments from native speakers, which either support a theory or provide counter-evidence. Our preliminary experiments show that a knowledge graph based linguistic survey can provide more nuanced results than traditional document-based grammaticality judgment surveys by allowing for tagging and manipulation of specific linguistic variables.
BitAV: Fast Anti-Malware by Distributed Blockchain Consensus and Feedforward Scanning

Synopsis—In the age of information, of the Internet, the protection of our most vital infrastructure becomes increasingly important. Moores law continues to prove accurate, with the number of transistors on standard integrated circuits doubling about every two years, but virus scanning applications have not innovated on the same level and development has stagnated. Thus, the attack surfaces become larger and the targets more lucrative, while the defensive mechanisms are failing to improve at a comparable rate. I present the design and implementation of a novel antimalware environment called BitAV. BitAV allows for the decentralization of the update and maintenance mechanisms of the software, traditionally performed by a central host, and uses a staggered scanning mechanism in order to improve performance. The peer-to-peer network maintenance mechanism lowered the average update propagation speed by 500% and is far less susceptible to targeted denialof-service attacks. The feedforward scanning mechanism significantly improved end-to-end performance of the malware matching system, to a degree of an average 14× increase, by decomposing the file matching process into efficient queries that operate in verifiably constant (O(1)) time.
Using four different online media sources to forecast the crude oil price

This study looks for signals of economic awareness on online social media and tests their significance in economic predictions. The study analyzes, over a period of two years, the relationship between West Texas Intermediate crude oil price daily movements and multiple predictors extracted from Twitter, Google Trends, Wikipedia, and the Global Data on Events, Language, and Tone database (GDELT). Semantic analysis is applied to study the sentiment, emotionality, and complexity of the language used. ARIMAX models are used to make predictions and to confirm variable values. Results show that the combined analysis of the four media platforms carries valuable information in financial forecasting. Twitter language complexity, GDELT number of articles and Wikipedia page reads have the highest predictive power. The study also gives evidence of the different speeds at which collective awareness is reflected on different media. In comparison to previous works, more media sources and more dimensions of the interaction and of the language used are combined in a joint analysis.
Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.
Quantifying the Ripple : Word-Of-Mouth and Advertising Effectiveness

In this article the authors demonstrate how a customer lifetime value approach can provide a better assessment of advertising effectiveness that takes into account post purchase behaviors such as word-of-mouth. While for many advertisers word-of-mouth is viewed as an alternative to advertising, the authors show that it is possible to quantify the way in which word-of-mouth often complements and extends the effects of advertising. The authors provide a simple approach to the measurement of post-purchase word-of-mouth sales effects and demonstrate how firms may be underestimating advertising effectiveness by ignoring such effects. Their approach illustrates how customer lifetime value models can provide an important tool to assess the long-term effects of advertising campaigns.
Unconstrained face verification using deep CNN features

In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset as well as on the traditional Labeled Face in the Wild (LFW) dataset. The IJB-A dataset includes real-world unconstrained faces from 500 subjects with full pose and illumination variations which are much harder than the LFW and Youtube Face (YTF) datasets. The deep convolutional neural network (DCNN) is trained using the CASIA-WebFace dataset. Results of experimental evaluations on the IJB-A and the LFW datasets are provided.
Language Specific Issue and Feature Exploration in Chinese Event Extraction

In this paper, we present a Chinese event extraction system. We point out a language specific issue in Chinese trigger labeling, and then commit to discussing the contributions of lexical, syntactic and semantic features applied in trigger labeling and argument labeling. As a result, we achieved competitive performance, specifically, F-measure of 59.9 in trigger labeling and F-measure of 43.8 in argument labeling.
The evolution of innovation management towards contextual innovation

In recent decades, innovation management has changed. This article provides an overview of the changes that have taken place, focusing on innovation management in large companies, with the aim of explaining that innovation management has evolved toward a contextual approach, which it will explain and illustrate using two cases.  The basic approach in this article is to juxtapose a review of existing literature regarding trends in innovation management and research and development (R&D) management generations, and empirical data about actual approaches to innovation.  The idea that there is a single mainstream innovation approach does not match with the (successful) approaches companies have adopted. What is required is a contextual approach. However, research with regard to such an approach is fragmented. Decisions to adapt the innovation management approach to the newness of an innovation or the type of organization respectively have thus far been investigated separately.  An integrated approach is needed to support the intuitive decisions managers make to tailor their innovation approach to the type of innovation, organization(s), industry and country/culture.  The practical and scientific value of this paper is that is describes an integrated approach to contextual innovation.
A Better Way to Attend: Attention With Trees for Video Question Answering

We propose a new attention model for video question answering. The main idea of the attention models is to locate on the most informative parts of the visual data. The attention mechanisms are quite popular these days. However, most existing visual attention mechanisms regard the question as a whole. They ignore the word-level semantics where each word can have different attentions and some words need no attention. Neither do they consider the semantic structure of the sentences. Although the extended soft attention model for video question answering leverages the word-level attention, it performs poorly on long question sentences. In this paper, we propose the heterogeneous tree-structured memory network (HTreeMN) for video question answering. Our proposed approach is based upon the syntax parse trees of the question sentences. The HTreeMN treats the words differently where the visual words are processed with an attention module and the verbal ones not. It also utilizes the semantic structure of the sentences by combining the neighbors based on the recursive structure of the parse trees. The understandings of the words and the videos are propagated and merged from leaves to the root. Furthermore, we build a hierarchical attention mechanism to distill the attended features. We evaluate our approach on two data sets. The experimental results show the superiority of our HTreeMN model over the other attention models, especially on complex questions.
Deep Learning is Robust to Massive Label Noise

Deep neural networks trained on large supervised datasets have led to impressive results in image classification and other tasks. However, wellannotated datasets can be time-consuming and expensive to collect, lending increased interest to larger but noisy datasets that are more easily obtained. In this paper, we show that deep neural networks are capable of generalizing from training data for which true labels are massively outnumbered by incorrect labels. We demonstrate remarkably high test performance after training on corrupted data from MNIST, CIFAR, and ImageNet. For example, on MNIST we obtain test accuracy above 90 percent even after each clean training example has been diluted with 100 randomlylabeled examples. Such behavior holds across multiple patterns of label noise, even when erroneous labels are biased towards confusing classes. We show that training in this regime requires a significant but manageable increase in dataset size that is related to the factor by which correct labels have been diluted. Finally, we provide an analysis of our results that shows how increasing noise decreases the effective batch size.