nils-herrmann/clean_scirepeval huggingface dataset:
HS2: Active Learning over Hypergraphs

We propose a hypergraph-based active learning scheme which we term HS; HS generalizes the previously reported algorithm S originally proposed for graph-based active learning with pointwise queries . Our HS method can accommodate hypergraph structures and allows one to ask both pointwise queries and pairwise queries. Based on a novel parametric system particularly designed for hypergraphs, we derive theoretical results on the query complexity of HS for the above described generalized settings. Both the theoretical and empirical results show that HS requires a significantly fewer number of queries than S when one uses S over a graph obtained from the corresponding hypergraph via clique expansion.
REVIEW OF FATIGUE DETECTION AND PREDICTION TECHNOLOGIES September 2000 Prepared

This report reviews existing fatigue detection and prediction technologies. Data regarding the different technologies available were collected from a wide variety of worldwide sources. The first half of this report summarises the current state of research and development of the technologies and summarises the status of the technologies with respect to the key issues of sensitivity, reliability, validity and acceptability. The second half evaluates the role of the technologies in transportation, and comments on the place of the technologies vis-a-vis other enforcement and regulatory frameworks, especially in Australia and New Zealand. The report authors conclude that the hardware technologies should never be used as the company fatigue management system. Hardware technologies only have the potential to be a last ditch safety device. Nevertheless, the output of hardware technologies could usefully feed into company fatigue management systems to provide real time risk assessment. However, hardware technology output should never be the only input into a management system. Other inputs should at least come from validated software technologies, mutual assessment of fitness for duty and other risk assessments of work load, schedules and rosters.  For information: to provide an understanding of the place of fatigue detection and prediction technologies in the management of fatigue in drivers of heavy vehicles.
Quantity and Quality Aware Artificial Bee Colony Algorithm for Clustering

Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence technique for clustering. It produces higher quality clusters compared to other population-based algorithms but with poor energy efficiency, cluster quality consistency and typically slower in convergence speed. Inspired by energy saving foraging behavior of natural honey bees this paper presents a Quality and Quantity Aware Artificial Bee Colony (QABC) algorithm to improve quality of cluster identification, energy efficiency and convergence speed of the original ABC. To evaluate the performance of QABC algorithm, experiments were conducted on a suite of ten benchmark UCI datasets. The results demonstrate QABC outperformed ABC and K-means algorithm in the quality of clusters delivered. Artificial bee colony algorithm, clustering.
Gender differences in participation and reward on Stack Overflow

Programming is a valuable skill in the labor market, making the underrepresentation of women in computing an increasingly important issue. Online question and answer platforms serve a dual purpose in this field: they form a body of knowledge useful as a reference and learning tool, and they provide opportunities for individuals to demonstrate credible, verifiable expertise. Issues, such as male-oriented site design or overrepresentation of men among the site’s elite may therefore compound the issue of women’s underrepresentation in IT. In this paper we audit the differences in behavior and outcomes between men and women on Stack Overflow, the most popular of these Q&A sites. We observe significant differences in how men and women participate in the platform and how successful they are. For example, the average woman has roughly half of the reputation points, the primary measure of success on the site, of the average man. Using an Oaxaca-Blinder decomposition, an econometric technique commonly applied to analyze differences in wages between groups, we find that most of the gap in success between men and women can be explained by differences in their activity on the site and differences in how these activities are rewarded. Specifically, 1) men give more answers than women and 2) are rewarded more for their answers on average, even when controlling for possible confounders such as tenure or buy-in to the site. Women ask more questions and gain more reward per question. We conclude with a hypothetical redesign of the site’s scoring system based on these behavioral differences, cutting the reputation gap in half.
Integrated antennas in eWLB packages for 77 GHz and 79 GHz automotive radar sensors

The feasibility of an antenna-in-package (AiP) concept is demonstrated for automotive radar sensors at the 77 GHz-band. Infineon's embedded wafer level ball grid array (eWLB) is employed as packaging technology. We developed a printed dipole antenna (PDA) and a printed loop antenna (PLA) in 8×8 mm2 eWLB packages that include 3×3 mm2 dummy Si chips. The fabricated eWLB packages are soldered onto evaluation boards that are designed and fabricated on an RF substrate. The measurements and the simulations are in a good agreement and predict a promising solution for the realization of cost efficient antenna-in-package concepts.
Automatic generation of fill-in clues and answers from raw texts for crosswords

This paper presents a method to generate fill-in clues and answers for building automatically a crossword. Answers are capitalised words present in an input sentence and clues are segments of the dependency syntactic structure of that sentence. The pairs (Clue, ANSWER) are extracted from a collection of raw sentences related to the history of Sarawak. This work is at its early stage, and thus the proposed method that generates automatically fill-in clues, was tested on a small set of sentences and the obtained results are promising. Near 53% of the generated fill-in clues are considered correct. The major contribution of this work is the innovative strategy used to read the result of a pre-order depth-first search applied on a dependency graph to generate the clues. The clues and answers generator is implemented in Python.
BRAIN TUMOR DETECTION AND SEGMENTATION USING WATERSHED SEGMENTATION AND MORPHOLOGICAL OPERATION

In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region.
A Dual-Fed Aperture-Coupled Microstrip Antenna With Polarization Diversity

This communication presents a dual-port reconfigurable square patch antenna with polarization diversity for 2.4 GHz. By controlling the states of four p-i-n diodes on the patch, the polarization of the proposed antenna can be switched among linear polarization (LP), left- or right-hand circular polarization (CP) at each port. The air substrate and aperture-coupled feed structure are employed to simplify the bias circuit of p-i-n diodes. With high isolation and low cross-polarization level in LP modes, both ports can work simultaneously as a dual linearly polarized antenna for polarimetric radars. Different CP waves are obtained at each port, which are suitable for addressing challenges ranging from mobility, adverse weather conditions and non-line-of-sight applications. The antenna has advantages of simple biasing network, easy fabrication and adjustment, which can be widely applied in polarization diversity applications.
Analysis on the Effect of Term-Document's Matrix to the Accuracy of Latent-Semantic-Analysis-Based Cross-Language Plagiarism Detection

This paper presents the results of experimental investigation on the impact of term-document matrix variations to the accuracy of cross-language LSA-based plagiarism detection. The experiment was focusing in comparing Indonesian and English papers. The increase of document definition size as the source of matrix construction significantly caused negative impact to the detection accuracy in all scenarios. The results of the experiments showed that the document definition size must be kept below 10 in order to maintain high accuracy, and reached its worst performance at 25. Additionally, the implementation of term-document matrix using the frequency of word's occurrence was found beneficial to the improvement of detection accuracy compared to the binary implementation using simply the existence/absence of words.
Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis

The frequently changing user preferences and/or item profiles have put essential importance on the dynamic modeling of users and items in personalized recommender systems. However, due to the insufficiency of per user/item records when splitting the already sparse data across time dimension, previous methods have to restrict the drifting purchasing patterns to pre-assumed distributions, and were hardly able to model them rather directly with, for example, time series analysis. Integrating content information helps to alleviate the problem in practical systems, but the domain-dependent content knowledge is expensive to obtain due to the large amount of manual efforts.
 In this paper, we make use of the large volume of textual reviews for the automatic extraction of domain knowledge, namely, the explicit features/aspects in a specific product domain. We thus degrade the product-level modeling of user preferences, which suffers from the lack of data, to the feature-level modeling, which not only grants us the ability to predict user preferences through direct time series analysis, but also allows us to know the essence under the surface of product-level changes in purchasing patterns. Besides, the expanded feature space also helps to make cold-start recommendations for users with few purchasing records.
 Technically, we develop the Fourier-assisted Auto-Regressive Integrated Moving Average (FARIMA) process to tackle with the year-long seasonal period of purchasing data to achieve daily-aware preference predictions, and we leverage the conditional opportunity models for daily-aware personalized recommendation. Extensive experimental results on real-world cosmetic purchasing data from a major e-commerce website (JD.com) in China verified both the effectiveness and efficiency of our approach.
Near-lighting Photometric Stereo for unknown scene distance and medium attenuation

Photometric Stereo in murky water is subject to light attenuation and near-field illumination, and the resulting image formation model is complex. Apart from the scene normals and albedo, the incident illumination varies per-pixel and it depends on the scene depth and the attenuation coefficient of the medium. When these are unknown, e.g. in a realistic scenario where a robotic platform explores an underwater scene (unknown shape and distance) within the dynamic subsea environment (unknown scattering level), Photometric Stereo becomes ambiguous. Previous approaches have tackled the problem by assuming distant-lighting and resorting to external hardware for estimating the unknown model variables. In our work, we show that the Photometric Stereo problem can be determined as soon as some additional constraints regarding the scene albedo and the presence of pixels with local intensity maxima within the image are incorporated into the optimization framework. Our proposed solution leads to effective Photometric Stereo and yields detailed 3D reconstruction of objects in murky water when the scene distance and the medium attenuation are unknown. We evaluate our work using both numerical simulations and real experiments in the controlled environment of a water tank and real port water using a remotely operated vehicle.
Blockstack : A New Decentralized Internet

The traditional internet has many central points of failure and trust, like (a) the Domain Name System (DNS) servers, (b) public-key infrastructure, and (c) end-user data stored on centralized data stores. We present the design and implementation of a new internet, called Blockstack, where users don’t need to trust remote servers. We remove any trust points from the middle of the network and use blockchains to secure critical data bindings. Blockstack implements services for identity, discovery, and storage and can survive failures of underlying blockchains. The design of Blockstack is informed by three years of experience from a large blockchain-based production system. Blockstack gives comparable performance to traditional internet services and enables a much-needed security and reliability upgrade to the traditional internet.
Power assist method for HAL-3 using EMG-based feedback controller

We have developed the exoskeletal robotics suite HAL (Hybrid Assisitve Leg) which is integrated with human and assists suitable power for ‘lower limb of people with goit disorder. This study proposes the method of assist motion and ossist torque to realize a power assist corresponding to the operator’s intention. In the method of a,ssist motion, we adopted Phase Sequence control which generates a series of assist m,otions hi/ transiti.n,q .some simple basic motions called Phase. w e used the feedback controller to adjust the assist torque to m.ointain myoelectricity signals which were generated while performing the power assist uiolking. The experiment xsults showed the effective power assist according to operator’s intention b y using these control methods.
The development of integrated career portal in university using agile methodology

A gap between university graduate's quality and industry sector demand is one of the challenge university has to tackle, which makes university graduates absorption in job market slows down. One of the tools that can be used to bridge between graduates and companies is an integrated career portal. This portal can be optimized to help industry find the employees they need from the alumni from university. On the other hand, this tool can help graduates to get job that suits their competency. By using the portal, alumni can hopefully minimize the number of unemployed university graduates and accelerate their absorption into job market. The development of this career portal will use agile methodology. Questionnaire was distributed to alumni and employers to collect information about what user need in the portal. The result of this research can create a career portal which integrated business process and social media.
Phishing counter measures and their effectiveness – literature review

Phishing is essentially a social engineering crime on the Web, whose rampant occurrences and technique advancements are posing big challenges for researchers in both academia and the industry. The purpose of this study is to examine the available phishing literatures and phishing countermeasures, to determine how research has evolved and advanced in terms of quantity, content and publication outlets. In addition to that, this paper aims to identify the important trends in phishing and its countermeasures and provides a view of the research gap that is still prevailing in this field of study.  This paper is a comprehensive literature review prepared after analysing 16 doctoral theses and 358 papers in this field of research. The papers were analyzed based on their research focus, empirical basis on phishing and proposed countermeasures.  The findings reveal that the current anti-phishing approaches that have seen significant deployments over the internet can be classified into eight categories. Also, the different approaches proposed so far are all preventive in nature. A Phisher will mainly target the innocent consumers who happen to be the weakest link in the security chain and it was found through various usability studies that neither server-side security indicators nor client-side toolbars and warnings are successful in preventing vulnerable users from being deceived.  Educating the internet users about phishing, as well as the implementation and proper application of anti-phishing measures, are critical steps in protecting the identities of online consumers against phishing attacks. Further research is required to evaluate the effectiveness of the available countermeasures against fresh phishing attacks. Also there is the need to find out the factors which influence internet user’s ability to correctly identify phishing websites.
Crowd-Squared: Amplifying the Predictive Power of Search Trend Data

Table A1. Search Terms Generated by the Crowd (a) Influenza – Crowd-Squared Search Terms and % of Turkers Mentioning Each Term Search Term % Mention Search Term % Mention Search Term % Mention Search Term % Mention sick 68% influenza 4% achy 2% fatigue 1% fever 39% coughing 4% weak 2% home 1% cough 21% germs 4% bad 2% hospital 1% cold 16% headache 4% shots 2% nyquil 1% shot 13% ache 4% body aches 2% season 1% vomit 11% runny nose 4% flu shot 2% tea 1%
Torch7: A Matlab-like Environment for Machine Learning

Torch7 is a versatile numeric computing framework and machine learning library that extends Lua. Its goal is to provide a flexible environment to design and train learning machines. Flexibility is obtained via Lua, an extremely lightweight scripting language. High performance is obtained via efficient OpenMP/SSE and CUDA implementations of low-level numeric routines. Torch7 can easily be interfaced to third-party software thanks to Lua’s light interface.
Computed tomographic analysis of tooth-bearing alveolar bone for orthodontic miniscrew placement.

When monocortical orthodontic miniscrews are placed in interdental alveolar bone, the safe position of the miniscrew tip should be ensured. This study was designed to quantify the periradicular space in the tooth-bearing area to provide practical guidelines for miniscrew placement.



Computerized tomographs of 30 maxillae and mandibles were taken from nonorthodontic adults with normal occlusion. Both mesiodistal interradicular distance and bone thickness over the narrowest interradicular space (safety depth) were measured at 2, 4, 6, and 8 mm from the cementoenamel junction.



Mesiodistal space greater than 3 mm was available at the 8-mm level in the maxillary anterior region, between the premolars, and between the second premolar and the first molar at 4 mm. In the mandible, sufficient mesiodistal space was found between the premolars, between the molars, and between the second premolar and the first molar at the 4-mm level. Safety depth greater than 4 mm was found in the maxillary and mandibular intermolar regions, and between the second premolar and the first molar in both arches.



Subapical placement is advocated in the anterior segment. Premolar areas appear reliable in both arches. Angulated placement in the intermolar area is suggested to use the sufficient safety depth in this area.
Shallow convolutional neural network for eyeglasses detection in facial images

Automatic eyeglasses detection plays a major role in many facial analysis systems. To improve the robustness of these systems and cope with real-world applications, a high-speed eyeglasses detector that can achieve high accuracy is needed. Recent studies indicate that the features extracted from convolutional neural networks are compelling. Therefore, this paper presents an effective and efficient method for eyeglasses detection in facial images based on extracting deep features from a well-designed shallow convolutional neural network (CNN). The main contribution of this paper is to address the two essential aspects of CNN: (1) the size of the training dataset required and (2) the depth of the network architecture. To this end, we initialize the learning parameters of the shallow CNN by the parameters of a deep CNN which is fine-tuned on a small dataset. The depth of the neural network is then decreased by removing some convolutional layers after testing its performance on the validation dataset. As a result, a significantly more accurate shallow CNN architecture, Shallow-GlassNet, is obtained, which achieves not only high accuracy but also high speed in eyeglasses detection. Evaluation experiments have been conducted on two large unconstrained facial image databases, LFW and Celeb Faces. The results have demonstrated the superior performance of the proposed framework which achieves a mean accuracy of 99.73%.
Digital implementation of fuzzy logic controller for wide range speed control of brushless DC motor

The brushless DC motors find wide applications such as in battery operated vehicles, wheel chairs, automotive fuel pumps, robotics, machine tools, aerospace and in many industrial applications due to their superior electrical and mechanical characteristics and its capability to operate in hazardous environment. Conventional controllers fail to yield desired performance in BLDC motor control systems due to the non-linearity arising out of variation in the system parameters and change in load. The main focus is now on the application of artificial intelligent techniques such as fuzzy logic to solve this problem. Another great challenge is to reduce the size and cost of the drive system without compromising the performance. In this paper, the design and digital implementation of fuzzy logic controller using a versatile ADUC812 microcontroller, and low-cost, compact, superior performance components are used in order to reduce the cost and size of the drive system. The experimental results are presented to prove the flexibility of the control scheme in real time.
Clustering-based Approach for Anomaly Detection in XACML Policies

The development of distributed applications arises multiple security issues such as access control. AttributeBased Access Control has been proposed as a generic access control model, which provides more flexibility and promotes information and security sharing. eXtensible Access Control Markup Language (XACML) is the most convenient way to express ABAC policies. However, in distributed environments, XACML policies become more complex and hard to manage. In fact, an XACML policy in distributed applications may be aggregated from multiple parties and can be managed by more than one administrator. Therefore, it may contain several anomalies such as conflicts and redundancies, which may affect the performance of the policy execution. In this paper, we propose an anomaly detection method based on the decomposition of a policy into clusters before searching anomalies within each cluster. Our evaluation results demonstrate the efficiency of the suggested approach.