nils-herrmann/clean_scirepeval huggingface dataset:
Hyperbolic Feature-based Sarcasm Detection in Tweets: A Machine Learning Approach

In recent times, sarcasm analysis has been one of the toughest challenges in Natural Language Processing (NLP). The property of sarcasm that makes it difficult to analyze and detect is the gap between its literal and intended meaning. Detecting sarcastic sentiment in the domain of social media such as Facebook, Twitter, online blogs, reviews, etc. has become an essential task as they influence every business organization. In this article, a hyperbolic feature-based sarcasm detector for Twitter data is proposed. The hyperbolic features consist of intensifiers and interjections of the text. The performance of the proposed system is analyzed using several standard machine learning approaches namely, Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and AdaBoost. The system attains an accuracy (%) of 75.12, 80.27, 80.67, 80.79, and 80.07 using NB, DT, SVM, RF, and AdaBoost respectively.
On feeling good and getting your way: mood effects on negotiator cognition and bargaining strategies.

Are happy people more likely to be cooperative and successful negotiators? On the basis of the Affect Infusion Model (AIM; Forgas, 1995a). Experiment 1 predicted and found that both good and bad moods had a significant mood-congruent effect on people's thoughts and plans, and on their negotiation strategies and outcomes in both interpersonal and intergroup bargaining. Experiment 2 replicated these results and also showed that mood effects were reduced for persons more likely to adopt motivated processing strategies (scoring high on machiavellianism and need for approval). Experiment 3 confirmed these effects and demonstrated that the mood of the opposition also produced more mood-congruent bargaining strategies and outcomes. The results are discussed in terms of affect priming influences on interpersonal behaviors, and the implications of these findings for real-life cognitive tasks and bargaining encounters are considered.
Social and Information Network Analysis Final Report Reviewer Expertise Ranking Using Link Analysis

User generated online reviews on products or services are valuable to other users to make informed decisions. Thus, identifying experts in reviewers becomes important. We tackle the problem of reviewer expertise ranking in online review datasets by analyzing the bipartite graphs of reviewers and reviewed items, an approach that has not been considered in previous studies of the problem. We use Co-HITS as our primary ranking algorithm, and compare it with other ranking metrics such as degree and pagerank in reviewer graphs. We evaluate the rankings using Kendall rank correlation coefficient, and validate our results against the real datasets.
A 7.1-fJ/Conversion-Step 88-dB SFDR SAR ADC With Energy-Free “Swap To Reset”

The digital-to-analog converter (DAC) in SAR anolog-to-digital converters (ADCs) is often dominant for both power consumption and linearity. Dedicated switching schemes can save power, but mostly focus on conversion energy, whereas the DAC reset can consume significant energy as well. This paper presents an energy-free DAC reset scheme, “swap to reset,” for charge-redistribution SAR ADCs. It is widely applicable to existing low-power switching schemes. Additionally, to limit complexity while maintaining most of the energy savings, it can be utilized for the MSBs of the DAC only while the LSBs use conventional reset. To demonstrate the scheme, it is applied to the 2 MSBs of a 12-b SAR ADC using a split-monotonic DAC in 65-nm CMOS, resulting in an energy saving of 33% for the DAC or 18% for the whole ADC. Besides the “swap to reset,” a rotation is also applied to the 2 MSBs, hence enhancing the linearity to 88-dB spurious free dynamic range. The SAR ADC operates at 0.8-V power supply and 40 kS/s, achieving an signal to noise and distortion ratio of 64.2 dB and a Figure of Merit of 7.1-fJ/conversion step.
Arbuscular mycorrhiza: the mother of plant root endosymbioses

Arbuscular mycorrhiza (AM), a symbiosis between plants and members of an ancient phylum of fungi, the Glomeromycota, improves the supply of water and nutrients, such as phosphate and nitrogen, to the host plant. In return, up to 20% of plant-fixed carbon is transferred to the fungus. Nutrient transport occurs through symbiotic structures inside plant root cells known as arbuscules. AM development is accompanied by an exchange of signalling molecules between the symbionts. A novel class of plant hormones known as strigolactones are exuded by the plant roots. On the one hand, strigolactones stimulate fungal metabolism and branching. On the other hand, they also trigger seed germination of parasitic plants. Fungi release signalling molecules, in the form of 'Myc factors' that trigger symbiotic root responses. Plant genes required for AM development have been characterized. During evolution, the genetic programme for AM has been recruited for other plant root symbioses: functional adaptation of a plant receptor kinase that is essential for AM symbiosis paved the way for nitrogen-fixing bacteria to form intracellular symbioses with plant cells.
Quantum Algorithm Implementations for Beginners

Patrick J. Coles, Stephan Eidenbenz,∗ Scott Pakin, Adetokunbo Adedoyin, John Ambrosiano, Petr Anisimov, William Casper, Gopinath Chennupati, Carleton Coffrin, Hristo Djidjev, David Gunter, Satish Karra, Nathan Lemons, Shizeng Lin, Andrey Lokhov, Alexander Malyzhenkov, David Mascarenas, Susan Mniszewski, Balu Nadiga, Dan O’Malley, Diane Oyen, Lakshman Prasad, Randy Roberts, Phil Romero, Nandakishore Santhi, Nikolai Sinitsyn, Pieter Swart, Marc Vuffray, Jim Wendelberger, Boram Yoon, Richard Zamora, and Wei Zhu Los Alamos National Laboratory, Los Alamos, New Mexico, USA
A 1-V, 16.9 ppm/$^{\circ}$C, 250 nA Switched-Capacitor CMOS Voltage Reference

An ultra low-power, precise voltage reference using a switched-capacitor technique in 0.35-μm CMOS is presented in this paper. The temperature dependence of the carrier mobility and channel length modulation effect can be effectively minimized by using 3.3 and 5 V N-type transistors to operate in the saturation and subthreshold regions, respectively. In place of resistors, a precise reference voltage with flexible trimming capability is achieved by using capacitors. When the supply voltage is 1 V and the temperature is 80°C, the supply current is 250 nA. The line sensitivity is 0.76%/V; the PSRR is -41 dB at 100 Hz and -17 dB at 10 MHz. Moreover, the occupied die area is 0.049 mm2.
122 GHz radar sensor based on a monostatic SiGe-BiCMOS IC with an on-chip antenna

In this paper, a short range monostatic radar sensor operating at 122 GHz is presented. The millimeter-wave frontend, including the on-chip antenna, is based on an integrated circuit realized in SiGe BiCMOS technology. The setup of the radar sensor is introduced and measurement results for FMCW modulation demonstrate the performance of the radar frontend.
A Machine Learning Approach for Product Matching and Categorization Use case : Enriching Product Ads with Semantic Structured Data

Consumers today have the option to purchase products from thousands of e-shops. However, the completeness of the product specifications and the taxonomies used for organizing the products differ across different e-shops. To improve the consumer experience, e.g., by allowing for easily comparing offers by different vendors, approaches for product integration on the Web are needed. In this paper, we present an approach that leverages neural language models and deep learning techniques in combination with standard classification approaches for product matching and categorization. In our approach we use structured product data as supervision for training feature extraction models able to extract attribute-value pairs from textual product descriptions. To minimize the need for lots of data for supervision, we use neural language models to produce word embeddings from large quantities of publicly available product data marked up with Microdata, which boost the performance of the feature extraction model, thus leading to better product matching and categorization performances. Furthermore, we use a deep Convolutional Neural Network to produce image embeddings from product images, which further improve the results on both tasks.
Automated Non-Gaussian Clustering of Polarimetric Synthetic Aperture Radar Images

This paper presents an automatic image segmentation method for polarimetric synthetic aperture radar data. It utilizes the full polarimetric information and incorporates texture by modeling with a non-Gaussian distribution for the complex scattering coefficients. The modeling is based upon the well-known product model, with a Gamma-distributed texture parameter leading to the K-Wishart model for the covariance matrix. The automatic clustering is achieved through a finite mixture model estimated with a modified expectation maximization algorithm. We include an additional goodness-of-fit test stage that allows for splitting and merging of clusters. This not only improves the model fit of the clusters, but also dynamically selects the appropriate number of clusters. The resulting image segmentation depicts the statistically significant clusters within the image. A key feature is that the degree of sub-sampling of the input image will affect the detail level of the clustering, revealing only the major classes or a variable level of detail. Real-world examples are shown to demonstrate the technique.
Neural differences between monolinguals and early bilinguals in their native language during comprehension

Research has shown that semantic processing of sentences engages more activity in the bilingual compared to the monolingual brain and, more specifically, in the inferior frontal gyrus. The present study aims to extend those results and examines whether semantic and also grammatical sentence processing involve different cerebral structures when testing in the native language. In this regard, highly proficient Spanish/Catalan bilinguals and Spanish monolinguals made grammatical and semantic judgments in Spanish while being scanned. Results showed that both types of judgments recruited more cerebral activity for bilinguals in language-related areas including the superior and middle temporal gyri. Such neural differences co-occurred with similar performance at the behavioral level. Taken together, these data suggest that early bilingualism shapes the brain and cognitive processes in sentence comprehension even in their native language; on the other hand, they indicate that brain over activation in bilinguals is not constrained to a specific area.
Sketchable Histograms of Oriented Gradients for Object Detection

In this paper we investigate a new representation approach for visual object recognition. The new representation, called sketchableHoG, extends the classical histogram of oriented gradients (HoG) feature by adding two different aspects: the stability of the majority orientation and the continuity of gradient orientations. In this way, the sketchableHoG locally characterizes the complexity of an object model and introduces global structure information while still keeping simplicity, compactness and robustness. We evaluated the proposed image descriptor on publicly Catltech 101 dataset. The obtained results outperforms classical HoG descriptor as well as other reported descriptors in the literature.
Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation

We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e. a deep neural network. This representation allows for integrating algorithmic planning and deep learning in a principled manner, and thus combine the benefits of model-free and model-based methods. We apply the proposed approach to a challenging partially observable robot navigation task. The robot must navigate to a goal in a previously unseen 3-D environment without knowing its initial location, and instead relying on a 2-D floor map and visual observations from an onboard camera. We introduce the Navigation Networks (NavNets) that encode state estimation, planning and acting in a single, end-to-end trainable recurrent neural network. In preliminary simulation experiments we successfully trained navigation networks to solve the challenging partially observable navigation task.
Constraint-based sequential pattern mining: the pattern-growth methods

Constraints are essential for many sequential pattern mining applications. However, there is no systematic study on constraint-based sequential pattern mining. In this paper, we investigate this issue and point out that the framework developed for constrained frequent-pattern mining does not fit our mission well. An extended framework is developed based on a sequential pattern growth methodology. Our study shows that constraints can be effectively and efficiently pushed deep into the sequential pattern mining under this new framework. Moreover, this framework can be extended to constraint-based structured pattern mining as well.
Biofuels in Brazil

Brazil achieved important success in the implementation of ethanol as a reality renewable energy source after the inception of the National Alcohol Program (PROÁLCOOL) in 1970. Today, ethanol produced from sugarcane replaces almost 50 % of gasoline in Brazil. More than 448 bioethanol production (firstgeneration ethanol) units are functional, which fulfill the 25 % ethanol blending to gasoline that eventually reduces the import of 550 million oil barrels improving the socioeconomic status and saving foreign exchange reserves. Brazil has more than 80 % of its light vehicles running on bioethanol, reducing greenhouse gas emissions. At present, this demand for ethanol is being met through first-generation (1G) ethanol which is directly produced from sugarcane juice and molasses. However, significant research in bioenergy in the last two decades has shown the possibilities of commercialization of second-generation (2G) ethanol, which can be produced from sugarcane bagasse (SB) and straw (SS), complementing 1G ethanol. Nevertheless, both the residues (SB and SS) are an excellent source for cogeneration of heat and power (CHP) in sugarcane processing units. Process simulation studies have provided additional source of information on the overall use of sugarcane for ethanol production and CHP. For the evaluation of the fullest utilization of sugarcane and its by-products, CTBE (Brazilian Bioethanol Science A. Kumar Chandel (&) S. S. da Silva (&) Department of Biotechnology, Engineering School of Lorena, University of São Paulo, Estrada Municipal do Campinho, Lorena, SP 12602-810, Brazil e-mail: anuj.kumar.chandel@gmail.com S. S. da Silva e-mail: silvio@debiq.eel.usp.br T. L. Junqueira E. R. Morais V. L. R. Gouveia O. Cavalett E. C. Rivera V. C. Geraldo A. Bonomi Laboratório Nacional de Ciência e Tecnologia do Bioetanol–CTBE, P. O. Box 6170, Campinas, SP 13083-970, Brazil S. S. da Silva and A. K. Chandel (eds.), Biofuels in Brazil, 1_1, Springer International Publishing Switzerland 2014 1 and Technology Laboratory) has developed the Virtual Sugarcane Biorefinery (VSB), a comprehensive assessment framework to evaluate a sustainability standpoint (economic, environmental, and social), different biorefinery alternatives. This chapter reviews the important insights made into bioethanol production in Brazil. Technical configuration for 1G and 2G ethanol production and sustainability of ethanol (economic and environmental assessment) have also been discussed.
Design and Evaluation of a High-Frequency LTCC Inductor Substrate for a Three-Dimensional Integrated DC/DC Converter

High operating frequency and integration technique are two main approaches to achieve high power density for the switching mode power supply. The emerging gallium nitride (GaN)-based power device enables a multimegahertz high-efficiency point-of-load (POL) converter with high current capability. The low-temperature cofire ceramic (LTCC)-based integration technique successfully extends the three-dimensional (3-D) integrated POL module from the low current level to the high current level (>10 A). This paper presents the low-profile LTCC inductor substrate design and evaluation for a multimegahertz 3-D integrated POL converter with large output current. The detailed study about the impact of frequency on the LTCC inductor shows that the high frequency not only shrinks the volume of the inductor, but also simplifies the inductor structure. The comparison between the LTCC inductor and the discrete inductor demonstrates that the LTCC inductor dramatically boosts the converter light-load efficiency due to its nonlinear inductance. Because of the low-profile design, the power density of the single-phase POL module with LTCC inductor achieves 1.1 kW/in3 at 5 MHz. The performance of the LTCC inductor can be further improved by the inverse coupling, which results in more than 40% core thickness and core loss reduction. Therefore, the power density of a two-phase integrated POL module is pushed to 1.5 kW/in3, which is around ten times of the power density of state-of-the-art industry products with the same current level.
Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model

This paper aims to predict consumer acceptance of e-commerce by proposing a set of key drivers for engaging consumers in on-line transactions. The primary constructs for capturing consumer acceptance of e-commerce are intention to transact and on-line transaction behavior. Following the theory of reasoned action (TRA) as applied to a technology-driven environment, technology acceptance model (TAM) variables (perceived usefulness and ease of use) are posited as key drivers of e-commerce acceptance. The practical utility of TAM stems from the fact that e-commerce is technology-driven. The proposed model integrates trust and perceived risk, which are incorporated given the implicit uncertainty of the e-commerce environment. The proposed integration of the hypothesized independent variables is justified by placing all the variables under the nomological TRA structure and proposing their interrelationships. The resulting research model is tested using data from two empirical studies. The first, exploratory study comprises three experiential scenarios with 103 students. The second, confirmatory study uses a sample of 155 on-line consumers. Both studies strongly support the e-commerce acceptance model by validating the proposed hypotheses. The paper discusses the implications for e-commerce theory, research, and practice, and makes several suggestions for
Performance analysis of downlink NOMA networks over Rayleigh fading channels

This paper analyzes the performance of downlink non-orthogonal multiple-access (NOMA) networks over independent but not necessarily identically distributed Rayleigh fading channels for arbitrary number of network users. Specifically, closed-form expressions for the average SNR, average achievable rate, and outage probability are derived, where it has been shown that network users achieve diversity orders equivalent to their ordered channel gains. Numerical evaluations are presented to validate the derived closed-form expressions for the different performance metrics, which are found to be in agreement with the network simulation results.
Sixth sense technology: Comparisons and future predictions

This paper discusses a technology that is expected to widely spread in the near future, which is the Sixth Sense Technology. With this new technology yet to be introduced in the market, different implementation approaches are discussed. Such approaches are demonstrated through other inventions similar to the Sixth Sense (SS). They all fall into the same category, which is Augmented Reality Technologies. The paper focuses on the possible applications and opportunities of such technology. Furthermore, the implementation approaches are compared and the pros/cons of each approach are explored. Most importantly, technical challenges and open issues regarding each implementation approach are brought out. Furthermore, predictions are made on which approach is expected to succeed in the coming years. Finally, solutions are discussed to improve the Sixth Sense Technology with regards to its implementation approaches to insure new and better ways of human-computer interaction.
Opinion-aware Knowledge Graph for Political Ideology Detection

Identifying individual’s political ideology from their speeches and written texts is important for analyzing political opinions and user behavior on social media. Traditional opinion mining methods rely on bag-of-words representations to classify texts into different ideology categories. Such methods are too coarse for understanding political ideologies. The key to identify different ideologies is to recognize different opinions expressed toward a specific topic. To model this insight, we classify ideologies based on the distribution of opinions expressed towards real-world entities or topics. Specifically, we propose a novel approach to political ideology detection that makes predictions based on an opinion-aware knowledge graph. We show how to construct such graph by integrating the opinions and targeted entities extracted from text into an existing structured knowledge base, and show how to perform ideology inference by information propagation on the graph. Experimental results demonstrate that our method achieves high accuracy in detecting ideologies compared to baselines including LR, SVM and RNN.
A New Round Robin Based Scheduling Algorithm for Operating Systems: Dynamic Quantum Using the Mean Average

Robin, considered as the most widely adopted CPU scheduling algorithm, undergoes severe problems directly related to quantum size. If time quantum chosen is too large, the response time of the processes is considered too high. On the other hand, if this quantum is too small, it increases the overhead of the CPU. In this paper, we propose a new algorithm, called AN, based on a new approach called dynamic-time-quantum; the idea of this approach is to make the operating systems adjusts the time quantum according to the burst time of the set of waiting processes in the ready queue. Based on the simulations and experiments, we show that the new proposed algorithm solves the fixed time quantum problem and increases the performance of Round Robin.
BEHIND ADVERTISING : THE LANGUAGE OF PERSUASION Kenechukwu

Advertising is a force that makes it possible to sell more merchandise due to its persuasive nature. As a tool of marketing communication, advertising is the structured and composed, non-personal communication of information. It is usually paid for and usually persuasive in nature. The language of persuasion is employed for successful advertising campaign thereby, boosting patronage of idea, product or service. The persuasive nature of advertising however, has generated criticisms and controversies. Advertising has been vigorously attacked on the ground that it is unnecessary and wasteful and that through it, people are induced to buy worthless products. It is further argued by the objectors that much advertising is misleading and untruthful. Thus, this paper examines how consumers can be persuaded to patronise goods and services advertised.