nils-herrmann/clean_scirepeval huggingface dataset:
DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks

Our daily lives have been immersed in widespread location-based social networks (LBSNs). As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information, often with the intention of influencing users' decisions in urban computing environments. To provide reliable information and improve the experience for legitimate users, we design and implement DeepScan, a malicious account detection system for LBSNs. Different from existing approaches, DeepScan leverages emerging deep learning technologies to learn users' dynamic behavior. In particular, we introduce the long short-term memory (LSTM) neural network to conduct time series analysis of user activities. DeepScan combines newly introduced time series features and a set of conventional features extracted from user activities, and exploits a supervised machine-learning-based model for detection. Using real traces collected from Dianping, a representative LBSN, we demonstrate that DeepScan can achieve excellent prediction performance with an F1-score of 0.964. We also find that the time series features play a critical role in the detection system.
An Interpolating Digitally Controlled Oscillator for a Wide-Range All-Digital PLL

A digitally controlled oscillator (DCO) for the all-digital phase-locked loop (ADPLL) with both the wide frequency range and the high maximum frequency was proposed by using the interpolation scheme at both the coarse and fine delay blocks of the DCO. The coarse block consists of two ladder-shaped coarse delay chains. The delay of the first one is an odd multiple of an inverter delay and that of the second one is an even multiple. An interpolation operation is performed at the second coarse delay chain, which reduces both the resolution of the coarse delay block and the delay range of the fine block to half. This increases the maximum output frequency of the DCO while it maintains the wide frequency range. The ADPLL with the proposed DCO was fabricated in a 0.18 mum CMOS process with the active area of 0.32 mm2 . The measured output frequency of the ADPLL ranges from 33 to 1040 MHz at the supply of 1.8 V. The measured rms and peak-to-peak jitters are 13.8 ps and 86.7 ps, respectively, at the output frequency of 950 MHz. The power consumption is 15.7 mW.
Policy Optimization with Demonstrations

Exploration remains a significant challenge to reinforcement learning methods, especially in environments where reward signals are sparse. Recent methods of learning from demonstrations have shown to be promising in overcoming exploration difficulties but typically require considerable highquality demonstrations that are difficult to collect. We propose to effectively leverage available demonstrations to guide exploration through enforcing occupancy measure matching between the learned policy and current demonstrations, and develop a novel Policy Optimization from Demonstration (POfD) method. We show that POfD induces implicit dynamic reward shaping and brings provable benefits for policy improvement. Furthermore, it can be combined with policy gradient methods to produce state-of-the-art results, as demonstrated experimentally on a range of popular benchmark sparse-reward tasks, even when the demonstrations are few and imperfect.
Gesture control of drone using a motion controller

In this study, we present our implementation of using a motion controller to control the motion of a drone via simple human gestures. We have used the Leap as the motion controller and the Parrot AR DRONE 2.0 for this implementation. The Parrot AR DRONE is an off the shelf quad rotor having an on board Wi-Fi system. The AR DRONE is connected to the ground station via Wi-Fi and the Leap is connected to the ground station via USB port. The LEAP Motion Controller recognizes the hand gestures and relays it on to the ground station. The ground station runs ROS (Robot Operating System) in Linux which is used as the platform for this implementation. Python is the programming language used for interaction with the AR DRONE in order to convey the simple hand gestures. In our implementation, we have written python codes to interpret the hand gestures captured by the LEAP, and transmit them in order to control the motion of the AR DRONE via these gestures.
Microvascular and Macrovascular Complications of Diabetes

Diabetes is a group of chronic diseases characterized by hyperglycemia. Chronic hyperglycemia injures the human body in many different ways. Modern medical care therefore uses a vast array of lifestyle and pharmaceutical interventions aimed at preventing and controlling hyperglycemia. One of the chief injuries arising from hyperglycemia is injury to vasculature, which is classified as either small vascular injury (microvascular disease) or injury to the large blood vessels of the body (macrovascular disease). As medical science advances increasingly toward prevention of complications of diabetes, it is important for clinicians to be familiar with the relationship between diabetes control and vascular injury.
Selective mutism and comorbidity with developmental disorder/delay, anxiety disorder, and elimination disorder.

To assess the comorbidity of developmental disorder/delay in children with selective mutism (SM) and to assess other comorbid symptoms such as anxiety, enuresis, and encopresis.



Subjects with SM and their matched controls were evaluated by a comprehensive assessment of the child and by means of a parental structured diagnostic interview with focus on developmental history. Diagnoses were made according to DSM-IV.



A total of 54 children with SM and 108 control children were evaluated. Of the children with SM, 68.5% met the criteria for a diagnosis reflecting developmental disorder/delay compared with 13.0% in the control group. The criteria for any anxiety diagnosis were met by 74.1% in the SM group and for an elimination disorder by 31.5% versus 7.4% and 9.3%, respectively, in the control group. In the SM group, 46.3% of the children met the criteria for both an anxiety diagnosis and a diagnosis reflecting developmental disorder/delay versus 0.9% in the controls.



SM is associated with developmental disorder/delay nearly as frequently as with anxiety disorders. The mutism may conceal developmental problems in children with SM. Children with SM often meet diagnostic criteria for both a developmental and an anxiety disorder.
Power decoupling control method for an isolated single-phase ac-to-dc converter based on high-frequency cycloconverter topology

This paper proposes a new power decoupling method for a high-frequency cycloconverter. The cycloconverter consists of two half-bridge inverters, two input filter capacitors, and a series-resonant circuit, which enables to convert the single-phase line-frequency ac input to the high-frequency ac output directly. The proposed power decoupling method stores the input power ripple at double the line frequency in the filter capacitors. Therefore, the proposed method achieves a unity power factor in the ac input and a constant amplitude in the high-frequency output without any additional switching device or energy storage element. This paper theoretically discusses the principle and operating performance of the proposed method and confirms the effectiveness of the proposed method in experiments using an isolated ac-to-dc converter based on the high-frequency cycloconverter. As a result, the proposed power decoupling method effectively improved the displacement factor of the ac-input current to more than 0.99 and reduced the voltage ripple in the dc output to 7%.
Cyber Defense Capability Model: A Foundation Taxonomy

Cyber attacks have significantly increased over the last few years, where the attackers are highly skilled, more organized and supported by other powerful actors to devise attacks towards specific targets. To aid the development of a strategic plan to defend against emerging attacks, we present a high-level taxonomy along with a cyber defense model to address the interaction and relationships between taxonomy elements. A cyber-kinetic reference model which is used widely by U.S Air Force is adopted as a baseline for the model and taxonomy development. Asset, Cyber Capability, and Preparation Process are the three high-level elements that are presented for the cyber defense capability model. The Cyber Capability, as the focal point of the study, uses three classifiers to characterize the strategic cyber defense mechanisms, which are classified by active, passive and collaborative defense. To achieve a proper cyber defense strategy, the key actors, assets and associated preparation procedure are identified. Finally, the proposed taxonomy is extensible so that additional dimensions or classifications can be added to future needs.
State of the Art in LP-WAN Solutions for Industrial IoT Services

The emergence of low-cost connected devices is enabling a new wave of sensorization services. These services can be highly leveraged in industrial applications. However, the technologies employed so far for managing this kind of system do not fully cover the strict requirements of industrial networks, especially those regarding energy efficiency. In this article a novel paradigm, called Low-Power Wide Area Networking (LP-WAN), is explored. By means of a cellular-type architecture, LP-WAN-based solutions aim at fulfilling the reliability and efficiency challenges posed by long-term industrial networks. Thus, the most prominent LP-WAN solutions are reviewed, identifying and discussing the pros and cons of each of them. The focus is also on examining the current deployment state of these platforms in Spain. Although LP-WAN systems are at early stages of development, they represent a promising alternative for boosting future industrial IIoT (Industrial Internet of Things) networks and services.
Visual Sensing for Urban Flood Monitoring

With the increasing climatic extremes, the frequency and severity of urban flood events have intensified worldwide. In this study, image-based automated monitoring of flood formation and analyses of water level fluctuation were proposed as value-added intelligent sensing applications to turn a passive monitoring camera into a visual sensor. Combined with the proposed visual sensing method, traditional hydrological monitoring cameras have the ability to sense and analyze the local situation of flood events. This can solve the current problem that image-based flood monitoring heavily relies on continuous manned monitoring. Conventional sensing networks can only offer one-dimensional physical parameters measured by gauge sensors, whereas visual sensors can acquire dynamic image information of monitored sites and provide disaster prevention agencies with actual field information for decision-making to relieve flood hazards. The visual sensing method established in this study provides spatiotemporal information that can be used for automated remote analysis for monitoring urban floods. This paper focuses on the determination of flood formation based on image-processing techniques. The experimental results suggest that the visual sensing approach may be a reliable way for determining the water fluctuation and measuring its elevation and flood intrusion with respect to real-world coordinates. The performance of the proposed method has been confirmed; it has the capability to monitor and analyze the flood status, and therefore, it can serve as an active flood warning system.
An Efficient Method of Number Plate Extraction from Indian Vehicles Image

Automatic Number Plate Recognition (ANPR) is an image-processing technology that identifies vehicles by their number plates without direct human intervention. It is an application of computer vision and important area of research due to its many applications. The main process of ANPR is divided into four stages. This paper presents a simple and efficient method for the extraction of number plate from the vehicle image based on morphological operations, thresholding and sobel edge detection, and the connected component analysis.
Programming challenges of chatbot: Current and future prospective

In the modern Era of technology, Chatbots is the next big thing in the era of conversational services. Chatbots is a virtual person who can effectively talk to any human being using interactive textual skills. Currently, there are many cloud base Chatbots services which are available for the development and improvement of the chatbot sector such as IBM Watson, Microsoft bot, AWS Lambda, Heroku and many others. A virtual person is based on machine learning and Artificial Intelligence (AI) concepts and due to dynamic nature, there is a drawback in the design and development of these chatbots as they have built-in AI, NLP, programming and conversion services. This paper gives an overview of cloud-based chatbots technologies along with programming of chatbots and challenges of programming in current and future Era of chatbot.
Development and Initial Validation of a Multidimensional Scale Assessing Subjective Well-Being: The Well-Being Scale (WeBS).

Numerous scales currently exist that assess well-being, but research on measures of well-being is still advancing. Conceptualization and measurement of subjective well-being have emphasized intrapsychic over psychosocial domains of optimal functioning, and disparate research on hedonic, eudaimonic, and psychological well-being lacks a unifying theoretical model. Lack of systematic investigations on the impact of culture on subjective well-being has also limited advancement of this field. The goals of this investigation were to (1) develop and validate a self-report measure, the Well-Being Scale (WeBS), that simultaneously assesses overall well-being and physical, financial, social, hedonic, and eudaimonic domains of this construct; (2) evaluate factor structures that underlie subjective well-being; and (3) examine the measure's psychometric properties. Three empirical studies were conducted to develop and validate the 29-item scale. The WeBS demonstrated an adequate five-factor structure in an exploratory structural equation model in Study 1. Confirmatory factor analyses showed that a bifactor structure best fit the WeBS data in Study 2 and Study 3. Overall WeBS scores and five domain-specific subscale scores demonstrated adequate to excellent internal consistency reliability and construct validity. Mean differences in overall well-being and its five subdomains are presented for different ethnic groups. The WeBS is a reliable and valid measure of multiple aspects of well-being that are considered important to different ethnocultural groups.
An analysis of the Steam community network evolution

The Steam community network is a large social network of players on the Steam gaming platform, with over 30 million users to date. In this paper we introduce an analysis of the Steam community network in 2011, looking at the characteristics of the users network and the connectivity graph. We next present the evolution of the network over time and show how the network has changed over the years. Last, we analyze the role of games and groups in the Steam community. This work is the first to analyze the Steam network, and to provide a large scale analysis of the characteristics of gaming platforms communities.
Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes

There is a previously identified equivalence between wide fully connected neural networks (FCNs) and Gaussian processes (GPs). This equivalence enables, for instance, test set predictions that would have resulted from a fully Bayesian, infinitely wide trained FCN to be computed without ever instantiating the FCN, but by instead evaluating the corresponding GP. In this work, we derive an analogous equivalence for multi-layer convolutional neural networks (CNNs) both with and without pooling layers, and achieve state of the art results on CIFAR10 for GPs without trainable kernels. We also introduce a Monte Carlo method to estimate the GP corresponding to a given neural network architecture, even in cases where the analytic form has too many terms to be computationally feasible. Surprisingly, in the absence of pooling layers, the GPs corresponding to CNNs with and without weight sharing are identical. As a consequence, translation equivariance in finite-channel CNNs trained with stochastic gradient descent (SGD) has no corresponding property in the Bayesian treatment of the infinite channel limit – a qualitative difference between the two regimes that is not present in the FCN case. We confirm experimentally, that while in some scenarios the performance of SGD-trained finite CNNs approaches that of the corresponding GPs as the channel count increases, with careful tuning SGD-trained CNNs can significantly outperform their corresponding GPs, suggesting advantages from SGD training compared to fully Bayesian parameter estimation.
Introduction to partitioning-based clustering methods with a robust example

Data clustering is an unsupervised data analysis and data mining technique, which offers refined and more abstract views to the inherent structure of a data set by partitioning it into a number of disjoint or overlapping (fuzzy) groups. Hundreds of clustering algorithms have been developed by researchers from a number of different scientific disciplines. The intention of this report is to present a special class of clustering algorithms, namely partition-based methods. After the introduction and a review on iterative relocation clustering algorithms, a new robust partitioning-based method is presented. Also some illustrative results are presented.
Photonic Crystal-Structures for THz Vacuum Electron Devices

The technology of photonic crystals (PhCs) is investigated here to improve the performance of THz vacuum electron devices. Compared with conventional metallic waveguides, the PhC arrangement alleviates typical issues in THz vacuum electron tubes, i.e. difficult vacuum pumping process and assembling, and improves the input/output coupling. A slow-wave structure (SWS) based on a corrugated waveguide assisted by PhC lateral walls and the efficient design of a PhC coupler for sheet-beam interaction devices are demonstrated. Based on the proposed technology, a backward-wave oscillator (BWO) is designed in this paper. Cold parameters of the novel PhC SWS as well as 3-D particle-in-cell simulations of the overall BWO are investigated, obtaining more than 70-mW-peak output power at 0.650 THz for beam voltage of 11 kV and beam current of 6 mA.
Lexical Analysis for the Webshell Attacks

In this paper, we proposed a design considering for web application firewall system. Anyway, the website managing, web technology and hacker technology are vastly different from each other, whilst hacker techs are keep on moving forward, while company and government MIS are difficult of updating their information to the latest state due to working, they are often not updated and followed to the latest stuffs. And moreover, these newest techs are in generally needed to be figured out whether it matches the compatibility of the old versions, such as the recent PHP 7, many MIS are not capable to update their software to handle the variants of WAF bypass methods.
Motion planning based on learning models of pedestrian and driver behaviors

Autonomous driving has shown the capability of providing driver convenience and enhancing safety. While introducing autonomous driving into our current traffic system, one significant issue is to make the autonomous vehicle be able to react in the same way as real human drivers. In order to ensure that an autonomous vehicle of the future will perform like human drivers, this paper proposes a vehicle motion planning model, which can represent how drivers control vehicles based on the assessment of traffic environments in the real signalized intersection. The proposed motion planning model comprises functions of pedestrian intention detection, gap detection and vehicle dynamic control. The three functions are constructed based on the analysis of actual data collected from real traffic environments. Finally, this paper demonstrates the performance of the proposed method by comparing the behaviors of our model with the behaviors of real pedestrians and human drivers. The experimental results show that our proposed model can achieve 85% recognition rate for the pedestrian crossing intention. Moreover, the vehicle controlled by the proposed motion planning model and the actual human-driven vehicle are highly similar with respect to the gap acceptance in intersections.
Hate Speech Detection with Comment Embeddings

We address the problem of hate speech detection in online user comments. Hate speech, defined as an "abusive speech targeting specific group characteristics, such as ethnicity, religion, or gender", is an important problem plaguing websites that allow users to leave feedback, having a negative impact on their online business and overall user experience. We propose to learn distributed low-dimensional representations of comments using recently proposed neural language models, that can then be fed as inputs to a classification algorithm. Our approach addresses issues of high-dimensionality and sparsity that impact the current state-of-the-art, resulting in highly efficient and effective hate speech detectors.
Bloom Filter for Fixed-Size Beacon in VANET

Most work in vehicular ad hoc network focuses on an efficient mechanism to deliver data. These mechanisms need a beacon to exchange all necessary information between neighbors. Although a beacon is a small packet that is periodically broadcast to maintain accuracy, too much information can cause a bulky beacon. This leads to a contention problem due to limited resources in wireless networks. Many approaches are proposed to avoid the problem by reducing the frequency of beacon broadcasting. However, bulky beacons still exist. In this paper, we propose a solution using a Bloom filter to create fixed-size beacons. A single Bloom filter can replace all variable size data structures. Our solution reduces the complexity of a connected dominating set algorithm from O(n^5) to O(n). The evaluation indicates that our proposed solution can significantly reduce the beacon overhead without decreasing the protocol performance.
Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies

Various approaches have been proposed to learn visuo-motor policies for real-world robotic applications. One solution is first learning in simulation then transferring to the real world. In the transfer, most existing approaches need real-world images with labels. However, the labelling process is often expensive or even impractical in many robotic applications. In this paper, we propose an adversarial discriminative sim-to-real transfer approach to reduce the cost of labelling real data. The effectiveness of the approach is demonstrated with modular networks in a table-top object reaching task where a 7 DoF arm is controlled in velocity mode to reach a blue cuboid in clutter through visual observations. The adversarial transfer approach reduced the labelled real data requirement by 50%. Policies can be transferred to real environments with only 93 labelled and 186 unlabelled real images. The transferred visuo-motor policies are robust to novel (not seen in training) objects in clutter and even a moving target, achieving a 97.8% success rate and 1.8 cm control accuracy.