nils-herrmann/clean_scirepeval huggingface dataset:
Addressing Concept-Evolution in Concept-Drifting Data Streams

The problem of data stream classification is challenging because of many practical aspects associated with efficient processing and temporal behavior of the stream. Two such well studied aspects are infinite length and concept-drift. Since a data stream may be considered a continuous process, which is theoretically infinite in length, it is impractical to store and use all the historical data for training. Data streams also frequently experience concept-drift as a result of changes in the underlying concepts. However, another important characteristic of data streams, namely, concept-evolution is rarely addressed in the literature. Concept-evolution occurs as a result of new classes evolving in the stream. This paper addresses concept-evolution in addition to the existing challenges of infinite-length and concept-drift. In this paper, the concept-evolution phenomenon is studied, and the insights are used to construct superior novel class detection techniques. First, we propose an adaptive threshold for outlier detection, which is a vital part of novel class detection. Second, we propose a probabilistic approach for novel class detection using discrete Gini Coefficient, and prove its effectiveness both theoretically and empirically. Finally, we address the issue of simultaneous multiple novel class occurrence, and provide an elegant solution to detect more than one novel classes at the same time. We also consider feature-evolution in text data streams, which occurs because new features (i.e., words) evolve in the stream. Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach.
The Neglected Intrinsic Resistome of Bacterial Pathogens

Bacteria with intrinsic resistance to antibiotics are a worrisome health problem. It is widely believed that intrinsic antibiotic resistance of bacterial pathogens is mainly the consequence of cellular impermeability and activity of efflux pumps. However, the analysis of transposon-tagged Pseudomonas aeruginosa mutants presented in this article shows that this phenotype emerges from the action of numerous proteins from all functional categories. Mutations in some genes make P. aeruginosa more susceptible to antibiotics and thereby represent new targets. Mutations in other genes make P. aeruginosa more resistant and therefore define novel mechanisms for mutation-driven acquisition of antibiotic resistance, opening a new research field based in the prediction of resistance before it emerges in clinical environments. Antibiotics are not just weapons against bacterial competitors, but also natural signalling molecules. Our results demonstrate that antibiotic resistance genes are not merely protective shields and offer a more comprehensive view of the role of antibiotic resistance genes in the clinic and in nature.
A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector

Over the past two decades, human action recognition from video has been an important area of research in computer vision. Its applications include surveillance systems, human–computer interactions and various real-world applications where one of the actor is a human being. A number of review works have been done by several researchers in the context of human action recognition. However, it is found that there is a gap in literature when it comes to methodologies of STIP-based detector for human action recognition. This paper presents a comprehensive review on STIP-based methods for human action recognition. STIP-based detectors are robust in detecting interest points from video in spatio-temporal domain. This paper also summarizes related public datasets useful for comparing performances of various techniques.
Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting

Traffic forecasting is a vital part of intelligent transportation systems. It becomes particularly challenging due to short-term (e.g., accidents, constructions) and long-term (e.g., peak-hour, seasonal, weather) traffic patterns. While most of the previously proposed techniques focus on normal condition forecasting, a single framework for extreme condition traffic forecasting does not exist. To address this need, we propose to take a deep learning approach. We build a deep neural network based on long short term memory (LSTM) units. We apply Deep LSTM to forecast peak-hour traffic and manage to identify unique characteristics of the traffic data. We further improve the model for postaccident forecasting with Mixture Deep LSTM model. It jointly models the normal condition traffic and the pattern of accidents. We evaluate our model on a realworld large-scale traffic dataset in Los Angeles. When trained end-to-end with suitable regularization, our approach achieves 30%-50% improvement over baselines. We also demonstrate a novel technique to interpret the model with signal stimulation. We note interesting observations from the trained neural network.
The Anti-vaccination Movement: A Regression in Modern Medicine

There have been recent trends of parents in Western countries refusing to vaccinate their children due to numerous reasons and perceived fears. While opposition to vaccines is as old as the vaccines themselves, there has been a recent surge in the opposition to vaccines in general, specifically against the MMR (measles, mumps, and rubella) vaccine, most notably since the rise in prominence of the notorious British ex-physician, Andrew Wakefield, and his works. This has caused multiple measles outbreaks in Western countries where the measles virus was previously considered eliminated. This paper evaluates and reviews the origins of the anti-vaccination movement, the reasons behind the recent strengthening of the movement, role of the internet in the spread of anti-vaccination ideas, and the repercussions in terms of public health and safety.
An Edge Based Smart Parking Solution Using Camera Networks and Deep Learning

The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. For urban dwellers, few things are more irritating than anxiously searching for a parking space. Research results show that as much as 30% of traffic is caused by drivers driving around looking for parking spaces in congested city areas. There has been considerable activity among researchers to develop smart technologies that can help drivers find a parking spot with greater ease, not only reducing traffic congestion but also the subsequent air pollution. Many existing solutions deploy sensors in every parking spot to address the automatic parking spot detection problems. However, the device and deployment costs are very high, especially for some large and old parking structures. A wide variety of other technological innovations are beginning to enable more adaptable systems—including license plate number detection, smart parking meter, and vision-based parking spot detection. In this paper, we propose to design a more adaptable and affordable smart parking system via distributed cameras, edge computing, data analytics, and advanced deep learning algorithms. Specifically, we deploy cameras with zoom-lens and motorized head to capture license plate numbers by tracking the vehicles when they enter or leave the parking lot; cameras with wide angle fish-eye lens will monitor the large parking lot via our custom designed deep neural network. We further optimize the algorithm and enable the real-time deep learning inference in an edge device. Through the intelligent algorithm, we can significantly reduce the cost of existing systems, while achieving a more adaptable solution. For example, our system can automatically detect when a car enters the parking space, the location of the parking spot, and precisely charge the parking fee and associate this with the license plate number.
An Improvement on ArUco Marker for Pose Tracking Using Kalman Filter

This paper presents a robust but simple object pose tracking algorithm based on Kalman filtering. Compared to markerless pose tracking, a fiducial marker called ArUco provides a fast and accurate solution to the problem. With these advantages, this marker-based technique is ready to be used in virtual reality and operate in low-cost wearable devices. However, it still suffers from the problem of occlusion and noise. If a large part of the marker is occluded, no pose information can be acquired for the moment. Noises due to hand shaking also affect the quality of the resulting pose. This is not desirable in a real-time environment. We tackle the problems by employing a linear Kalman filter. The pose information can be estimated even if the camera view is blocked temporarily. We have performed real experiments to demonstrate the effects of the application of Kalman filter. The results are satisfactory.
A Novel Phase-Locked Loop Based on Frequency Detector and Initial Phase Angle Detector

This paper proposes a new three-phase software phase-locked loop (PLL) that operates fast and accurately in unbalanced, polluted, and frequency deviating circumstances. The proposed PLL consists of a frequency detector and an initial phase angle detector. In the synchronous reference frame, the initial phase angle detector tracks a ramp phase angle, which is generated from frequency deviation of the inputs, with steady error. This detection error is utilized to estimate the actual grid frequency. Frequency adaptive moving average filters (MAFs) are applied in this new PLL to eliminate noises, harmonics, and negative sequence components. In this paper, the effect of discrete sampling on the MAFs is analyzed, and a linear interpolation is employed to enhance the performances of the MAFs. The stability of the proposed PLL is also analyzed, a sufficient stability condition is identified, and the design procedures of the control parameters are also presented. Simulations and experiments verify the performances of the novel PLL.
Energy Big Data Analytics and Security: Challenges and Opportunities

The limited available fossil fuels and the call for sustainable environment have brought about new technologies for the high efficiency in the use of fossil fuels and introduction of renewable energy. Smart grid is an emerging technology that can fulfill such demands by incorporating advanced information and communications technology (ICT). The pervasive deployment of the advanced ICT, especially the smart metering, will generate big energy data in terms of volume, velocity, and variety. The generated big data can bring huge benefits to the better energy planning, efficient energy generation, and distribution. As such data involve end users' privacy and secure operation of the critical infrastructure, there will be new security issues. This paper is to survey and discuss new findings and developments in the existing big energy data analytics and security. Several taxonomies have been proposed to express the intriguing relationships of various variables in the field.
Smart heterogeneous precision agriculture using wireless sensor network based on extended Kalman filter

Smart heterogeneous precision agriculture (SHPA) using a wireless sensor network is introduced for limited energy sensor nodes, deployed in an agricultural farm which is divided into a number of heterogeneous agricultural areas. At each time step, the extended Kalman filter is adopted to measure and to predict the agricultural parameters including the soil moisture and temperature so that the noise associated with noisy measurements is filtered. After that, sensor node selection algorithm proactively selects the sensor nodes for each individual area to sense the agricultural parameters. Thus, the network lifetime and sensing accuracy are improved. The sampling interval for each crop is predefined based on the crop types and agricultural requirements so that the crop yields are improved. Also, the design, framework, algorithms, and architecture of SHPA are considered and proposed. Compared with other schemes, simulation results show that the proposed SHPA scheme eliminates the noise associated with the measurements, improves the network lifetime and sensing accuracy, and enhances the crop yields.
The Analysis of the Mozart Effect on Visual Search

Over twenty-five years ago, researchers began to study the effects of music on the brain and cognition. The Mozart Effect is a term that was first coined by Dr. Alfred Tomatis in 1991 in his book titled “Porquoi Mozart” in which Tomatis advocated the use of Mozart music as alternative medicine for those suffering from dyslexia, autism, and other learning disorders.  From his observations, Tomatis claimed that Mozart music helped promote healing and could even cure depression. The definition of the term evolved over time; in the present day, the term “Mozart Effect” is typically used to refer to the phenomena of improved spatial reasoning after exposure to Mozart music.
CONSUMERS ' KNOWLEDGE , UNDERSTANDING AND USE OF FOOD LABEL INFORMATION , AND HOW IT AFFECTS PURCHASING DECISION IN HO , GHANA

The main objective of the study was to assess consumers' knowledge, understanding and use of food label information. Data was collected from the capital city of Ho Municipal district in the Volta Region of Ghana using a Survey questionnaire and observation. Participants were adults, aged 18-65 years (n=600; 53%, 47%) randomly selected to assure increased response rate. Observation, reading, knowledge, understanding and use of food labelinformation were the main outcome measures used. Chi–square and Kruskal test were used to assess differences in participants’ characteristics and use of food labeling in food purchase. Findings showed high awareness (P< 0.05) and frequency in food label reading among participants but this did not necessarily influence their purchase of food. Insufficient knowledge, time constraints and small font sizes were reported by participants as the main factors that prevented them from reading and using food label. Although sex (P<0.89) did not influence participants’ reasons for reading labels, age did at P< 0.01. Younger consumers read labels to compare products; older ones did so out of curiosity and for special dietary needs. Knowledge, understanding and use of food label information did not significantly differ with gender but did with age. Understanding food labels through education is needed to promote use among both young and
Working Memory Capacity and Fluid Intelligence: Maintenance and Disengagement.

Working memory capacity and fluid intelligence have been demonstrated to be strongly correlated traits. Typically, high working memory capacity is believed to facilitate reasoning through accurate maintenance of relevant information. In this article, we present a proposal reframing this issue, such that tests of working memory capacity and fluid intelligence are seen as measuring complementary processes that facilitate complex cognition. Respectively, these are the ability to maintain access to critical information and the ability to disengage from or block outdated information. In the realm of problem solving, high working memory capacity allows a person to represent and maintain a problem accurately and stably, so that hypothesis testing can be conducted. However, as hypotheses are disproven or become untenable, disengaging from outdated problem solving attempts becomes important so that new hypotheses can be generated and tested. From this perspective, the strong correlation between working memory capacity and fluid intelligence is due not to one ability having a causal influence on the other but to separate attention-demanding mental functions that can be contrary to one another but are organized around top-down processing goals.
Secure spread spectrum watermarking for multimedia

This paper presents a secure (tamper-resistant) algorithm for watermarking images, and a methodology for digital watermarking that may be generalized to audio, video, and multimedia data. We advocate that a watermark should be constructed as an independent and identically distributed (i.i.d.) Gaussian random vector that is imperceptibly inserted in a spread-spectrum-like fashion into the perceptually most significant spectral components of the data. We argue that insertion of a watermark under this regime makes the watermark robust to signal processing operations (such as lossy compression, filtering, digital-analog and analog-digital conversion, requantization, etc.), and common geometric transformations (such as cropping, scaling, translation, and rotation) provided that the original image is available and that it can be successfully registered against the transformed watermarked image. In these cases, the watermark detector unambiguously identifies the owner. Further, the use of Gaussian noise, ensures strong resilience to multiple-document, or collusional, attacks. Experimental results are provided to support these claims, along with an exposition of pending open problems.
ON THE PRICING OF CORPORATE DEBT : THE RISK STRUCTURE OF INTEREST RATES

On the Pricing of Corporate Debt: The Risk Structure of Interest Rates Author(s): Robert C. Merton Source: The  New York, New York, December 28-30, 1973 (May, 1974), pp. 449-470  Blackwell Publishing for the American Finance Association Stable URL: http://www.jstor.org/stable/2978814 Accessed: 16/10/2008 10:09
Advanced Optical Modulation Formats

Fiber-optic communication systems form the high-capacity transport infrastructure that enables global broadband data services and advanced Internet applications. The desire for higher per-fiber transport capacities and, at the same time, the drive for lower costs per end-to-end transmitted information bit has led to optically routed networks with high spectral efficiencies. Among other enabling technologies, advanced optical modulation formats have become key to the design of modern wavelength division multiplexed (WDM) fiber systems. In this paper, we review optical modulation formats in the broader context of optically routed WDM networks. We discuss the generation and detection of multigigabit/s intensity- and phase-modulated formats, and highlight their resilience to key impairments found in optical networking, such as optical amplifier noise, multipath interference, chromatic dispersion, polarization-mode dispersion, WDM crosstalk, concatenated optical filtering, and fiber nonlinearity
Sneak into Devil's Colony- A study of Fake Profiles in Online Social Networks and the Cyber Law

Massive content about user’s social, personal and professional life stored on Online Social Networks (OSNs) has attracted not only the attention of researchers and social analysts but also the cyber criminals. These cyber criminals penetrate illegally into an OSN by establishing fake profiles or by designing bots and exploit the vulnerabilities of an OSN to carry out illegal activities. With the growth of technology cyber crimes have been increasing manifold. Daily reports of the security and privacy threats in the OSNs demand not only the intelligent automated detection systems that can identify and alleviate fake profiles in real time but also the reinforcement of the security and privacy laws to curtail the cyber crime. In this paper, we have studied various categories of fake profiles like compromised profiles, cloned profiles and online bots (spam-bots, social-bots, like-bots and influential-bots) on different OSN sites along with existing cyber laws to mitigate their threats. In order to design fake profile detection systems, we have highlighted different category of fake profile features which are capable to distinguish different kinds of fake entities from real ones. Another major challenges faced by researchers while building the fake profile detection systems is the unavailability of data specific to fake users. The paper addresses this challenge by providing extremely obliging data collection techniques along with some existing data sources. Furthermore, an attempt is made to present several machine learning techniques employed to design different fake profile detection systems.
Introducing Serendipity in a Content-Based Recommender System

Today recommenders are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/ searched/ visited item and all the items stored in a repository. It is a common belief that the user is interested in what is similar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen. This paper presents the design and implementation of a hybrid recommender system that joins a content-based approach and serendipitous heuristics in order to mitigate the over-specialization problem with surprising suggestions.
Comparative Analysis of ADS-B Verification Techniques

ADS-B is one of many Federal Aviation Administration (FAA) regulated technologies used to monitor air traffic with high precision, while reducing dependencies on dated and costly radar equipment . The FAA hopes to decrease the separation between aircraft, reduce risk of collision as air traffic density increases, save fuel costs, and increase situational awareness of both commercial and general aviation aircraft within United States airspace. Several aviation technology experts have expressed concern over the security of the ADS-B protocol  . ADS-B has an open and well known data format, which is broadcast on known frequencies. This means that the protocol is highly susceptible to radio frequency (RF) attacks such as eavesdropping, jamming, and spoofing. Eavesdropping and jamming will be reviewed in Section 3.4. While eavesdropping and jamming attacks are well studied, due to their applicability in many radio technologies, spoofing attacks against ADS-B are particular to this system. As such, the latter is the focus of our research. This paper evaluates so-called Kalman Filtering and Group Validation techniques (described below) in order to assess which would be a better position verification method of ADS-B signals. The parameters for the comparative analysis include both technical feasibility and practical implementation of each position verification technique. The goal is to offer a practical position verification process which could be implemented with limited government funding within the next 10 years.
Low-Sidelobe Design of Microstrip Comb-Line Antennas Using Stub-Integrated Radiating Elements in the Millimeter-Wave Band

A novel radiating element with a matching circuit was developed for comb-line antennas of traveling-wave operation. In the design of low-sidelobe traveling-wave array antennas, large coupling is required for array elements of the comb-line antenna around the input port. Furthermore, the traveling-wave arrays, whose number of elements is small, also require large coupling because the radiation power for each array element is assigned to be much larger. However, wide radiating elements for large coupling perform poor impedance matching characteristics and radiate large cross polarization. Thus, we proposed a novel radiating element with matching stub in parallel to the radiating element to realize strong radiating power, low reflection, and low cross-polarization, simultaneously. The simulated and measured performances of low-sidelobe 14-element and 6-element antennas were demonstrated in this paper.