nils-herrmann/clean_scirepeval huggingface dataset:
ZnO Devices and Applications: A Review of Current Status and Future Prospects

ZnO is an attractive material for applications in electronics, photonics, acoustics, and sensing. In optical emitters, its high exciton binding energy (60 meV) gives ZnO an edge over other semiconductors such as GaN if reproducible and reliable p-type doping in ZnO were to be achieved, which currently remains to be the main obstacle for realization of bipolar devices. On the electronic side, ZnO holds some potential in transparent thin film transistors (TFTs) owing to its high optical transmittivity and high conductivity. Among the other promising areas of application for ZnO are acoustic wave devices, due to large electromechanical coupling in ZnO, and devices utilizing nanowires/nanorods such as biosensors and gas sensors and solar cells, since it is relatively easy to produce such forms of ZnO nanostructures, which have good charge carrier transport properties and high crystalline quality. Despite the significant progress made, there is still a number of important issues that need to be resolved before ZnO can be transitioned to commercial use, not to mention the stiff competition it is facing with GaN, which is much more mature in terms of devices. In this paper, recent progress in device applications of ZnO is discussed and a review of critical issues for realization of ZnO-based devices is given.
Towards better measurement of attention and satisfaction in mobile search

Web Search has seen two big changes recently: rapid growth in mobile search traffic, and an increasing trend towards providing answer-like results for relatively simple information needs (e.g., [weather today]). Such results display the answer or relevant information on the search page itself without requiring a user to click. While clicks on organic search results have been used extensively to infer result relevance and search satisfaction, clicks on answer-like results are often rare (or meaningless), making it challenging to evaluate answer quality. Together, these call for better measurement and understanding of search satisfaction on mobile devices. In this paper, we studied whether tracking the browser viewport (visible portion of a web page) on mobile phones could enable accurate measurement of user attention at scale, and provide good measurement of search satisfaction in the absence of clicks. Focusing on answer-like results in web search, we designed a lab study to systematically vary answer presence and relevance (to the user's information need), obtained satisfaction ratings from users, and simultaneously recorded eye gaze and viewport data as users performed search tasks. Using this ground truth, we identified increased scrolling past answer and increased time below answer as clear, measurable signals of user dissatisfaction with answers. While the viewport may contain three to four results at any given time, we found strong correlations between gaze duration and viewport duration on a per result basis, and that the average user attention is focused on the top half of the phone screen, suggesting that we may be able to scalably and reliably identify which specific result the user is looking at, from viewport data alone.
Yet Another Visit to Paxos

This paper presents a modular introduction to crash-tolerant and Byzantine-tolerant protocols for reaching consensus that use the method introduced by the Paxos algorithm of Lamport and by the viewstamped replication algorithm of Oki and Liskov. The consensus protocol runs a sequence of epoch abstractions as governed by an epoch-change abstraction. Implementations of epoch and epoch-change that tolerate crash faults yield the consensus algorithm in Paxos and in viewstamped replication. Implementations of epoch and epoch-change that tolerate Byzantine faults yield the consensus algorithm in the PBFT protocol of Castro and Liskov.
A Multiagent Modeling and Investigation of Smart Homes With Power Generation, Storage, and Trading Features

Smart homes, as active participants in a smart grid, may no longer be modeled by passive load curves; because their interactive communication and bidirectional power flow within the smart grid affects demand, generation, and electricity rates. To consider such dynamic environmental properties, we use a multiagent-system-based approach in which individual homes are autonomous agents making rational decisions to buy, sell, or store electricity based on their present and expected future amount of load, generation, and storage, accounting for the benefits each decision can offer. In the proposed scheme, home agents prioritize their decisions based on the expected utilities they provide. Smart homes' intention to minimize their electricity bills is in line with the grid's aim to flatten the total demand curve. With a set of case studies and sensitivity analyses, we show how the overall performance of the home agents converges-as an emergent behavior-to an equilibrium benefiting both the entities in different operational conditions and determines the situations in which conventional homes would benefit from purchasing their own local generation-storage systems.
Machine Learning for E-mail Spam Filtering: Review, Techniques and Trends

We present a comprehensive review of the most effective content-based e-mail spam filtering techniques. We focus primarily on Machine Learning-based spam filters and their variants, and report on a broad review ranging from surveying the relevant ideas, efforts, effectiveness, and the current progress. The initial exposition of the background examines the basics of e-mail spam filtering, the evolving nature of spam, spammers playing cat-and-mouse with e-mail service providers (ESPs), and the Machine Learning front in fighting spam. We conclude by measuring the impact of Machine Learning-based filters and explore the promising offshoots of latest developments.
Hybrid precoding for millimeter wave cellular systems with partial channel knowledge

Next-generation cellular standards may leverage the large bandwidth available at millimeter wave (mmWave) frequencies to provide gigabit-per-second data rates in outdoor wireless systems. A main challenge in realizing mmWave cellular is achieving sufficient operating link margin, which is enabled via directional beamforming with large antenna arrays. Due to the high cost and power consumption of high-bandwidth mixed-signal devices, mmWave beamforming will likely include a combination of analog and digital processing. In this paper, we develop an iterative hybrid beamforming algorithm for the single user mmWave channel. The proposed algorithm accounts for the limitations of analog beamforming circuitry and assumes only partial channel knowledge at both the base and mobile stations. The precoding strategy exploits the sparse nature of the mmWave channel and uses a variant of matching pursuit to provide simple solutions to the hybrid beamforming problem. Simulation results show that the proposed algorithm can approach the rates achieved by unconstrained digital beamforming solutions.
Low-resource routing attacks against tor

Tor has become one of the most popular overlay networks for anonymizing TCP traffic. Its popularity is due in part to its perceived strong anonymity properties and its relatively low latency service. Low latency is achieved through Torâ s ability to balance the traffic load by optimizing Tor router selection to probabilistically favor routers with highbandwidth capabilities.
 We investigate how Torâ s routing optimizations impact its ability to provide strong anonymity. Through experiments conducted on PlanetLab, we show the extent to which routing performance optimizations have left the system vulnerable to end-to-end traffic analysis attacks from non-global adversaries with minimal resources. Further, we demonstrate that entry guards, added to mitigate path disruption attacks, are themselves vulnerable to attack. Finally, we explore solutions to improve Torâ s current routing algorithms and propose alternative routing strategies that prevent some of the routing attacks used in our experiments.
MODIFIED MULTILOOK CROSS CORRELATION ( MLCC ) ALGORITHM FOR DOPPLER CENTROID ESTIMATION IN SYNTHETIC APERTURE RADAR SIGNAL PROCESS -

The Multilook Cross Correlation (MLCC) is one of the most reliable algorithms used for Doppler ambiguity number estimation of the Doppler centroid parameter. However, the existing MLCC algorithm is only suitable for low contrast scenes. In high contrast scenes, the estimated result is not reliable, and the error is unacceptable. Besides, the Doppler centroid estimation processing time is long and can only be used in offline processing. In this paper, we introduce a modified MLCC algorithm that has better sensitivity which is suitable not only for low contrast scenes, but also for high contrast scenes. In addition, the modified MLCC algorithm can be implemented on parallel signal processing units for better time efficiency. Experiments with RADARSAT-1 data show that the modified algorithm works well in both high and low contrast scenes.
SEARNN: Training RNNs with Global-Local Losses

We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the “learning to search” (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translation or parsing, and are commonly trained using maximum likelihood estimation (MLE). Unfortunately, this training loss is not always an appropriate surrogate for the test error: by only maximizing the ground truth probability, it fails to exploit the wealth of information offered by structured losses. Further, it introduces discrepancies between training and predicting (such as exposure bias) that may hurt test performance. Instead, SEARNN leverages test-alike search space exploration to introduce global-local losses that are closer to the test error. We demonstrate improved performance over MLE on three different tasks: OCR, spelling correction and text chunking. Finally, we propose a subsampling strategy to enable SEARNN to scale to large vocabulary sizes.
Laplacian Surface Editing

Surface editing operations commonly require geometric details of the surface to be preserved as much as possible. We argue that geometric detail is an intrinsic property of a surface and that, consequently, surface editing is best performed by operating over an intrinsic surface representation. We provide such a representation of a surface, based on the Laplacian of the mesh, by encoding each vertex relative to its neighborhood. The Laplacian of the mesh is enhanced to be invariant to locally linearized rigid transformations and scaling. Based on this Laplacian representation, we develop useful editing operations: interactive free-form deformation in a region of interest based on the transformation of a handle, transfer and mixing of geometric details between two surfaces, and transplanting of a partial surface mesh onto another surface. The main computation involved in all operations is the solution of a sparse linear system, which can be done at interactive rates. We demonstrate the effectiveness of our approach in several examples, showing that the editing operations change the shape while respecting the structural geometric detail.
Power cycling test bench for IGBT power modules used in wind applications

A power cycling test bench is realized for the lifetime analysis of IGBT power modules and for the development of a reliability management system predicting the deterioration of the devices. The focus is on converters for doubly-fed induction generators used in wind turbines. In these applications, due to the low frequent power losses under steady state conditions and due to changing wind conditions, the thermomechanical stress on IGBT power modules is high. The presented test bench offers accelerated ageing of the power semiconductor devices under real load conditions with the phase legs operating from a 1070VDC dc-link allowing sinusoidal load currents up to 800A amplitude at frequencies from 0.1Hz to 13Hz.
Organizational Culture What is Organizational Culture ?

1. Artifacts: These are visible components of culture, they are easy to formulate, have some physical shape, yet its perception varies from one individual to another. 1.  New hire trainings, new hire welcome lunches, annual corporate conferences, awards, offsite meetings and trainings are few examples of most common rituals and ceremonies. 2. Symbols & Slogans: These are high level abstraction of the culture; they effectively summarize organization’s intrinsic behavior. Symbols are rituals, awards or incentives that symbolize preferred behavior; “employee of the month” is one such example of a symbol. Slogans are linguistic phrases that are intended for the same reason, “customer first” is an example of corporate slogan. 3. Stories: These are narratives based on true events, but often exaggerated as it told from old to new employees. The stories of the organization’s founders or other dominant leaders are the most common ones, the challenges they had faced and how they dealt with those hurdles etc. In some form, these are stories of the organization’s heroes, employees relate the current system due to events that had happened in the past and stories are the medium that carries the legacies.
New Hash Functions and Their Use in Authentication and Set Equality

In this paper we exhibit several new classes of hash functions with certain desirable properties, and introduce two novel applications for hashing which make use of these functions. One class contains a small number of functions, yet is almost universal,. If the functions hash n-bit long names into m-bit indices, then specifying a member of the class requires only O((m + log, log,(n)) . log,(n)) bits as compared to O(n) bits for earlier techniques. For long names, this is about a factor of m larger than the lower bound of m + log, n -log, m bits. An application of this class is a provably secure authentication technique for sending messages over insecure lines. A second class of functions satisfies a much stronger property than universal,. We present the application of testing sets for equality. The authentication technique allows the receiver to be certain that a message is genuine. An “enemy”-even one with infinite computer resources-cannot forge or modify a message without detection. The set equality technique allows operations including “add member to set,” “delete member from set” and “test two sets for equality” to be performed in expected constant time and with less than a specified probability of error.
Neurocognitive Architecture of Working Memory

A crucial role for working memory in temporary information processing and guidance of complex behavior has been recognized for many decades. There is emerging consensus that working-memory maintenance results from the interactions among long-term memory representations and basic processes, including attention, that are instantiated as reentrant loops between frontal and posterior cortical areas, as well as sub-cortical structures. The nature of such interactions can account for capacity limitations, lifespan changes, and restricted transfer after working-memory training. Recent data and models indicate that working memory may also be based on synaptic plasticity and that working memory can operate on non-consciously perceived information.
Computational approaches for emotion detection in text

Emotions are part and parcel of human life and among other things, highly influence decision making. Computers have been used for decision making for quite some time now but have traditionally relied on factual information. Recently, interest has been growing among researchers to find ways of detecting subjective information used in blogs and other online social media. This paper presents emotion theories that provide a basis for emotion models. It shows how these models have been used by discussing computational approaches to emotion detection. We propose a hybrid based architecture for emotion detection. The SVM algorithm is used for validating the proposed architecture and achieves a prediction accuracy of 96.43% on web blog data.
An integrated approach to characterize genetic interaction networks in yeast metabolism

Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between ∼185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.
A novel approach to CAD system for the detection of lung nodules in CT images

Detection of pulmonary nodule plays a significant role in the diagnosis of lung cancer in early stage that improves the chances of survival of an individual. In this paper, a computer aided nodule detection method is proposed for the segmentation and detection of challenging nodules like juxtavascular and juxtapleural nodules. Lungs are segmented from computed tomography (CT) images using intensity thresholding; brief analysis of CT image histogram is done to select a suitable threshold value for better segmentation results. Simple morphological closing is used to include juxtapleural nodules in segmented lung regions. K-means clustering is applied for the initial detection and segmentation of potential nodules; shape specific morphological opening is implemented to refine segmentation outcomes. These segmented potential nodules are then divided into six groups on the basis of their thickness and percentage connectivity with lung walls. Grouping not only helped in improving system's efficiency but also reduced computational time, otherwise consumed in calculating and analyzing unnecessary features for all nodules. Different sets of 2D and 3D features are extracted from nodules in each group to eliminate false positives. Small size nodules are differentiated from false positives (FPs) on the basis of their salient features; sensitivity of the system for small nodules is 83.33%. SVM classifier is used for the classification of large nodules, for which the sensitivity of the proposed system is 93.8% applying 10-fold cross-validation. Receiver Operating Characteristic (ROC) curve is used for the analysis of CAD system. Overall sensitivity of the system is 91.65% with 3.19 FPs per case, and accuracy is 96.22%. The system took 3.8 seconds to analyze each image.
A cognitive developmental approach to morality: investigating the psychopath

Various social animal species have been noted to inhibit aggressive attacks when a conspecific displays submission cues. Blair (1993) has suggested that humans possess a functionally similar mechanism which mediates the suppression of aggression in the context of distress cues. He has suggested that this mechanism is a prerequisite for the development of the moral/conventional distinction; the consistently observed distinction in subject's judgments between moral and conventional transgressions. Psychopaths may lack this violence inhibitor. A causal model is developed showing how the lack of this mechanism would explain the core behavioural symptoms associated with the psychopathic disorder. A prediction of such a causal model would be that psychopaths should fail to make the moral/conventional distinction. This prediction was confirmed. The implication of this finding for other theories of morality is discussed.
Benchmarks as Limits to Arbitrage : Understanding the Low Volatility Anomaly ∗

Over the past 41 years, high volatility and high beta stocks have substantially underperformed low volatility and low beta stocks in U.S. markets. We propose an explanation that combines the average investor's preference for risk and the typical institutional investor’s mandate to maximize the ratio of excess returns and tracking error relative to a fixed benchmark (the information ratio) without resorting to leverage. Models of delegated asset management show that such mandates discourage arbitrage activity in both high alpha, low beta stocks and low alpha, high beta stocks. This explanation is consistent with several aspects of the low volatility anomaly including why it has strengthened in recent years even as institutional investors have become more dominant.
Human Age Estimation Based on Locality and Ordinal Information

In this paper, we propose a novel feature selection-based method for facial age estimation. The face aging is a typical temporal process, and facial images should have certain ordinal patterns in the aging feature space. From the geometrical perspective, a facial image can be usually seen as sampled from a low-dimensional manifold embedded in the original high-dimensional feature space. Thus, we first measure the energy of each feature in preserving the underlying local structure information and the ordinal information of the facial images, respectively, and then we intend to learn a low-dimensional aging representation that can maximally preserve both kinds of information. To further improve the performance, we try to eliminate the redundant local information and ordinal information as much as possible by minimizing nonlinear correlation and rank correlation among features. Finally, we formulate all these issues into a unified optimization problem, which is similar to linear discriminant analysis in format. Since it is expensive to collect the labeled facial aging images in practice, we extend the proposed supervised method to a semi-supervised learning mode including the semi-supervised feature selection method and the semi-supervised age prediction algorithm. Extensive experiments are conducted on the FACES dataset, the Images of Groups dataset, and the FG-NET aging dataset to show the power of the proposed algorithms, compared to the state-of-the-arts.
Business Intelligence for E-commerce: Survey and Research Directions

The increase of online shopping has allowed e-commerce to become an usual concept. However, companies still have little knowledge about their customers and the most appropriate and effective processes they should apply to create a perfect fit between costumers’ needs and companies’ offers. To solve this problem, it is important to merge e-commerce with business intelligence, because this would enable to obtain knowledge about e-commerce platforms’ customers, allowing the analysis of customers’ behavior, discovering purchasing patterns, improve relationship management with customer, get better stock management, support to create marketing actions, better financial performance and so forth. This paper provides a review of the literature, suggests new research directions, and proposes an architecture to combine e-commerce with business intelli‐