nils-herrmann/clean_scirepeval huggingface dataset:
Obscuro: A Bitcoin Mixer using Trusted Execution Environments

Bitcoin provides only pseudo-anonymous transactions, which can be exploited to link payers and payees -- defeating the goal of anonymous payments. To thwart such attacks, several Bitcoin mixers have been proposed, with the objective of providing unlinkability between payers and payees. However, existing Bitcoin mixers can be regarded as either insecure or inefficient.
 We present Obscuro, a highly efficient and secure Bitcoin mixer that utilizes trusted execution environments (TEEs). With the TEE's confidentiality and integrity guarantees for code and data, our mixer design ensures the correct mixing operations and the protection of sensitive data (i.e., private keys and mixing logs), ruling out coin theft and address linking attacks by a malicious service provider. Yet, the TEE-based implementation does not prevent the manipulation of inputs (e.g., deposit submissions, blockchain feeds) to the mixer, hence Obscuro is designed to overcome such limitations: it (1) offers an indirect deposit mechanism to prevent a malicious service provider from rejecting benign user deposits; and (2) scrutinizes blockchain feeds to prevent deposits from being mixed more than once (thus degrading anonymity) while being eclipsed from the main blockchain branch. In addition, Obscuro provides several unique anonymity features (e.g., minimum mixing set size guarantee, resistant to dropping user deposits) that are not available in existing centralized and decentralized mixers.
 Our prototype of Obscuro is built using Intel SGX and we demonstrate its effectiveness in Bitcoin Testnet. Our implementation mixes 1000 inputs in just 6.49 seconds, which vastly outperforms all of the existing decentralized mixers.
Simulation of a photovoltaic panels by using Matlab/Simulink

In recent years, it is getting attention for renewable energy sources such as solar energy, fuel cells, batteries or ultracapacitors for distributed power generation systems. This paper proposes a general mathematical model of solar cells and Matlab/Simulink software based simulation of this model has been visually programmed. Proposed model can be used with other hybrid systems to develop solar cell simulations. Also, all equations are performed by using Matlab/Simulink programming.
Antifungal efficacy of Capsicum frutescens L. extracts against some prevalent fungal strains associated with groundnut storage.

The antifungal potential of aqueous leaf and fruit extracts of Capsicum frutescens against four major fungal strains associated with groundnut storage was evaluated. These seed-borne fungi, namely Aspergillus flavus, A. niger, Penicillium sp. and Rhizopus sp. were isolated by standard agar plate method and identified by macroscopic and microscopic features. The minimum inhibitory concentrations (MIC) and minimum fungicidal concentration (MFC) of C. frutescens extracts were determined. MIC values of the fruit extract were lower compared to the leaf extract. At MIC, leaf extract showed strong activity against A. flavus (88.06%), while fruit extract against A. niger (88.33%) in the well diffusion method. Groundnut seeds treated with C.frutescens fruit extract (10mg/ml) showed a higher rate of fungal inhibition. The present results suggest that groundnuts treated with C. frutescens fruit extracts are capable of preventing fungal infection to a certain extent.
Supporting data quality management in decision-making

In the complex decision-environments that characterize e-business settings, it is important to permit decision-makers to proactively manage data quality. In this paper we propose a decision-support framework that permits decision-makers to gauge quality both in an objective (context-independent) and in a context-dependent manner. The framework is based on the information product approach and uses the Information Product Map (IPMAP). We illustrate its application in evaluating data quality using completeness—a data quality dimension that is acknowledged as important. A decision-support tool (IPView) for managing data quality that incorporates the proposed framework is also described. D 2005 Elsevier B.V. All rights reserved.
Mycorrhizal networks: Mechanisms, ecology and modelling

Mycorrhizal networks, defined as a common mycorrhizal mycelium linking the roots of at least two plants, occur in all major terrestrial ecosystems. This review discusses the recent progress and challenges in our understanding of the characteristics, functions, ecology and models of mycorrhizal networks, with the goal of encouraging future research to improve our understanding of their ecology, adaptability and evolution. We focus on four themes in the recent literature: (1) the physical, physiological and molecular evidence for the existence of mycorrhizal networks, as well as the genetic characteristics and topology of networks in natural ecosystems; (2) the types, amounts and mechanisms of interplant material transfer (including carbon, nutrients, water, defence signals and allelochemicals) in autotrophic, mycoheterotrophic or partial mycoheterotrophic plants, with particular focus on carbon transfer; (3) the influence of mycorrhizal networks on plant establishment, survival and growth, and the implications for community diversity or stability in response to environmental stress; and (4) insights into emerging methods for modelling the spatial configuration and temporal dynamics of mycorrhizal networks, including the inclusion of mycorrhizal networks in conceptual models of complex adaptive systems. We suggest that mycorrhizal networks are fundamental agents of complex adaptive systems (ecosystems) because they provide avenues for feedbacks and cross-scale interactions that lead to selforganization and emergent properties in ecosystems. We have found that research in the genetics of mycorrhizal networks has accelerated rapidly in the past 5 y with increasing resolution and throughput of molecular tools, but there still remains a large gap between understanding genes and understanding the physiology, ecology and evolution of mycorrhizal networks in our changing environment. There is now enormous and exciting potential for mycorrhizal researchers to address these higher level questions and thus inform ecosystem and evolutionary research more broadly. a 2012 The British Mycological Society.  fax: þ1 604 822 9102. ca (S. W. Simard), kjbeiler@interchange.ubc.ca (K. J. Beiler), binghm@interchange.ubc.ca ch.co.nz (J. R. Deslippe), Leanne.Philip@SMU.CA (L. J. Philip), francois.teste@uwa.edu.au ritish Mycological Society.
Reliability-Driven Power/Ground Routing for Analog ICs

Electromigration and voltage drop (IR-drop) are two major reliability issues in modern IC design. Electromigration gradually creates permanently open or short circuits due to excessive current densities; IR-drop causes insufficient power supply, thus degrading performance or even inducing functional errors because of nonzero wire resistance. Both types of failure can be triggered by insufficient wire widths. Although expanding the wire width alleviates electromigration and IR-drop, unlimited expansion not only increases the routing cost, but may also be infeasible due to the limited routing resource. In addition, electromigration and IR-drop manifest mainly in the power/ground (P/G) network. Therefore, taking wire widths into consideration is desirable to prevent electromigration and IR-drop at P/G routing. Unlike mature digital IC designs, P/G routing in analog ICs has not yet been well studied. In a conventional design, analog designers manually route P/G networks by implementing greedy strategies. However, the growing scale of analog ICs renders manual routing inefficient, and the greedy strategies may be ineffective when electromigration and IR-drop are considered. This study distances itself from conventional manual design and proposes an automatic analog P/G router that considers electromigration and IR-drops. First, employing transportation formulation, this article constructs an electromigration-aware rectilinear Steiner tree with the minimum routing cost. Second, without changing the solution quality, wires are bundled to release routing space for enhancing routability and relaxing congestion. A wire width extension method is subsequently adopted to reduce wire resistance for IR-drop safety. Compared with high-tech designs, the proposed approach achieves equally optimal solutions for electromigration avoidance, with superior efficiencies. Furthermore, via industrial design, experimental results also show the effectiveness and efficiency of the proposed algorithm for electromigration prevention and IR-drop reduction.
Chinese Social Media Reaction to Information about 42 Notifiable Infectious Diseases

This study aimed to identify what information triggered social media users' responses regarding infectious diseases. Chinese microblogs in 2012 regarding 42 infectious diseases were obtained through a keyword search in the Weiboscope database. Qualitative content analysis was performed for the posts pertinent to each keyword of the day of the year with the highest daily count. Similar posts were grouped and coded. We identified five categories of information that increased microblog traffic pertaining to infectious diseases: news of an outbreak or a case; health education/information; alternative health information/Traditional Chinese Medicine; commercial advertisement/entertainment; and social issues. News unrelated to the specified infectious diseases also led to elevated microblog traffic. Our study showcases the diverse contexts from which increased social media traffic occur. Our results will facilitate better health communication as causes underlying increased social media traffic are revealed.
Compact conformal dipole antenna on organic substrate for 2.4 GHz applications

In this paper, a microstrip dipole antenna on a flexible organic substrate is proposed. The antenna arms are tilted to make different variations of the dipole with more compact size and almost same performance. The antennas are fed using a coplanar stripline (CPS) geometry (Simons, 2001). The antennas are then conformed over cylindrical surfaces and their performances are compared to their flat counterparts. Good performance is achieved for both the flat and conformal antennas.
Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains

Link prediction is a key technique in many applications such as recommender systems, where potential links between users and items need to be predicted. A challenge in link prediction is the data sparsity problem. In this paper, we address this problem by jointly considering multiple heterogeneous link prediction tasks such as predicting links between users and different types of items including books, movies and songs, which we refer to as the collective link prediction (CLP) problem. We propose a nonparametric Bayesian framework for solving the CLP problem, which allows knowledge to be adaptively transferred across heterogeneous tasks while taking into account the similarities between tasks. We learn the inter-task similarity automatically. We also introduce link functions for different tasks to correct their biases and skewness of distributions in their link data. We conduct experiments on several real world datasets and demonstrate significant improvements over several existing state-of-the-art methods.
Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction

In classification or prediction tasks, data imbalance problem is frequently observed when most of instances belong to one majority class. Data imbalance problem has received considerable attention in machine learning community because it is one of the main causes that degrade the performance of classifiers or predictors. In this paper, we propose geometric mean based boosting algorithm (GMBoost) to resolve data imbalance problem. GMBoost enables learning with consideration of both majority and minority classes because it uses the geometric mean of both classes in error rate and accuracy calculation. To evaluate the performance of GMBoost, we have applied GMBoost to bankruptcy prediction task. The results and their comparative analysis with AdaBoost and cost-sensitive boosting indicate that GMBoost has the advantages of high prediction power and robust learning capability in imbalanced data as well as balanced data distribution.
Automatic Brain Tumor Segmentation Using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of data augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs’ performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation can achieve higher segmentation accuracy and obtain uncertainty estimation of the segmentation results.
Cyberbullying or Cyber Aggression ? : A Review of Existing Definitions of Cyber-Based Peer-to-Peer Aggression

Due to the ongoing debate regarding the definitions and measurement of cyberbullying, the present article critically appraises the existing literature and offers direction regarding the question of how best to conceptualise peer-to-peer abuse in a cyber context. Variations across definitions are problematic as it has been argued that inconsistencies with regard to definitions result in researchers examining different phenomena, whilst the absence of an agreed conceptualisation of the behaviour(s) involved hinders the development of reliable and valid measures. Existing definitions of cyberbullying often incorporate the criteria of traditional bullying such as intent to harm, repetition, and imbalance of power. However, due to the unique nature of cyber-based communication, it can be difficult to identify such criteria in relation to cyber-based abuse. Thus, for these reasons cyberbullying may not be the most appropriate term. Rather than attempting to “shoe-horn” this abusive behaviour into the preconceived conceptual framework that provides an understanding of traditional bullying, it is timely to take an alternative approach. We argue that it is now time to turn our attention to the broader issue of cyber aggression, rather than persist with the narrow focus that is cyberbullying.
Potential Health Benefits of Cannabis Extracts : A Review

A central tenet underlying the use of plant preparations is that herbs contain many bioactive compounds. Cannabis contains tetrahydrocannabinols (THC) a primary metabolite with reported psychotropic effects. Therefore, the presence of THC makes controversial the use of Cannabis to treat diseases by which their uses and applications were limited. The question then is: is it possible to use the extracts from Cannabis to treat the diseases related with it use in folk medicine? More recently, the synergistic contributions of bioactive constituents have been scientifically demonstrated. We reviewed the literature concerning medical cannabis and its secondary metabolites, including fraction and total extracts. Scientific evidence shows that secondary metabolites in cannabis may enhance the positive effects of THC a primary metabolite. Other chemical components (cannabinoid and non-cannabinoid) in cannabis or its extracts may reduce THC-induced anxiety, cholinergic deficits, and immunosuppression; which could increase its therapeutic potential. Particular attention will be placed on noncannabinoid compounds interactions that could produce synergy with respect to treatment of pain, inflammation, epilepsy, fungal and bacterial infections. The evidence accessible herein pointed out for the possible synergism that might occur involving the main phytocompounds with each other or with other minor components.
Treatment timing for an orthopedic approach to patients with increased vertical dimension.

The aim of this study was to investigate the role of treatment timing on the effectiveness of vertical-pull chincup (V-PCC) therapy in conjunction with a bonded rapid maxillary expander (RME) in growing subjects with mild-to-severe hyperdivergent facial patterns.



The records of 39 subjects treated with a bonded RME combined with a V-PCC were compared with 29 untreated subjects with similar vertical skeletal disharmonies. Lateral cephalograms were analyzed before (T1) and after treatment or observation (T2). Both the treated and the untreated samples were divided into prepubertal and pubertal groups on the basis of cervical vertebral maturation (prepubertal treated group, 21 subjects; pubertal treated group, 18 subjects; prepubertal control group, 15 subjects; pubertal control group, 14 subjects). Mean change differences from T2 to T1 were compared in the 2 prepubertal and the 2 pubertal groups with independent-sample t tests.



No statistically significant differences between the 2 prepubertal groups were found for any cephalometric skeletal measures from T1 to T2. When compared with the untreated pubertal sample, the group treated with the RME and V-PCC at puberty showed a statistically significant reduction in the inclination of the mandibular plane to the Frankfort horizontal (-2.2 mm), a statistically significant reduction in the inclination of the condylar axis to the mandibular plane (-2.2 degrees), and statistically significant supplementary growth of the mandibular ramus (1.7 mm).



Treatment of increased vertical dimension with the RME and V-PCC protocol appears to produce better results during the pubertal growth spurt than before puberty, although the absolute amount of correction in the vertical skeletal parameters is limited.
DeepFam: deep learning based alignment-free method for protein family modeling and prediction

A large number of newly sequenced proteins are generated by the next-generation sequencing technologies and the biochemical function assignment of the proteins is an important task. However, biological experiments are too expensive to characterize such a large number of protein sequences, thus protein function prediction is primarily done by computational modeling methods, such as profile Hidden Markov Model (pHMM) and k-mer based methods. Nevertheless, existing methods have some limitations; k-mer based methods are not accurate enough to assign protein functions and pHMM is not fast enough to handle large number of protein sequences from numerous genome projects. Therefore, a more accurate and faster protein function prediction method is needed.



In this paper, we introduce DeepFam, an alignment-free method that can extract functional information directly from sequences without the need of multiple sequence alignments. In extensive experiments using the Clusters of Orthologous Groups (COGs) and G protein-coupled receptor (GPCR) dataset, DeepFam achieved better performance in terms of accuracy and runtime for predicting functions of proteins compared to the state-of-the-art methods, both alignment-free and alignment-based methods. Additionally, we showed that DeepFam has a power of capturing conserved regions to model protein families. In fact, DeepFam was able to detect conserved regions documented in the Prosite database while predicting functions of proteins. Our deep learning method will be useful in characterizing functions of the ever increasing protein sequences.


Availability and implementation
Codes are available at https://bhi-kimlab.github.io/DeepFam.
IMPROVED PULSE REPETITION INTERVAL ( PRI ) DEINTERLEAVING FOR ELECTRONIC SUPPORT MEASURE ( ESM ) RECEIVER

In an electronic warfare (EW) battlefield environment, it is highly necessary for a fighter aircraft to intercept and identify the several interleaved radar signals that it receives from the surrounding emitters, so as to prepare itself for countermeasures. The main function of the Electronic Support Measure (ESM) receiver is to receive, measure, deinterleave pulses and then identify alternative threat emitters. Deinterleaving of radar signals is based on time of arrival (TOA) analysis and the use of the sequential difference (SDIF) histogram method for determining the pulse repetition interval (PRI), which is an important pulse parameter. Once the pulse repetition intervals are determined, check for the existence of staggered PRI (level-2) is carried out, implemented in MATLAB. Keywordspulse deinterleaving, pulse repetition interval, stagger PRI, sequential difference histogram, time of arrival.
Kinematic calibration of a 7-DOF self-calibrated modular cable-driven robotic arm

This paper presents the kinematic calibration of a novel 7-degree-of-freedom (DOF) cable-driven robotic arm (CDRA), aimed at improving its absolute positioning accuracy. This CDRA consists of three 'self-calibrated' cable-driven parallel mechanism (CDPM) modules. In order to account for any kinematic errors that might arise when assembling the individual CDPMs, a calibration model is formulated based on the local product-of-exponential formula and the measurement residues in the tool-tip frame poses. An iterative least-squares algorithm is employed to identify the errors in the fixed transformation frames of the sequentially assembled 'self- calibrated' CDPM modules. Both computer simulations and experimental studies were carried out to verify the robustness and effectiveness of the proposed calibration algorithm. From the experimental studies, errors in the fixed kinematic transformation frames were precisely recovered after a minimum of 15 pose measurements.
A Master Attack Methodology for an AI-Based Automated Attack Planner for Smart Cities

America’s critical infrastructure is becoming “smarter” and increasingly dependent on highly specialized computers called industrial control systems (ICS). Networked ICS components now called the industrial Internet of Things (IIoT) are at the heart of the “smart city”, controlling critical infrastructure, such as CCTV security networks, electric grids, water networks, and transportation systems. Without the continuous, reliable functioning of these assets, economic and social disruption will ensue. Unfortunately, IIoT are hackable and difficult to secure from cyberattacks. This leaves our future smart cities in a state of perpetual uncertainty and the risk that the stability of our lives will be upended. The Local government has largely been absent from conversations about cybersecurity of critical infrastructure, despite its importance. One reason for this is public administrators do not have a good way of knowing which assets and which components of those assets are at the greatest risk. This is further complicated by the highly technical nature of the tools and techniques required to assess these risks. Using artificial intelligence planning techniques, an automated tool can be developed to evaluate the cyber risks to critical infrastructure. It can be used to automatically identify the adversarial strategies (attack trees) that can compromise these systems. This tool can enable both security novices and specialists to identify attack pathways. We propose and provide an example of an automated attack generation method that can produce detailed, scalable, and consistent attack trees–the first step in securing critical infrastructure from cyberattack.