nils-herrmann/clean_scirepeval huggingface dataset:
It's always April fools' day!: On the difficulty of social network misinformation classification via propagation features

Given the huge impact that Online Social Networks (OSN) had in the way people get informed and form their opinion, they became an attractive playground for malicious entities that want to spread misinformation, and leverage their effect. In fact, misinformation easily spreads on OSN, and this is a huge threat for modern society, possibly influencing also the outcome of elections, or even putting people's life at risk (e.g., spreading "anti-vaccines" misinformation). Therefore, it is of paramount importance for our society to have some sort of "validation" on information spreading through OSN. The need for a wide-scale validation would greatly benefit from automatic tools. In this paper, we show that it is difficult to carry out an automatic classification of misinformation considering only structural properties of content propagation cascades. We focus on structural properties, because they would be inherently difficult to be manipulated, with the the aim of circumventing classification systems. To support our claim, we carry out an extensive evaluation on Facebook posts belonging to conspiracy theories (representative of misinformation), and scientific news (representative of fact-checked content). Our findings show that conspiracy content reverberates in a way which is hard to distinguish from scientific content: for the classification mechanism we investigated, classification F-score never exceeds 0.7.
Concreteness ratings for 40 thousand generally known English word lemmas.

Concreteness ratings are presented for 37,058 English words and 2,896 two-word expressions (such as zebra crossing and zoom in), obtained from over 4,000 participants by means of a norming study using Internet crowdsourcing for data collection. Although the instructions stressed that the assessment of word concreteness would be based on experiences involving all senses and motor responses, a comparison with the existing concreteness norms indicates that participants, as before, largely focused on visual and haptic experiences. The reported data set is a subset of a comprehensive list of English lemmas and contains all lemmas known by at least 85 % of the raters. It can be used in future research as a reference list of generally known English lemmas.
Source-Connected p-GaN Gate HEMTs for Increased Threshold Voltage

A pathway to increase the threshold voltage <formula formulatype="inline"><tex Notation="TeX">$(V_{\rm TH})$</tex></formula> of p-GaN gate high-electron-mobility transistors (HEMTs) is presented. The hole depletion width in the p-GaN layer at the gate interface is one of the key controlling factors of <formula formulatype="inline"><tex Notation="TeX">$V_{\rm TH}$</tex></formula> in p-GaN gate HEMTs. In order to increase the depletion width, we devise a new device structure of p-GaN gate HEMT having a source-connected p-GaN bridge. We demonstrate that a bridged p-GaN gate HEMT structure increases the <formula formulatype="inline"><tex Notation="TeX">$V_{\rm TH}$</tex></formula> from 0.93 to 2.44 V.
Time-dependent event hierarchy construction

In this paper, an algorithm called Time Driven Documents-partition (TDD) is proposed to construct an event hierarchy in a text corpus based on a given query. Specifically, assume that a query contains only one feature - Election. Election is directly related to the events such as 2006 US Midterm Elections Campaign, 2004 US Presidential Election Campaign and 2004 Taiwan Presidential Election Campaign, where these events may further be divided into several smaller events (e.g. the 2006 US Midterm Elections Campaign can be broken down into events such as campaign for vote, election results and the resignation of Donald H. Rumsfeld). As such, an event hierarchy is resulted. Our proposed algorithm, TDD, tackles the problem by three major steps: (1)Identify the features that are related to the query according to both the timestamps and the contents of the documents. The features identified are regarded as bursty features; (2) Extract the documents that are highly related to the bursty features based on time; (3) Partition the extracted documents to form events and organize them in a hierarchicalstructure. To the best of our knowledge, there is little works targeting for constructing a feature-based event hierarchy for a text corpus. Practically, event hierarchies can assist us to efficiently locate our target information in a text corpus easily. Again, assume that Election is used for a query. Without an event hierarchy, it is very difficult to identify what are the major events related to it, when do these events happened, as well as the features and the news articles that are related to each of these events. We have archived two-year news articles to evaluate the feasibility of TDD. The encouraging results indicated that TDD is practically sound and highly effective.
Foot Step Based Person Identification Using Histogram Similarity and Wavelet Decomposition

Research in person identification and authentication has attracted significant attention from the researchers and scientists. This paper presents a biometric user authentication based on a person's foot step. The advantage of this recognition method over other types of biometrics is that it enables unobtrusive user authentication where other types of biometrics are not available. Firstly the ground reaction force data was extracted using force plate to gather ground reaction force for individuals. Later we utilized the discrete wavelet transform to de-noise the experimental data and in the final step, histograms were used to identify different person's foot step. The experimental results show improvements in identification accuracies compared to previously reported work.
Design and Optimization a Circular Shape Network Antenna Micro Strip for Some Application

To meet the demands of high speed required by mobile communication of past generations ,one solution is to increase the number of antennas to the show and the reception of the wireless link this is called MIMO (Multiple input ,Multiple output )technology .however ,the integration of multiple antennas on the same PCB is delicate because of the small volume that require some applications and electromagnetic antenna between the coupling ,phenomena that we cannot neglect them .indeed a strong isolation between them has been reached to reduce fading of the signal caused by the electromagnetic antenna reached to reduce fading of the signal caused by the electromagnetic coupling and maximize the overall gain .in this article we are interested then integration on the same printed circuit of eight antennas MIMO are not operation in the same frequency band .the first antenna of this last work at 2.4GHz .other antennas have resonance frequency folling each with 20MHz offset this device is characterized by its original form that keeps is highly isolated antennas from the point of view electromagnetic coupling . INDEX TERMS MIMO, Technology Micro-strip, Microwave, Network Antenna
Coupling CCG and Hybrid Logic Dependency Semantics

Categorial grammar has traditionally used the λ-calculus to represent meaning. We present an alternative, dependency-based perspective on linguistic meaning and situate it in the computational setting. This perspective is formalized in terms of hybrid logic and has a rich yet perspicuous propositional ontology that enables a wide variety of semantic phenomena to be represented in a single meaning formalism. Finally, we show how we can couple this formalization to Combinatory Categorial Grammar to produce interpretations compositionally.
Loop closure detection for visual SLAM systems using convolutional neural network

This paper is concerned of the loop closure detection problem, which is one of the most critical parts for visual Simultaneous Localization and Mapping (SLAM) systems. Most of state-of-the-art methods use hand-crafted features and bag-of-visual-words (BoVW) to tackle this problem. Recent development in deep learning indicates that CNN features significantly outperform hand-crafted features for image representation. This advanced technology has not been fully exploited in robotics, especially in visual SLAM systems. We propose a loop closure detection method based on convolutional neural networks (CNNs). Images are fed into a pre-trained CNN model to extract features. We pre-process CNN features instead of using them directly as most of the presented approaches did before they are used to detect loops. The workflow of extracting CNN features, processing data, computing similarity score and detecting loops is presented. Finally the performance of proposed method is evaluated on several open datasets by comparing it with Fab-Map using precision-recall metric.
Understanding visual perceptions of usability and security of Android's graphical password pattern

This paper reports the results of a user study of the Android graphical password system using an alternative survey methodology, pairwise preferences, that requests participants to select between pairs of patterns indicating either a security or usability preference. By carefully selecting password pairs to isolate a visual feature, a visual perception of usability and security of different features can be measured. We conducted a large IRB-approved survey using pairwise preferences which attracted 384 participants on Amazon Mechanical Turk. Analyzing the results, we find that visual features that can be attributed to complexity indicated a stronger perception of security, while spatial features, such as shifts up/down or left/right are not strong indicators for security or usability. We extended and applied the survey data by building logistic models to predict perception preferences by training on features used in the survey and other features proposed in related work. The logistic model accurately predicted preferences above 70%, twice the rate of random guessing, and the strongest feature in classification is password distance, the total length of all lines in the pattern, a feature not used in the online survey. This result provides insight into the internal visual calculus of users when comparing choices and selecting visual passwords, and the ultimate goal of this work is to leverage the visual calculus to design systems where inherent perceptions for usability coincides with a known metric of security.
Classification of Ordinal Data Using Neural Networks

Many real life problems require the classification of items in naturally ordered classes. These problems are traditionally handled by conventional methods for nominal classes, ignoring the order. This paper introduces a new training model for feedforward neural networks, for multiclass classification problems, where the classes are ordered. The proposed model has just one output unit which takes values in the interval [0,1]; this interval is then subdivided into K subintervals (one for each class), according to a specific probabilistic model. A comparison is made with conventional approaches, as well as with other architectures specific for ordinal data proposed in the literature. The new model compares favourably with the other methods under study, in the synthetic dataset used for evaluation.
PID Controller Tuning Using Evolutionary Algorithms

This paper presents the implementation of PID controller tuning using two sets of evolutionary techniques which are differential evolution (DE) and genetic algorithm (GA). The optimal PID control parameters are applied for a high order system, system with time delay and non-minimum phase system. The performance of the two techniques is evaluated by setting its objective function with mean square error (MSE) and integral absolute error (IAE). Both techniques will compete to achieve the globally minimum value of its objective functions. Meanwhile, reliability between DE and GA in consistently maintaining minimum MSE is also been studied. This paper also compares the performance of the tuned PID controller using GA and DE methods with Ziegler-Nichols method.  Differential Evolution, Genetic Algorithm, PID Controller, Ziegler-Nichols
Usability evaluation for history educational games

The potential for integration of digital games and learning becomes ever more significant recently. One of the goals in educational game design is to create engaging and immersive learning experiences for delivering specified learning goals, outcomes and experiences. However, there is limited number of research done on game usability or quality of game user interface. Failure to design usable game interfaces can interfere with the larger goal of creating a compelling experience for users and can have a negative effect on the overall quality and success of a game. In this paper, we review usability problems identified by previous researchers and propose a history educational game design which includes pedagogical and game design component. Some snapshots of our game module are also presented. Finally we present a usability evaluation method for history educational game design. From our critical literature reviews, we also proposed six constructs which are interface, mechanics, gameplay, playability, feedback and immersion for usability evaluation.
The K-factor : Individual differences in life history strategy

Until recently, variations in life history strategy were studied exclusively at the species level. Although this domain of study has been extended to examine systematic differences in life history strategy among various human ethnic groupings, more recent evolutionary theories of human development and related behavioral genetic work imply substantial within-group individual variation in life history strategy. We constructed a latent variable model identifying a single common factor, denoted as K, which underlies a variety of otherwise disparate life history parameters. This ‘‘K-Factor’’ loaded 0.36 on childhood attachment to the biological father, 0.36 on childhood attachment to any non-biological father figure, 0.38 on adult romantic partner attachment, 0.51 on mating effort, 0.58 on Machiavellianism, and 0.41 on risk propensity. The bivariate correlations of the K-factor with higher-order personality factors were statistically significant, 0.24 with ‘‘Big Neuroticism’’ and 0.67 with ‘‘Big Psychoticism’’, and approached significance, correlating 0.12, with ‘‘Big Extraversion’’. The K-factor appears to be an underappreciated individual difference variable of major importance to human development.
Compact Aspect Embedding for Diversified Query Expansions

Diversified query expansion (DQE) based approaches aim to select a set of expansion terms with less redundancy among them while covering as many query aspects as possible. Recently they have experimentally demonstrate their effectiveness for the task of search result diversification. One challenge faced by existing DQE approaches is to ensure the aspect coverage. In this paper, we propose a novel method for DQE, called compact aspect embedding, which exploits trace norm regularization to learn a low rank vector space for the query, with each eigenvector of the learnt vector space representing an aspect, and the absolute value of its corresponding eigenvalue representing the association strength of that aspect to the query. Meanwhile, each expansion term is mapped into the vector space as well. Based on this novel representation of the query aspects and expansion terms, we design a greedy selection strategy to choose a set of expansion terms to explicitly cover all possible aspects of the query. We test our method on several TREC diversification data sets, and show that our method significantly outperforms the state-of-the-art search result diversification approaches.
Combination therapy : Synergism between natural plant extracts and antibiotics against infectious diseases

Antibiotics are one of the most important weapons in fighting bacterial infections and have greatly benefited the health‐related quality of human life since their introduction. However, over the past few decades these health benefits are under threat as many commonly used antibiotics have become less and less effective against certain illnesses not only because many of them produce toxic reactions but also due to emergence of drug resistant bacteria. Resistance development is an even bigger problem since the bacterial resistance is often not restricted to the specific antibiotic prescribed, but generally extends to other compounds of the same class. Bacterial resistance and its rapid increase is a major concern of global public health and is emerging as one of the most significant challenges to human health. Treating bacterial infections by antibiotics is beneficial but their indiscriminate use has led to an alarming resistance among microorganisms as well as led to re-emergence of old infectious diseases. One approach to treat infectious diseases is the use of plant extracts individually and /or as an alternative approach is the use of combination of antibiotics with plant extracts. This latter approach i.e. combination therapy or synergistic therapy; against resistant microorganisms may lead to new ways of treating infectious diseases and probably this represents a potential area for further future investigations. Combination therapy is helpful and useful for patients with serious infections caused by drug resistant pathogens. The present review describes in detail, the observed synergy between natural extracts and standard antibiotics combating bacterial and fungal infections. The mode of action of combination therapy significantly differs from that of the same drugs acting individually; therefore the selection of an appropriate combination is crucial and essential which requires understanding the potential interaction between the plant extracts and antimicrobial agents.
Sensors for 3D Imaging: Metric Evaluation and Calibration of a CCD/CMOS Time-of-Flight Camera

3D imaging with Time-of-Flight (ToF) cameras is a promising recent technique which allows 3D point clouds to be acquired at video frame rates. However, the distance measurements of these devices are often affected by some systematic errors which decrease the quality of the acquired data. In order to evaluate these errors, some experimental tests on a CCD/CMOS ToF camera sensor, the SwissRanger (SR)-4000 camera, were performed and reported in this paper. In particular, two main aspects are treated: the calibration of the distance measurements of the SR-4000 camera, which deals with evaluation of the camera warm up time period, the distance measurement error evaluation and a study of the influence on distance measurements of the camera orientation with respect to the observed object; the second aspect concerns the photogrammetric calibration of the amplitude images delivered by the camera using a purpose-built multi-resolution field made of high contrast targets.
Smart Detection and Transmission of Abnormalities in ECG via Bluetooth

Technology plays the major role in healthcare not only for sensory devices but also in data transfer accordingly and displaying it. Networked sensors, either worn on the body or embedded in our living environment, makes it possible for gathering rich information indicative of our physical and mental health in a smart approach. In this paper the Arduino based microcontroller is used as a gateway to communicate to the various sensors such as temperature sensor, ECG Sensor and pulse oximeter sensor etc. The microcontroller picks up the sensor data and sends it to the network through Bluetooth using Bluetooth module interfaced to Arduino. This paper mainly explains about detection of abnormalities in heart through specially designed ECG circuit and transmission of data to smart phone via Bluetooth. Hence provides real time monitoring of the health care parameters for doctors and care takers where data can be accessed anytime. This system is efficient with low power consumption capability, easy setup, high performance and time to time response. This paper explains in brief how IoT functions and how it is used in conjunction with wireless and sensing techniques to implement the desired healthcare applications.
EvoNN: a customizable evolutionary neural network with heterogenous activation functions

While some attention has been given to the process of using Evolutionary Computation (EC) to optimize the activation functions within hidden layers, available activation function sets have always been hard coded by the developers, and were immutable by users.
 In this paper, we present EvoNN. While many other Neuro-evolution based tools or algorithms focus primarily on the evolution of either Neural Network (NN) architecture, or its weights, EvoNN focuses on simultaneous evolution of weights and the activation functions within hidden layers. The main novely offered by EvoNN lies in that users can provide additional activation functions to the EvoNN system to be employed as part of the "alphabet" of available functions. This feature gives users a greater degree of flexibility over which functions the evolutionary optimizer can utilize.
 We employ a set of three test cases where we compare EvoNN to a standard NN, and observe encouraging results showing a superior performance of the EvoNN system. We also observe this increase in performance comes at the cost of additional run time, but note that for some applications, this can be a worthwhile trade-off.
Quantitative analysis of small-plastic debris on beaches in the Hawaiian Archipelago.

Small-plastic beach debris from nine coastal locations throughout the Hawaiian Archipelago was analyzed. At each beach, replicate 20 l samples of sediment were collected, sieved for debris between 1 and 15 mm in size, sorted by type, counted and weighed. Small-plastic debris occurred on all of the beaches, but the greatest quantity was found at three of the most remote beaches on Midway Atoll and Moloka'i. Of the debris analyzed, 72% by weight was plastic. A total of 19100 pieces of plastic were collected from the nine beaches, 11% of which was pre-production plastic pellets. This study documents for the first time the presence of small-plastic debris on Hawaiian beaches and corroborates estimates of the abundance of plastics in the marine environment in the North Pacific.
An improved categorization of classifier's sensitivity on sample selection bias

A recent paper categorizes classifier learning algorithms according to their sensitivity to a common type of sample selection bias where the chance of an example being selected into the training sample depends on its feature vector x but not (directly) on its class label y. A classifier learner is categorized as "local" if it is insensitive to this type of sample selection bias, otherwise, it is considered "global". In that paper, the true model is not clearly distinguished from the model that the algorithm outputs. In their discussion of Bayesian classifiers, logistic regression and hard-margin SVMs, the true model (or the model that generates the true class label for every example) is implicitly assumed to be contained in the model space of the learner, and the true class probabilities and model estimated class probabilities are assumed to asymptotically converge as the training data set size increases. However, in the discussion of naive Bayes, decision trees and soft-margin SVMs, the model space is assumed not to contain the true model, and these three algorithms are instead argued to be "global learners". We argue that most classifier learners may or may not be affected by sample selection bias; this depends on the dataset as well as the heuristics or inductive bias implied by the learning algorithm and their appropriateness to the particular dataset.