nils-herrmann/clean_scirepeval huggingface dataset:
How to Improve Your Search Engine Ranking: Myths and Reality

Search engines have greatly influenced the way people access information on the Internet, as such engines provide the preferred entry point to billions of pages on the Web. Therefore, highly ranked Web pages generally have higher visibility to people and pushing the ranking higher has become the top priority for Web masters. As a matter of fact, Search Engine Optimization (SEO) has became a sizeable business that attempts to improve their clients’ ranking. Still, the lack of ways to validate SEO’s methods has created numerous myths and fallacies associated with ranking algorithms.
 In this article, we focus on two ranking algorithms, Google’s and Bing’s, and design, implement, and evaluate a ranking system to systematically validate assumptions others have made about these popular ranking algorithms. We demonstrate that linear learning models, coupled with a recursive partitioning ranking scheme, are capable of predicting ranking results with high accuracy. As an example, we manage to correctly predict 7 out of the top 10 pages for 78% of evaluated keywords. Moreover, for content-only ranking, our system can correctly predict 9 or more pages out of the top 10 ones for 77% of search terms. We show how our ranking system can be used to reveal the relative importance of ranking features in a search engine’s ranking function, provide guidelines for SEOs and Web masters to optimize their Web pages, validate or disprove new ranking features, and evaluate search engine ranking results for possible ranking bias.
Mathematical modeling of the capacity fade of Li-ion cells

A capacity fade prediction model has been developed for Li-ion cells based on a semi-empirical approach. Correlations for variation of capacity fade parameters with cycling were obtained with two different approaches. The first approach takes into account only the active material loss, while the second approach includes rate capability losses too. Both methods use correlations for variation of the film resistance with cycling. The state of charge (SOC) of the limiting electrode accounts for the active material loss. The diffusion coefficient of the limiting electrode was the parameter to account for the rate capability losses during cycling.
Named Data Networking of Things (Invited Paper)

The Internet of Things (IoT) is a vision for interconnecting all of the world's "things" -- from vehicles to diet scales, smart homes and electrical grids -- through a common set of networking technologies. Realizing this vision using a host-to-host communication paradigm, such as that of the Internet Protocol (IP), is challenging in the context of highly heterogeneous, constrained devices that connect intermittently to one or more networks, often using multiple interfaces, communicate within various security regimes, and require both local and global communication capability. Using IP and similar protocols as the narrow waist of interoperability for IoT requires managing data exchange and security in terms that are largely orthogonal to application semantics, while simultaneously needing to minimize resource usage. This paper explores how Named Data Networking (NDN), a proposed future Internet architecture, addresses the root causes of these challenges and can help achieve the IoT vision in a more secure, straightforward, and innovation-friendly manner. NDN's data-centric communication model aligns network and application semantics, enabling developers to work with "things" and their data directly, and for IoT networks to be deployed and configured easily. To substantiate the high-level discussion, we give examples of ongoing design and implementation work in IoT over NDN and compare the architecture to well-known existing protocols and frameworks. Finally, we discuss short-and long-term scenarios for employing NDN to enable the Internet of Things.
Factorized Hidden Layer Adaptation for Deep Neural Network Based Acoustic Modeling

In this paper, we propose the factorized hidden layer FHL approach to adapt the deep neural network DNN acoustic models for automatic speech recognition ASR. FHL aims at modeling speaker dependent SD hidden layers by representing an SD affine transformation as a linear combination of bases. The combination weights are low-dimensional speaker parameters that can be initialized using speaker representations like i-vectors and then reliably refined in an unsupervised adaptation fashion. Therefore, our method provides an efficient way to perform both adaptive training and test-time adaptation. Experimental results have shown that the FHL adaptation improves the ASR performance significantly, compared to the standard DNN models, as well as other state-of-the-art DNN adaptation approaches, such as training with the speaker-normalized CMLLR features, speaker-aware training using i-vector and learning hidden unit contributions LHUC. For Aurora 4, FHL achieves 3.8% and 2.3% absolute improvements over the standard DNNs trained on the LDA + STC and CMLLR features, respectively. It also achieves 1.7% absolute performance improvement over a system that combines the i-vector adaptive training with LHUC adaptation. For the AMI dataset, FHL achieved 1.4% and 1.9% absolute improvements over the sequence-trained CMLLR baseline systems, for the IHM and SDM tasks, respectively.
Credit scoring in banks and financial institutions via data mining techniques: A literature review

This paper presents a comprehensive review of the studies conducted in the application of data mining techniques focus on credit scoring from 2000 to 2012. Yet, there isn‟t adequate literature reviews in the field of data mining applications in credit scoring. Using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the Science direct online journal database. The studies are categorized and classified into enterprise, individual and small and midsized (SME) companies credit scoring. Data mining techniques are also categorized to single classifier, Hybrid methods and Ensembles. Variable selection methods are also investigated separately because there is a major issue in a credit scoring problem. The findings of this literature review reveals that data mining techniques are mostly applied to an individual credit score and there is inadequate research on enterprise and SME credit scoring. Also ensemble methods, support vector machines and neural network methods are the most favorite techniques used recently. Hybrid methods are investigated in four categories and two of the frequently used combinations are “classification and classification” and “clustering and classification”. This review of literature analysis provides scope for future research and concludes with some helpful suggestions for further research.
Psilocybin induces schizophrenia-like psychosis in humans via a serotonin-2 agonist action.

Psilocybin, an indoleamine hallucinogen, produces a psychosis-like syndrome in humans that resembles first episodes of schizophrenia. In healthy human volunteers, the psychotomimetic effects of psilocybin were blocked dose-dependently by the serotonin-2A antagonist ketanserin or the atypical antipsychotic risperidone, but were increased by the dopamine antagonist and typical antipsychotic haloperidol. These data are consistent with animal studies and provide the first evidence in humans that psilocybin-induced psychosis is due to serotonin-2A receptor activation, independently of dopamine stimulation. Thus, serotonin-2A overactivity may be involved in the pathophysiology of schizophrenia and serotonin-2A antagonism may contribute to therapeutic effects of antipsychotics.
A 16-bit, 125 MS/s, 385 mW, 78.7 dB SNR CMOS Pipeline ADC

A 16-bit 125 MS/s pipeline analog-to-digital converter (ADC) implemented in a 0.18 ¿m CMOS process is presented in this paper. A SHA-less 4-bit front-end is used to achieve low power and minimize the size of the input sampling capacitance in order to ease drivability. The ADC includes foreground factory digital calibration to correct for capacitor mismatches and dithering that can be optionally enabled to improve small-signal linearity. This ADC achieves an SNR of 78.7 dB, an SNDR of 78.6 dB and an SFDR of 96 dB with a 30 MHz input signal, while maintaining an SNR > 76 dB and an SFDR > 85 dB up to 150 MHz input signals. Further, with dithering enabled the worst spur is <-98 dB for inputs below -4 dBFS at 100 MHz IF. The ADC consumes 385 mW from a 1.8 V supply.
The Relationship Between Gender Social Identity and Support for Feminism

This study is an application of social identity theory to feminist consciousness and activism. For women, strong gender identifications may enhance support for equality struggles, whereas for men, they may contribute to backlashes against feminism. University students (N � 276), primarily Euroamerican, completed a measure of gender self-esteem (GSE, that part of one’s selfconcept derived from one’s gender), and two measures of feminism. High GSE in women and low GSE in men were related to support for feminism. Consistent with past research, women were more supportive of feminism than men, and in both genders, support for feminist ideas was greater than self-identification as a feminist.
An Empirical Study into the Security Exposure to Hosts of Hostile Virtualized Environments Tavis

As virtual machines become increasingly commonplace as a method of separating hostile or hazardous code from commodity systems, the potential security exposure from implementation flaws has increased dramatically. This paper investigates the state of popular virtual machine implementations for x86 systems, employing a combination of source code auditing and blackbox random testing to assess the security exposure to the hosts of hostile virtualized environments.
Approximate Computing Using Multiple-Access Single-Charge Associative Memory

Memory-based computing using associative memory is a promising way to reduce the energy consumption of important classes of streaming applications by avoiding redundant computations. A set of frequent patterns that represent basic functions are pre-stored in Ternary Content Addressable Memory (TCAM) and reused. The primary limitation to using associative memory in modern parallel processors is the large search energy required by TCAMs. In TCAMs, all rows that match, except hit rows, precharge and discharge for every search operation, resulting in high energy consumption. In this paper, we propose a new Multiple-Access Single-Charge (MASC) TCAM architecture which is capable of searching TCAM contents multiple times with only a single precharge cycle. In contrast to previous designs, the MASC TCAM keeps the match-line voltage of all miss-rows high and uses their charge for the next search operation, while only the hit rows discharge. We use periodic refresh to control the accuracy of the search. We also implement a new type of approximate associative memory by setting longer refresh times for MASC TCAMs, which yields search results within 1–2 bit Hamming distances of the exact value. To further decrease the energy consumption of MASC TCAM and reduce the area, we implement MASC with crossbar TCAMs. Our evaluation on AMD Southern Island GPU shows that using MASC (crossbar MASC) associative memory can improve the average floating point units energy efficiency by 33.4, 38.1, and 36.7 percent (37.7, 42.6, and 43.1 percent) for exact matching, selective 1-HD and 2-HD approximations respectively, providing an acceptable quality of service (PSNR > 30 dB and average relative error <10 percent). This shows that MASC (crossbar MASC) can achieve 1.77X (1.93X) higher energy savings as compared to the state of the art implementation of GPGPU that uses voltage overscaling on TCAM.
Prosocial behavior in an online game community: an ethnographic study

We report an ethnographic study of prosocial behavior inconnection to League of Legends, one of the most popular games in the world. In this game community, the game developer, Riot Games, implemented a system that allowed players to volunteer their time to identify unacceptable player behaviors and punish players associated with these behaviors. With the prosocial goal of improving the community and promoting sportsmanship with in the competitive culture, a small portion of players worked diligently in the system with little reward. In this paper, we use interviews and analysis of forum discussions to examine how players themselves explain their participation in the system situated in the game culture of League of Legends. We show a myriad of social and technical factors that facilitated or hindered players' prosocial behavior. We discuss how our findings might provide generalizable insights for player engagement and community-building in online games.
CoDraw: Visual Dialog for Collaborative Drawing

In this work, we propose a goal-driven collaborative task that contains vision, language, and action in a virtual environment as its core components. Specifically, we develop a collaborative ‘Image Drawing’ game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art objects. Two players, Teller and Drawer, are involved. The Teller sees an abstract scene containing multiple clip arts in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip arts. The two players communicate via two-way communication using natural language. We collect the CoDraw dataset of ∼10K dialogs consisting of 138K messages exchanged between a Teller and a Drawer from Amazon Mechanical Turk (AMT). We analyze our dataset and present three models to model the players’ behaviors, including an attention model to describe and draw multiple clip arts at each round. The attention models are quantitatively compared to the other models to show how the conventional approaches work for this new task. We also present qualitative visualizations.
Inferring method specifications from natural language API descriptions

Application Programming Interface (API) documents are a typical way of describing legal usage of reusable software libraries, thus facilitating software reuse. However, even with such documents, developers often overlook some documents and build software systems that are inconsistent with the legal usage of those libraries. Existing software verification tools require formal specifications (such as code contracts), and therefore cannot directly verify the legal usage described in natural language text of API documents against the code using that library. However, in practice, most libraries do not come with formal specifications, thus hindering tool-based verification. To address this issue, we propose a novel approach to infer formal specifications from natural language text of API documents. Our evaluation results show that our approach achieves an average of 92% precision and 93% recall in identifying sentences that describe code contracts from more than 2500 sentences of API documents. Furthermore, our results show that our approach has an average 83% accuracy in inferring specifications from over 1600 sentences describing code contracts.
An FPGA-Based Fully Synchronized Design of a Bilateral Filter for Real-Time Image Denoising

In this paper, a detailed description of a synchronous field-programmable gate array implementation of a bilateral filter for image processing is given. The bilateral filter is chosen for one unique reason: It reduces noise while preserving details. The design is described on register-transfer level. The distinctive feature of our design concept consists of changing the clock domain in a manner that kernel-based processing is possible, which means the processing of the entire filter window at one pixel clock cycle. This feature of the kernel-based design is supported by the arrangement of the input data into groups so that the internal clock of the design is a multiple of the pixel clock given by a targeted system. Additionally, by the exploitation of the separability and the symmetry of one filter component, the complexity of the design is widely reduced. Combining these features, the bilateral filter is implemented as a highly parallelized pipeline structure with very economical and effective utilization of dedicated resources. Due to the modularity of the filter design, kernels of different sizes can be implemented with low effort using our design and given instructions for scaling. As the original form of the bilateral filter with no approximations or modifications is implemented, the resulting image quality depends on the chosen filter parameters only. Due to the quantization of the filter coefficients, only negligible quality loss is introduced.
Control and Resonance Damping of Voltage-Source and Current-Source Converters With $LC$ Filters

In this paper, the closed-loop control of both voltage-source converter (VSC) and current-source converter (CSC) systems with LC filters is investigated, with a focus being put on the damping of LC resonance. First, both single-loop and multiloop control schemes for a voltage-source inverter (VSI) with output LC filter are analyzed, where the design and tuning procedure can also be applied to a current-source inverter (CSI) with output CL filter. It is shown that both VSI and CSI systems are subject to LC resonance, even with multiloop control, while the CSI system is also subject to LC resonant frequency variations. On the other hand, the LC resonant frequencies in both current-source rectifier (CSR) and voltage-source rectifier (VSR) systems with input LC filter circuits are relatively fixed due to the small variation range of source impedance. To further dampen the LC resonance in these converter systems, active damping techniques such as virtual harmonic damper and control-signal-shaping methods are investigated, and their implementation into the converter systems is discussed in detail. Experimental results from both a VSC system and a CSC system are obtained.
Extraction of Slum Areas From VHR Imagery Using GLCM Variance

Many cities in the global South are facing the emergence and growth of highly dynamic slum areas, but often lack detailed information on these developments. Available statistical data are commonly aggregated to large, heterogeneous administrative units that are geographically meaningless for informing effective pro-poor policies. General base information neither allows spatially disaggregated analysis of deprived areas nor monitoring of rapidly changing settlement dynamics, which characterize slums. This paper explores the utility of the gray-level co-occurrence matrix (GLCM) variance to distinguish between slums and formal built-up (formal) areas in very high spatial and spectral resolution satellite imagery such as WorldView-2, OrbView, Quickbird, and Resourcesat. Three geographically different cities are selected for this investigation: Mumbai and Ahmedabad, India and Kigali, Rwanda. The exploration of the utility and transferability of the GLCM shows that the variance of the GLCM combined with the normalized difference vegetation index (NDVI) is able to separate slums and formal areas. The overall accuracy achieved is 84% in Kigali, 87% in Mumbai, and 88% in Ahmedabad. Furthermore, combining spectral information with the GLCM variance within a random forest classifier results in a pixel-based classification accuracy of 90%. The final slum map, aggregated to homogenous urban patches (HUPs), shows an accuracy of 88%-95% for slum locations depending on the scale parameter.
Profiting from technological innovation : Implications for integration , collaboration , licensing and public policy

This paper attempts to explain why innovating firms often fail to obtain significant economic returns from an innovation, while customers, imitators and other industry participants benefit. Business strategy particularly as it relates to the firm's decision to integrate and collaborate is shown to be an important factor. The paper demonstrates that when imitation is easy, markets don't work well, and the profits from innovation may accrue to the owners of certain complementary assets, rather than to the developers of the intellectual property. This speaks to the need, in certain cases, for the innovating firm to establish a prior position in these complementary assets. The paper also indicates that innovators with new products and processes which provide value to consumers may sometimes be so ill positioned in the market that they necessarily will fail, The analysis provides a theoretical foundation for the proposition that manufacturing often matters, particularly to innovating nations. Innovating firms without the requisite manufacturing and related capacities may die, even though they are the best at innovation. Implications for trade policy and domestic economic policy are examined.
An anthropomorphic underactuated robotic hand with 15 dofs and a single actuator

This paper presents the design and experimental validation of an anthropomorphic underactuated robotic hand with 15 degrees of freedom and a single actuator. First, the force transmission design of underactuated fingers is revisited. An optimal geometry of the tendon-driven fingers is then obtained. Then, underactuation between the fingers is addressed using differential mechanisms. Tendon routings are proposed and verified experimentally. Finally, a prototype of a 15-degree-of-freedom hand is built and tested. The results demonstrate the feasibility of a humanoid hand with many degrees of freedom and one single degree of actuation.
Deep learning applications and challenges in big data analytics

Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning.
Working knowledge: how organizations manage what they know

KNOWLEDGE is neither data nor information, though it is related to both, and the differences between these terms are often a matter of degree. We start with those more familiar terms both because they are more familiar and because we can understand knowledge best with reference to them. Confusion about what data, information, and knowledge are -how they differ, what those words mean -has resulted in enormous expenditures on technology initiatives that rarely deliver what the firms spending the money needed or thought they were getting. Often firms don't understand what they need until they invest heavily in a system that fails to provide it.