nils-herrmann/clean_scirepeval huggingface dataset:
Direct Biodiesel Production from Wet Microalgae Biomass of Chlorella pyrenoidosa through In Situ Transesterification

A one-step process was applied to directly converting wet oil-bearing microalgae biomass of Chlorella pyrenoidosa containing about 90% of water into biodiesel. In order to investigate the effects of water content on biodiesel production, distilled water was added to dried microalgae biomass to form wet biomass used to produce biodiesel. The results showed that at lower temperature of 90°C, water had a negative effect on biodiesel production. The biodiesel yield decreased from 91.4% to 10.3% as water content increased from 0% to 90%. Higher temperature could compensate the negative effect. When temperature reached 150°C, there was no negative effect, and biodiesel yield was over 100%. Based on the above research, wet microalgae biomass was directly applied to biodiesel production, and the optimal conditions were investigated. Under the optimal conditions of 100 mg dry weight equivalent wet microalgae biomass, 4 mL methanol, 8 mL n-hexane, 0.5 M H2SO4, 120°C, and 180 min reaction time, the biodiesel yield reached as high as 92.5% and the FAME content was 93.2%. The results suggested that biodiesel could be effectively produced directly from wet microalgae biomass and this effort may offer the benefits of energy requirements for biodiesel production.
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease

The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal neuroimaging data, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), has shown its effectiveness for AD diagnosis. The deep polynomial networks (DPN) is a recently proposed deep learning algorithm, which performs well on both large-scale and small-size datasets. In this study, a multimodal stacked DPN (MM-SDPN) algorithm, which MM-SDPN consists of two-stage SDPNs, is proposed to fuse and learn feature representation from multimodal neuroimaging data for AD diagnosis. Specifically speaking, two SDPNs are first used to learn high-level features of MRI and PET, respectively, which are then fed to another SDPN to fuse multimodal neuroimaging information. The proposed MM-SDPN algorithm is applied to the ADNI dataset to conduct both binary classification and multiclass classification tasks. Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.
SoftLight: Adaptive visible light communication over screen-camera links

Screen-camera links for Visible Light Communication (VLC) are diverse, as the link quality varies according to many factors, such as ambient light and camera's performance. This paper presents SoftLight, a channel coding approach that considers the unique channel characteristics of VLC links and automatically adapts the transmission data rate to the link qualities of various scenarios. SoftLight incorporates two new ideas: (1) an expanded color modulation interface that provides soft hint about its confidence in each demodulated bit and establishes a bit-level VLC erasure channel, and (2) a rateless coding scheme that achieves bit-level rateless transmissions with low computation complexity and tolerates the false positive of bits provided by the soft hint enabled erasure channel. SoftLight is orthogonal to the visual coding schemes and can be applied atop any barcode layouts. We implement SoftLight on Android smartphones and evaluate its performance under a variety of environments. The experiment results show that SoftLight can correctly transmit a 22-KByte photo between two smartphones within 0.6 second and improves the average goodput of the state-of-the-art screen-camera VLC solution by 2.2.
Experimental evaluation of cyber attacks on Automatic Generation Control using a CPS Security Testbed

Cyber-Physical Security Testbeds serve as valuable experimental platforms to implement and evaluate realistic, complex cyber attack-defense experiments. Testbeds, unlike traditional simulation platforms, capture communication, control and physical system characteristics and their interdependencies adequately in a unified environment. In this paper, we show how the PowerCyber CPS testbed at Iowa State was used to implement and evaluate cyber attacks on one of the fundamental Wide-Area Control applications, namely, the Automatic Generation Control (AGC). We provide a brief overview of the implementation of the experimental setup on the testbed. We then present a case study using the IEEE 9 bus system to evaluate the impacts of cyber attacks on AGC. Specifically, we analyzed the impacts of measurement based attacks that manipulated the tie-line and frequency measurements, and control based attacks that manipulated the ACE values sent to generators. We found that these attacks could potentially create under frequency conditions and could cause unnecessary load shedding. As part of future work, we plan to extend this work and utilize the experimental setup to implement other sophisticated, stealthy attack vectors and also develop attack-resilient algorithms to detect and mitigate such attacks.
Ontology-based information extraction: An introduction and a survey of current approaches

Information Extraction aims to retrieve certain types of information from natural language text by processing them automatically. For example, an information extraction system might retrieve information about geopolitical indicators of countries from a set of web pages while ignoring other types of information. Ontology-based information extraction has recently emerged as a subfield of information extraction. Here, ontologies which provide formal and explicit specifications of conceptualizations play a crucial role in the information extraction process. Because of the use of ontologies, this field is related to knowledge representation and has the potential to assist the development of the Semantic Web. In this paper, we provide an introduction to ontology-based information extraction and review the details of different ontology-based information extraction systems developed so far. We attempt to identify a common architecture among these systems and classify them based on different factors, which leads to a better understanding on their operation. We also discuss the implementation details of these systems including the tools used by them and the metrics used to measure their performance. In addition, we attempt to identify the possible future directions for this field.
Dynamic Sampling Approach to Training Neural Networks for Multiclass Imbalance Classification

Class imbalance learning tackles supervised learning problems where some classes have significantly more examples than others. Most of the existing research focused only on binary-class cases. In this paper, we study multiclass imbalance problems and propose a dynamic sampling method (DyS) for multilayer perceptrons (MLP). In DyS, for each epoch of the training process, every example is fed to the current MLP and then the probability of it being selected for training the MLP is estimated. DyS dynamically selects informative data to train the MLP. In order to evaluate DyS and understand its strength and weakness, comprehensive experimental studies have been carried out. Results on 20 multiclass imbalanced data sets show that DyS can outperform the compared methods, including pre-sample methods, active learning methods, cost-sensitive methods, and boosting-type methods.
Differential privacy in data publication and analysis

Data privacy has been an important research topic in the security, theory and database communities in the last few decades. However, many existing studies have restrictive assumptions regarding the adversary's prior knowledge, meaning that they preserve individuals' privacy only when the adversary has rather limited background information about the sensitive data, or only uses certain kinds of attacks. Recently, differential privacy has emerged as a new paradigm for privacy protection with very conservative assumptions about the adversary's prior knowledge. Since its proposal, differential privacy had been gaining attention in many fields of computer science, and is considered among the most promising paradigms for privacy-preserving data publication and analysis. In this tutorial, we will motivate its introduction as a replacement for other paradigms, present the basics of the differential privacy model from a database perspective, describe the state of the art in differential privacy research, explain the limitations and shortcomings of differential privacy, and discuss open problems for future research.
An Ensemble Approach for Incremental Learning in Nonstationary Environments

We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presented with a series of training datasets, each of which is drawn from a different snapshot of a distribution that is drifting at an unknown rate. Furthermore, we assume that the algorithm must learn the new environment in an incremental manner, that is, without having access to previously available data. Instead of a time window over incoming instances, or an aged based forgetting – as used by most ensemble based nonstationary learning algorithms – a strategic weighting mechanism is employed that tracks the classifiers’ performances over drifting environments to determine appropriate voting weights. Specifically, the proposed approach generates a single classifier for each dataset that becomes available, and then combines them through a dynamically modified weighted majority voting, where the voting weights themselves are computed as weighted averages of classifiers’ individual performances over all environments. We describe the implementation details of this approach, as well as its initial results on simulated non-stationary environments.
HappyDB: A Corpus of 100, 000 Crowdsourced Happy Moments

The science of happiness is an area of positive psychology concerned with understanding what behaviors make people happy in a sustainable fashion. Recently, there has been interest in developing technologies that help incorporate the findings of the science of happiness into users’ daily lives by steering them towards behaviors that increase happiness. With the goal of building technology that can understand how people express their happy moments in text, we crowd-sourced HappyDB, a corpus of 100,000 happy moments that we make publicly available. This paper describes HappyDB and its properties, and outlines several important NLP problems that can be studied with the help of the corpus. We also apply several state-of-the-art analysis techniques to analyze HappyDB. Our results demonstrate the need for deeper NLP techniques to be developed which makes HappyDB an exciting resource for follow-on research.
Bug characteristics in open source software

To design effective tools for detecting and recovering from software failures requires a deep understanding of software bug characteristics. We study software bug characteristics by sampling 2,060 real world bugs in three large, representative open-source projects—the Linux kernel, Mozilla, and Apache. We manually study these bugs in three dimensions—root causes, impacts, and components. We further study the correlation between categories in different dimensions, and the trend of different types of bugs. The findings include: (1) semantic bugs are the dominant root cause. As software evolves, semantic bugs increase, while memory-related bugs decrease, calling for more research effort to address semantic bugs; (2) the Linux kernel operating system (OS) has more concurrency bugs than its non-OS counterparts, suggesting more effort into detecting concurrency bugs in operating system code; and (3) reported security bugs are increasing, and the majority of them are caused by semantic bugs, suggesting more support to help developers diagnose and fix security bugs, especially semantic security bugs. In addition, to reduce the manual effort in building bug benchmarks for evaluating bug detection and diagnosis tools, we use machine learning techniques to classify 109,014 bugs automatically.
Electronic Voting Service Using Block-Chain

Cryptocurrency, and its underlying technologies, has been gaining popularity for transaction management beyond financial transactions. Transaction information is maintained in the blockchain, which can be used to audit the integrity of the transaction. The focus on this paper is the potential availability of block-chain technology of other transactional uses. Block-chain is one of the most stable open ledgers that preserves transaction information, and is difficult to forge. Since the information stored in block-chain is not related to personally identifiable information, it has the characteristics of anonymity. Also, the block-chain allows for transparent transaction verification since all information in the block-chain is open to the public. These characteristics are the same as the requirements for a voting system. That is, strong robustness, anonymity, and transparency. In this paper, we propose an electronic voting system as an application of blockchain, and describe block-chain based voting at a national level through examples.
Generalized TM Dual-Mode Cavity Filters

A new cavity arrangement, namely, the generalized TM dual-mode cavity, is presented in this paper. In contrast with the previous contributions on TM dual-mode filters, the generalized TM dual-mode cavity allows the realization of both symmetric and asymmetric filtering functions, simultaneously exploiting the maximum number of finite frequency transmission zeros. The high design flexibility in terms of number and position of transmission zeros is obtained by exciting and exploiting a set of nonresonating modes. Five structure parameters are used to fully control its equivalent transversal topology. The relationship between structure parameters and filtering function realized is extensively discussed. The design of multiple cavity filters is presented along with the experimental results of a sixth-order filter having six asymmetrically located transmission zeros.
Survey Paper on Phishing Detection : Identification of Malicious URL Using Bayesian Classification on Social Network Sites

Nowadays people uses online social networking sites for communications with others. People post their message on social network sites. The messages are of various types such as text, images, audio and video. Sometime for these messages the people post their URL and request their friend to visit that site to show the messages. The fraud user uses malicious URL and post on social networking sites. These malicious URL contains viruses which harms user system. Malware is a one of the attack in which fraud URL creates replica of their own when user clicks on these URL and acquire resource. Sometime malicious URL directed towards a website which is a fraud website. These fraud websites are used to steal user’s confidential information. By using Bayesian classification identify the fraud URL on social networking sites and improve the security of social networking sites.
DEMU: A DPDK-based network latency emulator

A network latency emulator allows IT architects to thoroughly investigate how network latencies impact workload performance. Software-based emulation tools have been widely used by researchers and engineers. It is possible to use commodity server computers for emulation and set up an emulation environment quickly without outstanding hardware cost. However, existing software-based tools built in the network stack of an operating system are not capable of supporting the bandwidth of today's standard interconnects (e.g., 10GbE) and emulating sub-milliseconds latencies likely caused by network virtualization in a datacenter. In this paper, we propose a network latency emulator (DEMU) supporting broad bandwidth traffic with sub-milliseconds accuracy, which is based on an emerging packet processing framework, DPDK. It avoids the overhead of the network stack by directly interacting with NIC hardware. Through experiments, we confirmed that DEMU can emulate latencies on the order of 10 µs for short-packet traffic at the line rate of 10GbE. The standard deviation of inserted delays was only 2–3 µs. This is a significant improvement from a network emulator built in the Linux Kernel (i.e., NetEm), which loses more than 50% of its packets for the same 10GbE traffic. For 1 Gbps traffic, the latency deviation of NetEm was approximately 20 µs, while that of our mechanism was 2 orders of magnitude smaller (i.e., only 0.3 µs).
Very High Frame Rate Volumetric Integration of Depth Images on Mobile Devices

Volumetric methods provide efficient, flexible and simple ways of integrating multiple depth images into a full 3D model. They provide dense and photorealistic 3D reconstructions, and parallelised implementations on GPUs achieve real-time performance on modern graphics hardware. To run such methods on mobile devices, providing users with freedom of movement and instantaneous reconstruction feedback, remains challenging however. In this paper we present a range of modifications to existing volumetric integration methods based on voxel block hashing, considerably improving their performance and making them applicable to tablet computer applications. We present (i) optimisations for the basic data structure, and its allocation and integration; (ii) a highly optimised raycasting pipeline; and (iii) extensions to the camera tracker to incorporate IMU data. In total, our system thus achieves frame rates up 47 Hz on a Nvidia Shield Tablet and 910 Hz on a Nvidia GTX Titan XGPU, or even beyond 1.1 kHz without visualisation.
Saliency Based on Information Maximization

A model of bottom-up overt attention is proposed based on the principle of maximizing information sampled from a scene. The proposed operation is based on Shannon's self-information measure and is achieved in a neural circuit, which is demonstrated as having close ties with the circuitry existent in the primate visual cortex. It is further shown that the proposed saliency measure may be extended to address issues that currently elude explanation in the domain of saliency based models. Results on natural images are compared with experimental eye tracking data revealing the efficacy of the model in predicting the deployment of overt attention as compared with existing efforts.
Casual Information Visualization: Depictions of Data in Everyday Life

Information visualization has often focused on providing deep insight for expert user populations and on techniques for amplifying cognition through complicated interactive visual models. This paper proposes a new subdomain for infovis research that complements the focus on analytic tasks and expert use. Instead of work-related and analytically driven infovis, we propose casual information visualization (or casual infovis) as a complement to more traditional infovis domains. Traditional infovis systems, techniques, and methods do not easily lend themselves to the broad range of user populations, from expert to novices, or from work tasks to more everyday situations. We propose definitions, perspectives, and research directions for further investigations of this emerging subfield. These perspectives build from ambient information visualization (Skog et al., 2003), social visualization, and also from artistic work that visualizes information (Viegas and Wattenberg, 2007). We seek to provide a perspective on infovis that integrates these research agendas under a coherent vocabulary and framework for design. We enumerate the following contributions. First, we demonstrate how blurry the boundary of infovis is by examining systems that exhibit many of the putative properties of infovis systems, but perhaps would not be considered so. Second, we explore the notion of insight and how, instead of a monolithic definition of insight, there may be multiple types, each with particular characteristics. Third, we discuss design challenges for systems intended for casual audiences. Finally we conclude with challenges for system evaluation in this emerging subfield.
Grover ’ s quantum searching algorithm is optimal

I show that for any number of oracle lookups up to about π/4 √ N , Grover’s quantum searching algorithm gives the maximal possible probability of finding the desired element. I explain why this is also true for quantum algorithms which use measurements during the computation. I also show that unfortunately quantum searching cannot be parallelized better than by assigning different parts of the search space to independent quantum computers. 1 Quantum searching Imagine we have N cases of which only one fulfills our conditions. E.g. we have a function which gives 1 only for one out of N possible input values and gives 0 otherwise. Often an analysis of the algorithm for calculating the function will allow us to find quickly the input value for which the output is 1. Here we consider the case where we do not know better than to repeatedly calculate the function without looking at the algorithm, e.g. because the function is calculated in a black box subroutine into which we are not allowed to look. In computer science this is called an oracle. Here I consider only oracles which give 1 for exactly one input. Quantum searching for the case with several inputs which give 1 and even with an unknown number of such inputs is treated in . Obviously on a classical computer we have to query the oracle on average N/2 times before we find the answer. Grover  has given a quantum algorithm which can solve the problem in about π/4 √ N steps. Bennett et al.  have shown that asymptotically no quantum algorithm can solve the problem in less than a number of steps proportional to √ N . Boyer et al.  have improved this result to show that e.g. for a 50% success probability no quantum algorithm can do better than only a few percent faster than Grover’s algorithm. I improve ∗Supported by Schweizerischer Nationalfonds and LANL
Collaborative Filtering with Low Regret

There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amounts to online binary matrix completion, where at each time a random user requests a recommendation and the algorithm chooses an entry to reveal in the user's row. The goal is to minimize regret, or equivalently to maximize the number of +1 entries revealed at any time. We analyze an item-item collaborative filtering algorithm that can achieve fundamentally better performance compared to user-user collaborative filtering. The algorithm achieves good "cold-start" performance (appropriately defined) by quickly making good recommendations to new users about whom there is little information.