nils-herrmann/clean_scirepeval huggingface dataset:
Protection from Application Layer DDoS Attacks for Popular Websites

—One of the major threats for the Internet's reliability and stability is Distributed Denial-of-Service (DDoS) attack. The attackers are becoming more sophisticated and organized, also several high-profile attacks targeted prominent Websites. These are the prime reasons that gained importance for the study of DDoS attack detection and prevention. It becomes more undetectable if the legitimate HTTP requests are utilized by Application-layer based DDoS attacks to overwhelm the victim resource. Whenever such an attack occur or mimics the normal flash crowd event of a popular website, then it leads to serious problems. Flash crowd is a situation when a large number of web users are simultaneously accessing a popular Website, which results in a sudden increase of traffic to the website and this may cause the site to be virtually unreachable[1]. Here a mechanism to capture the normal flash crowd event pattern is introduced and the App-DDoS attack monitoring, detection and then blocking of further attack is implemented. An effective method is introduced to identify whether the surge in traffic is caused by normal Web surfers or by App-DDoS attackers. Access Matrix (AM) is defined to detect App-DDoS attacks based on userlogs and threshold value. Hidden Markov model is used to detect App-DDoS attack based on user behavior.
Low-power, folded cascode near rail-to-rail OTA for moderate frequency signal processing

This paper presents a gate-driven, folded-cascode operational transconductance amplifier (FCOTA), operating in strong inversion region. The input core utilizes two complementary PMOS and NMOS input pairs to ensure rail-to-rail input common mode range (ICMR). The cascoded load structure at output port performs the job of current summer and provides enhanced gain due to high output impedance for FCOTA. The proposed OTA has been employed to implement universal biquadratic low-pass, high-pass, band-pass and notch transfer functions, simultaneously available at different nodes of the filter. The proposed FCOTA used dual supply voltage of ± 0.9V and dissipates around 93.6 μW power and provides 80.24 dB open loop gain and gain bandwidth (GBW) of 6.03 MHz. The Cadence VIRTUOSO environment using UMC 0.18 μm CMOS process technology has been used to simulate the proposed circuit.
A self-steering I/Q receiver array in 45-nm CMOS SOI

A novel I/Q receiver array is demonstrated that adapts phase shifts in each receive channel to point a receive beam toward an incident RF signal. The measured array operates at 8.1 GHz and covers steering angles of +/-35 degrees for a four element array. Additionally, the receiver incorporates an I/Q down-converter and demodulates 64QAM with EVM less than 4%. The chip is fabricated in 45 nm CMOS SOI process and occupies an area of 3.45 mm2 while consuming 143 mW dc power.
"With most of it being pictures now, I rarely use it": Understanding Twitter's Evolving Accessibility to Blind Users

Social media is an increasingly important part of modern life. We investigate the use of and usability of Twitter by blind users, via a combination of surveys of blind Twitter users, large-scale analysis of tweets from and Twitter profiles of blind and sighted users, and analysis of tweets containing embedded imagery. While Twitter has traditionally been thought of as the most accessible social media platform for blind users, Twitter's increasing integration of image content and users' diverse uses for images have presented emergent accessibility challenges. Our findings illuminate the importance of the ability to use social media for people who are blind, while also highlighting the many challenges such media currently present this user base, including difficulty in creating profiles, in awareness of available features and settings, in controlling revelations of one's disability status, and in dealing with the increasing pervasiveness of image-based content. We propose changes that Twitter and other social platforms should make to promote fuller access to users with visual impairments.
An operational measure of information leakage

Given two discrete random variables X and Y, an operational approach is undertaken to quantify the “leakage” of information from X to Y. The resulting measure ℒ(X→Y ) is called maximal leakage, and is defined as the multiplicative increase, upon observing Y, of the probability of correctly guessing a randomized function of X, maximized over all such randomized functions. It is shown to be equal to the Sibson mutual information of order infinity, giving the latter operational significance. Its resulting properties are consistent with an axiomatic view of a leakage measure; for example, it satisfies the data processing inequality, it is asymmetric, and it is additive over independent pairs of random variables. Moreover, it is shown that the definition is robust in several respects: allowing for several guesses or requiring the guess to be only within a certain distance of the true function value does not change the resulting measure.
Non-rigid image registration using self-supervised fully convolutional networks without training data

A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms. The image registration is implemented in a multi-resolution image registration framework to jointly optimize and learn spatial transformations and FCNs at different spatial resolutions with deep self-supervision through typical feedforward and backpropagation computation. The proposed method has been evaluated for registering 3D structural brain magnetic resonance (MR) images and obtained better performance than state-of-the-art image registration algorithms.
Validity in qualitative research.

Much contemporary dialogue has centered on the difficulty of establishing validity criteria in qualitative research. Developing validity standards in qualitative research is challenging because of the necessity to incorporate rigor and subjectivity as well as creativity into the scientific process. This article explores the extant issues related to the science and art of qualitative research and proposes a synthesis of contemporary viewpoints. A distinction between primary and secondary validity criteria in qualitative research is made with credibility, authenticity, criticality, and integrity identified as primary validity criteria and explicitness, vividness, creativity, thoroughness, congruence, and sensitivity identified as secondary validity criteria.
Encryption and hash based security in Internet of Things

The Internet of Things (IoT) promises to be the next big revolution of the World Wide Web. It has a very wide range of applications, ranging from smart cities, smart homes, monitoring radiation levels in nuclear plants, animal tracking, health surveillance and a lot more. When nodes in wireless sensor networks are monitored through internet it becomes a part of Internet of Things. This brings in a lot of concerns related to security, privacy, standardization, power management. This paper aims at enhancing security in smart home systems. Devices like thermostat, air conditioners, doors and lighting systems are connected with each other and the internet through the internet of things technologies. Encryption and hash algorithms are proposed in this paper through which devices in the IoT can securely send messages between them. Encryption algorithm is used to ensure confidentiality as the attackers cannot interpret the cipher text that is sent. In order to ensure integrity (cipher text is not changed) hash algorithm is used.
Structural Models of Corporate Bond Pricing : An Empirical Analysis

This article empirically tests five structural models of corporate bond pricing: those of Merton (1974), Geske (1977), Longstaff and Schwartz (1995), Leland and Toft (1996), and Collin-Dufresne and Goldstein (2001). We implement the models using a sample of 182 bond prices from firms with simple capital structures during the period 1986–1997. The conventional wisdom is that structural models do not generate spreads as high as those seen in the bond market, and true to expectations, we find that the predicted spreads in our implementation of the Merton model are too low. However, most of the other structural models predict spreads that are too high on average. Nevertheless, accuracy is a problem, as the newer models tend to severely overstate the credit risk of firms with high leverage or volatility and yet suffer from a spread underprediction problem with safer bonds. The Leland and Toft model is an exception in that it overpredicts spreads on most bonds, particularly those with high coupons. More accurate structural models must avoid features that increase the credit risk on the riskier bonds while scarcely affecting the spreads of the safest bonds.
Is it possible to cure Internet addiction with the Internet?

Significant technological advancements over the last two decades have led to enhanced accessibility to computing devices and the Internet. Our society is experiencing an ever-growing integration of the Internet into everyday lives, and this has transformed the way we obtain and exchange information, communicate and interact with one another as well as conduct business. However, the term ‘Internet addiction’ (IA) has emerged from problematic and excessive Internet usage which leads to the development of addictive cyber-behaviours, causing health and social problems. The most commonly used intervention treatments such as motivational interviewing, cognitive-behavioural therapy, and retreat or inpatient care mix a variety of psychotherapy theories to treat such addictive behaviour and try to address underlying psychosocial issues that are often coexistent with IA, but the efficacy of these approaches is not yet proved. The aim of this paper is to address the question of whether it is possible to cure IA with the Internet. After detailing the current state-of-the-art including various IA definitions, risk factors, assessment methods and IA treatments, we outline the main research challenges that need to be solved. Moreover, we propose an Internet-based IA Recovery Framework (IARF) which uses AI to closely observe, visualize and analyse patient’s Internet usage behaviour for possible staged intervention. The proposal to use smart Internet-based systems to control IA can be expected to be controversial. This paper is intended to stimulate further discussion and research in IA recovery through Internet-based frameworks.
Prediction of driver intended path at intersections

The complexity of situations occurring at intersections is demanding on the cognitive abilities of drivers. Advanced Driver Assistance Systems (ADAS) are intended to assist particularly in those situations. However, for adequate system reaction strategies it is essential to develop situation assessment. Especially the driver's intention has to be estimated. So, the criticality can be inferred and efficient intervention strategies can take action. In this paper, we present a prediction framework based on Hidden Markov Models (HMMs) and analyze its performance using a large database of real driving data. Our focus is on the variation of the model parameters and the choice of the dataset for learning. The direction of travel while approaching a 4-way intersection is to be estimated. A solid prediction is accomplished with high prediction rates above 90% and mean prediction times up to 7 seconds before entering the intersection area.
Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture–on-chip (4C)

The spatial organization of DNA in the cell nucleus is an emerging key contributor to genomic function. We developed 4C technology (chromosome conformation capture (3C)-on-chip), which allows for an unbiased genome-wide search for DNA loci that contact a given locus in the nuclear space. We demonstrate here that active and inactive genes are engaged in many long-range intrachromosomal interactions and can also form interchromosomal contacts. The active β-globin locus in fetal liver preferentially contacts transcribed, but not necessarily tissue-specific, loci elsewhere on chromosome 7, whereas the inactive locus in fetal brain contacts different transcriptionally silent loci. A housekeeping gene in a gene-dense region on chromosome 8 forms long-range contacts predominantly with other active gene clusters, both in cis and in trans, and many of these intra- and interchromosomal interactions are conserved between the tissues analyzed. Our data demonstrate that chromosomes fold into areas of active chromatin and areas of inactive chromatin and establish 4C technology as a powerful tool to study nuclear architecture.
A novel wideband waveguide-to-microstrip transition with waveguide stepped impedance transformer

A novel wideband waveguide-to-microstrip transition is presented in this paper. A printed fan-shaped probe is used, while a waveguide stepped impedance transformer is utilized to broaden the bandwidth further. With an optimized design, a relative bandwidth of 27.8% for -20dB return loss with a center frequency of 15GHz is achieved. The simulated insertion loss in this bandwidth is less than 0.29dB. A back-to-back transition is fabricated and measured. The measured result is similar to that of simulation.
An Improved Location Estimation Method for Wifi Fingerprint-based Indoor Localization

The Accurate indoor localization is a challenging task due to the absence of GPS. Among numerous proposals, Wifi fingerprint-based localization is one of the most promising approach, since most buildings are nowadays equipped with Wifi access points for wireless network coverage. Due to the nature of Wifi access points in which any user can deploy and manage their own, fingerprints from some access points lead to estimation errors. Location estimation algorithms should consider these factors and be able to locate users with low error distance. Finding the nearest neighbor using Euclidean distance in signal space is most widely used method in estimating location. However, this paper shows that Euclidean distance is prone to error when unstable access points are present. Also, Euclidean distance does not differentiate strong signals and weak signals, which can also mislead location estimation. We propose a different way to determine the nearest neighbor, which penalizes signals from unstable access points, and signifies strong signals compared to weak signals. Experiments with real measurements show that the proposed algorithm reduces mean error distance by 57% and 90-percentile error distance by 64% compared to the Euclidean distance method.
Split bolus technique in polytrauma: a prospective study on scan protocols for trauma analysis.

For the evaluation of severely injured trauma patients a variety of total body computed tomography (CT) scanning protocols exist. Frequently multiple pass protocols are used. A split bolus contrast protocol can reduce the number of passes through the body, and thereby radiation exposure, in this relatively young and vitally threatened population.



To evaluate three protocols for single pass total body scanning in 64-slice multidetector CT (MDCT) on optimal image quality.



Three total body CT protocols were prospectively evaluated in three series of 10 consecutive trauma patients. In Group A unenhanced brain and cervical spine CT was followed by chest-abdomen-pelvis CT in portovenous phase after repositioning of the arms. Group B underwent brain CT followed without arm repositioning by a one-volume contrast CT from skull base to the pubic symphysis. Group C was identical to Group A, but the torso was scanned with a split bolus technique. Three radiologists independently evaluated protocol quality scores (5-point Likert scale), parenchymal and vascular enhancement and artifacts.



Overall image quality was good (4.10) in Group A, more than satisfactory (3.38) in Group B, and nearly excellent (4.75) in Group C (P < 0.001). Interfering artifacts were mostly reported in Group B in the liver and spleen.



In single pass total body CT scanning a split bolus technique reached the highest overall image quality compared to conventional total body CT and one-volume contrast CT.
"Everybody knows what you're doing": a critical design approach to personal informatics

We present an alternative approach to the design of personal informatics systems: instead of motivating people to examine their own behaviors, this approach promotes awareness of and reflection on the infrastructures behind personal informatics and the modes of engagement that they promote. Specifically, this paper presents an interface that displays personal web browsing data. The interface aims to reveal underlying infrastructure using several methods: drawing attention to the scope of mined data by displaying deliberately selected sensitive data, using purposeful malfunction as a way to encourage reverse engineering, and challenging normative expectations around data mining by displaying information in unconventional ways. Qualitative results from a two-week deployment show that these strategies can raise people's awareness about data mining, promote efficacy and control over personal data, and inspire reflection on the goals and assumptions embedded in infrastructures for personal data analytics.
Understanding Stories with Large-Scale Common Sense

Story understanding systems need to be able to perform commonsense reasoning, specifically regarding characters’ goals and their associated actions. Some efforts have been made to form large-scale commonsense knowledge bases, but integrating that knowledge into story understanding systems remains a challenge. We have implemented the Aspire system, an application of large-scale commonsense knowledge to story understanding. Aspire extends Genesis, a rule-based story understanding system, with tens of thousands of goalrelated assertions from the commonsense semantic network ConceptNet. ’s knowledge to infer plausible implicit character goals and story causal connections at a scale unprecedented in the space of story understanding. Genesis’s rule-based inference enables precise story analysis, while ConceptNet’s relatively inexact but widely applicable knowledge provides a significant breadth of coverage difficult to achieve solely using rules. Genesis uses Aspire’s inferences to answer questions about stories, and these answers were found to be plausible in a small study. Though we focus on Genesis and ConceptNet, demonstrating the value of supplementing precise reasoning systems with large-scale, scruffy commonsense knowledge is our primary contribution.
Revealing Social Identity Phenomena in Videogames with Archetypal Analysis

In this paper, we present a novel approach toward revealing social identity phenomena in videogames using archetypal analysis (AA). Conventionally used as a dimensionality reduction technique for multivariate data, we demonstrate how AA can reveal social phenomena and inequity such as gender/race-related steretoyping and marginalization in videogame designs. We analyze characters and default attribute distributions of two critically acclaimed and commercially successful videogames (The Elder Scrolls IV: Oblivion and Ultima IV) together with 190 characters created by players in a user-study using a third system of our own design. We show that AA can computationally 1) reveal implicit categorization of characters in videogames (e.g., base player roles and hybrid roles), 2) model real world racial stereotypes and stigma using character attributes (e.g., physically dominant attributes for Oblivion’s ostensibly African-American “Redguard” race) and 3) model gender marginalization and bias (e.g., males characterized as more archetypal representations of each race than females across attributes.) We highlight how AA is an effective approach for computationally modeling identity representations and how it provides a systematic way for the critical assessment of social identity phenomena in videogames.
Method of verifying declared identity in optical answer sheets

In the use of optical answer sheet examinations, occasionally the identity of the examinee is in question. A novel method for characterizing the personal quality of mark shapes in optically read answer sheets is described. The method may be used to identify imposters in multiple choice examinations. All the marks are segmented and measured in multiple parameters including area, dimensions, perimeter and optical density. Imposter decisions are made on the collected data by comparing an identified test form against the form in question in comparison with a population using SVM (support vector machine) modeling. In testing the method, 300 test forms from 100 examinees from past tests were used. An EER (equal error rate) of 15–17% was found. While performance of the presented method is currently insufficient for practical purposes, future research options have been mentioned.
Biodegradable polymeric nanoparticles based drug delivery systems.

Biodegradable nanoparticles have been used frequently as drug delivery vehicles due to its grand bioavailability, better encapsulation, control release and less toxic properties. Various nanoparticulate systems, general synthesis and encapsulation process, control release and improvement of therapeutic value of nanoencapsulated drugs are covered in this review. We have highlighted the impact of nanoencapsulation of various disease related drugs on biodegradable nanoparticles such as PLGA, PLA, chitosan, gelatin, polycaprolactone and poly-alkyl-cyanoacrylates.
Portfolio Selections in P2P Lending: A Multi-Objective Perspective

P2P lending is an emerging wealth-management service for individuals, which allows lenders to directly bid and invest on the loans created by borrowers. In these platforms, lenders often pursue multiple objectives (e.g., non-default probability, fully-funded probability and winning-bid probability) when they select loans to invest. How to automatically assess loans from these objectives and help lenders select loan portfolios is a very important but challenging problem. To that end, in this paper, we present a holistic study on portfolio selections in P2P lending. Specifically, we first propose to adapt gradient boosting decision tree, which combines both static features and dynamic features, to assess loans from multiple objectives. Then, we propose two strategies, i.e., weighted objective optimization strategy and multi-objective optimization strategy, to select portfolios for lenders. For each lender, the first strategy attempts to provide one optimal portfolio while the second strategy attempts to provide a Pareto-optimal portfolio set. Further, we design two algorithms, namely DPA and EVA, which can efficiently resolve the optimizations in these two strategies, respectively. Finally, extensive experiments on a large-scale real-world data set demonstrate the effectiveness of our solutions.
A Two-Iteration Clustering Method to Reveal Unique and Hidden Characteristics of Items Based on Text Reviews

This paper presents a new method for extracting unique features of items based on their textual reviews. The method is built of two similar iterations of applying a weighting scheme and then clustering the resultant set of vectors. In the first iteration, restaurants of similar food genres are grouped together into clusters. The second iteration reduces the importance of common terms in each such cluster, and highlights those that are unique to each specific restaurant. Clustering the restaurants again, now according to their unique features, reveals very interesting connections between the restaurants.