Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast concurrent lock-free binary search trees

We present a new lock-free algorithm for concurrent manipulation of a binary search tree in an asynchronous shared memory system that supports search, insert and delete operations. In addition to read and write instructions, our algorithm uses (single-word) compare-and-swap (CAS) and bit-test-and-set (SETB) atomic instructions, both of which are commonly supported by many modern processors including Intel~64 and AMD64. In contrast to existing lock-free algorithms for a binary search tree, our algorithm is based on marking edges rather than nodes. As a result, when compared to other lock-free algorithms, modify (insert and delete) operations in our algorithm work on a smaller portion of the tree, thereby reducing conflicts, and execute fewer atomic instructions (one for insert and three for delete). Our experiments indicate that our lock-free algorithm significantly outperforms all other algorithms for a concurrent binary search tree in many cases, especially when contention is high, by as much as 100%.
Interannual Variation in Carbon Sequestration Depends Mainly on the Carbon Uptake Period in Two Croplands on the North China Plain

Interannual variation in plant phenology can lead to major modifications in the interannual variation of net ecosystem production (NEP) and net biome production (NBP) as a result of recent climate change in croplands. Continuous measurements of carbon flux using the eddy covariance technique were conducted in two winter wheat and summer maize double-cropped croplands during 2003–2012 in Yucheng and during 2007–2012 in Luancheng on the North China Plain. Our results showed that the difference between the NEP and the NBP, i.e., the crop economic yield, was conservative even though the NEP and the NBP for both sites exhibited marked fluctuations during the years of observation. A significant and positive relationship was found between the annual carbon uptake period (CUP) and the NEP as well as the NBP. The NEP and the NBP would increase by 14.8±5.2 and 14.7±6.6 g C m−2 yr−1, respectively, if one CUP-day was extended. A positive relationship also existed between the CUP and the NEP as well as the NBP for winter wheat and summer maize, respectively. The annual air temperature, through its negative effect on the start date of the CUP, determined the length of the CUP. The spring temperature was the main indirect factor controlling the annual carbon sequestration when a one-season crop (winter wheat) was considered. Thus, global warming can be expected to extend the length of the CUP and thus increase carbon sequestration in croplands.
Informal Multilingual Multi-domain Sentiment Analysis

This paper addresses the problem of sentiment analysis in an informal setting in multiple domains and in two languages. We explore the influence of using background knowledge in the form of different sentiment lexicons, as well as the influence of various lexical surface features. We evaluate several different feature set combination strategies. We show that the improvement resulting from using a twolayer meta-model over the bag-of-words, sentiment lexicons and surface features is most notable on social media datasets in both English and Spanish. For English, we are also able to demonstrate improvement on the news domain using sentiment lexicons as well as a large improvement on the social media domain. We also demonstrate that domain-specific lexicons bring comparable performance to general-purpose lexicons.
Arginine-based cationic liposomes for efficient in vitro plasmid DNA delivery with low cytotoxicity

Background Currently available gene delivery vehicles have many limitations such as low gene delivery efficiency and high cytotoxicity. To overcome these drawbacks, we designed and synthesized two cationic lipids comprised of n-tetradecyl alcohol as the hydrophobic moiety, 3-hydrocarbon chain as the spacer, and different counterions (eg, hydrogen chloride [HCl] salt or trifluoroacetic acid [TFA] salt) in the arginine head group. Methods Cationic lipids were hydrated in 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffer to prepare cationic liposomes and characterized in terms of their size, zeta potential, phase transition temperature, and morphology. Lipoplexes were then prepared and characterized in terms of their size and zeta potential in the absence or presence of serum. The morphology of the lipoplexes was determined using transmission electron microscopy and atomic force microscopy. The gene delivery efficiency was evaluated in neuronal cells and HeLa cells and compared with that of lysine-based cationic assemblies and Lipofectamine™ 2000. The cytotoxicity level of the cationic lipids was investigated and compared with that of Lipofectamine™ 2000. Results We synthesized arginine-based cationic lipids having different counterions (ie, HCl-salt or TFA-salt) that formed cationic liposomes of around 100 nm in size. In the absence of serum, lipoplexes prepared from the arginine-based cationic liposomes and plasmid (p) DNA formed large aggregates and attained a positive zeta potential. However, in the presence of serum, the lipoplexes were smaller in size and negative in zeta potential. The morphology of the lipoplexes was vesicular. Arginine-based cationic liposomes with HCl-salt showed the highest transfection efficiency in PC-12 cells. However, arginine-based cationic liposomes with TFA salt showed the highest transfection efficiency in HeLa cells, regardless of the presence of serum, with very low associated cytotoxicity. Conclusion The gene delivery efficiency of amino acid-based cationic assemblies is influenced by the amino acids (ie, arginine or lysine) present as the hydrophilic head group and their associated counterions.
An assessment of the test–retest reliability of the New Nordic Diet score

Background There is a growing interest in the New Nordic Diet (NND) as a potentially health promoting, environmentally friendly, and palatable regional diet. Also, dietary scores are gaining ground as a complementary approach for examining relations between dietary patterns and various health outcomes. A score assessing adherence to the NND has earlier been published, yet not tested for reliability. Objective To assess the test–retest reliability of the NND score in a sample of parents of toddlers, residing in Southern Norway. Design A questionnaire survey was completed on two occasions, approximately 14 days apart, by 67 parents of toddlers [85% females, mean age 34 years (SD=5.3 years)]. The NND score was constructed from 24 items and comprised 10 subscales that summarize meal pattern and intake of typical Nordic foods. Each subscale was dichotomized by the median and assigned values of ‘0’ or ‘1’. Adding the subscales yielded a score ranging from 0 to 10, which was further trichotomized. Test–retest reliability of the final NND score and individual subscales was assessed by Pearson's correlation coefficient and Spearman's rank correlation coefficient, respectively. Additionally, cross tabulation and kappa measure of agreement (k) were used to assess the test–retest agreement of classification into the NND score, and the subscales. Results Test–retest correlations of the NND score and subscales were r=0.80 (Pearson) and r=0.54–0.84 (Spearman), respectively, all p<0.001. There were 69% (k=0.52) and 67–88% (k=0.32–0.76) test–retest correct classification of the trichotomized score and the dichotomized subscales, respectively. Conclusion The NND score and the 10 subscales appear to have acceptable test–retest reliability when tested in a sample of parents of toddlers.
A Local Learning Approach for Clustering

We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on its neighboring data and their cluster labels, using current supervised learning methods. An optimization problem is formulated such that its solution has the above property. Relaxation and eigen-decomposition are applied to solve this optimization problem. We also briefly investigate the parameter selection issue and provide a simple parameter selection method for the proposed algorithm. Experimental results are provided to validate the effectiveness of the proposed approach.
CluSID: a clustering scheme for intrusion detection improved by information theory

Security is a big issue for all networks in any enterprise environment. Many solutions have been proposed to secure the network infrastructure and communication over the Internet. Intrusion detection systems with many different techniques such as data mining approaches are employed to maximize the detection rate of intrusions while reducing false alarm rate. For instance, many clustering techniques are recommended which segregate normal and abnormal data in IDSs. Clustering methods put emphasis on finding differences and similarities of traffic sessions to categorize each one in its corresponding groups. These groups are represented by their assigned labels. Later, these labels are used to predict the type of the incoming network traffic. In this paper, we propose a clustering scheme to use in intrusion detection systems, named CluSID. The major contribution of CluSID is using information theory for taking full advantages of clustering techniques. The main logic behind CluSID is to use non-uniform gain functions for network traffic features in order to improve the accuracy of clustering process. To this end, we apply information theory concepts for moving center of clusters to the most important areas in the domain of the selected features. The results clearly show a raise in detection rate of CluSID in most of the attack categories in comparison to KDD CUP'99 Winner and simple clustering methods. The increase in detection rate of proposed system is about 25 percent.
A self-biased high performance folded cascode CMOS op-amp

Cascode CMOS op-amps use a large number of external bias voltages. This results in numerous drawbacks, namely, an area and power overhead, susceptibility of the bias lines to noise and cross-talk and high sensitivity of the bias point to process variations. In this paper we present a self-biasing technique for folded cascode CMOS op-amps that uses no additional devices and no bias voltages other than the two supply rails. The resulting self-biased op-amps are free from the above mentioned drawbacks and exhibit the same performance as existing folded casode op amps, except for a small reduction in slew rate. This is achieved by following transistor sizing constraints derived through detailed circuit analysis. The technique is applied to an existing high performance op amp. Simulation results show that the high performance is maintained while nine bias voltages are eliminated.
DIFFERENCES IN ETHICAL JUDGMENTS BETWEEN MALE AND FEMALE ACCOUNTANTS

Shaub (1994) and Cohen et al. (1998) reported that female accountants and undergraduate accounting students exhibit higher levels of moral reasoning than their male counterparts and that female undergraduates make higher ethical evaluations than their male counterparts. The current study extends these findings in two ways, using data collected from a large sample of professional accountants in accounting firms. First, we consider the effects of the moral intensity of the issue (Jones, 1991). Second, we consider a number of demographic and personal variables to determine if these factors are related to evaluations made on six vignettes describing ethical situations. We find, consistent with previous findings, that female accountants make higher ethical judgments than their male colleagues for situations of both high and low moral intensity. We also find that the decisions made by women are related to different demographic and personal factors than their male colleagues.
A new classifier for handwritten Chinese character recognition using 2-dimensional functional relationship model

This paper presents a new classification method for online handwritten Chinese character recognition (HCCR). For classification, a similarity measure is established via statistical technique which calculates the coefficient of determination (R2p) for 2-dimensional unreplicated linear functional relationship (MULFR) model between the trajectory pattern of input character and character in database, according to which the recognition result is determined. The principle of the proposed method makes R2p very robust against size and position variation as well as stroke shape deformation, without normalization. The efficiency of our proposed method is studied by the experimental result, showing that the proposed method still remains a promising recognition rate even without undergoing normalization if compared to city block distance with deviation (CBDD) and minimum distance (MD) classifier: a high recognition rate of 94% with reduced processing time up to 77.85%.
Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

Background Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature.
Constant Modulus Blind Equalization for Time-Varying MIMO-FIR Channels with Pulse Estimation

The constant modulus (CM) property of source signal can be mostly utilized to equalize the single-input multiple-output and finite impulse response (FIR) channels. In existing, the channels are blindly equalized and the equalization methods are based on higher order statistics that estimates all non-zero channel pulsation under time-invariance assumption. However, the assumption cannot be satisfied time varying multiple input multiple output (MIMO) applications ex. Mobile communication. In this paper, the proposed algorithm which extends the CM criterion to blind equalization using complex exponential basic expansion model (CE-BEM) and the channel is assumed as time varying MIMO-FIR. The method only employ the Second order statistics (SOS) and finally, it estimates only one pulsation. In this way, the system increases the SNR of the transmitted symbols and produces most beneficial result in time-varying channels. The fast convergence is also achieved through zero forcing equalization.
Modeling Connections Behavior for Web-Based Bots Detection

Botnet has become a prevalent platform for malicious attacks, which poses a significant threat to Internet security. Recently, botnets are inclined to utilize HTTP to route their command and control (C&C) communication instead of using the protocol Internet Relay Chat (IRC). And these web-based C&C bots try to blend into normal HTTP traffic, which makes them more difficult to be identified. In this work, we propose an automatic approach to identify web-based C&C bots by modeling the essential network behavior of web-based bots in supervised network. Experimental results show the proposed approach is very efficient and can detect web-base bots with low false positive ratio.
WaveSim and Adaptive WaveSim Transform for Subsequence Time-Series Clustering

Recent days advancement in sensor and instrumentation technology has seen a large amount of time series data being recorded in our day-to-day life. Knowledge and data mining research has taken up the responsibility of mining the hidden patterns in these huge collection of time series data during the past decade. In this paper we propose methodologies to extract hidden knowledge in a time series data through an unsupervised approach by using the novel WaveSim transform. This recently introduced transform is a novel perspective of wavelet transform and it is defined by keeping pattern analysis and recognition in mind. Time series data mining has been classified broadly into whole series mining and subsequence series mining. We propose a hierarchical tree based approach for subsequence mining in a time series using a modified WaveSim transform called Adaptive WaveSim transform. The technique has been illustrated through a set of experimentation results which is expected to open up a wide arena for future work.
Operating System Support for Procedural Abstraction in Embedded Systems

Procedural abstraction reduces code size by replacing repeated code fragments with call instructions to a subroutine that executes the repeated fragment. However, in order to build a subroutine, extra instructions are necessary to support the procedural call mechanism. In this paper, we present an operating system level technique which improves the space efficiency of a procedural abstraction-based code compaction technique. The call-related extra instructions are not used in the proposed technique because operating system routines implicitly supports the procedure call and return. The proposed technique consists of three execution modes including one applicable to ROM-based systems. The experimental results show the proposed technique reduces the code size significantly while increasing the execution time slightly
A novel approach for image dehazing combining visible-NIR images

Image dehazing either using single visible image or using visible and near-infrared (NIR) image pair has seen growing interest in last decade for improving visibility in landscape photographs. In this paper, we propose a novel approach for image dehazing scheme using a pair of visible and NIR images. The dehazing mechanism estimates depth map and airlight color using the visible-NIR scene statistics and uses them to form a haze-free image. Experiments on a variety of hazy images demonstrate that our method achieves high degree of detail recovery over the existing image dehazing algorithms. The resultant images exhibit a very good blend of details, contrast and color. The proposed algorithm is less computationally demanding and is fully automatic. The results are superior in both visual as well as quantitative analysis compared to state-of-the-art image dehazing algorithms.
The performance analysis of smartphone-based pedestrian dead reckoning and wireless locating technology for indoor navigation application

Recent developments in smartphone technology have increased user demand for indoors applications. The Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) are the two advanced systems for navigation technology. However, it is still difficult for GNSS to provide an accurate and practical navigation solution, especially in environments with little or no signal availability. These failings should be easy to overcome; because of their portability and multiple embedded hardware sensors, smartphones seem well positioned to make pedestrian navigation easy and convenient in any environment. Pedestrian Dead Reckoning (PDR) is one of the most commonly used technologies used for pedestrian navigation, but it is not always accurate because its positioning errors tend to accumulate over time. Therefore, this research introduces a new tool to overcome this failing; a Bluetooth Low-Energy (BLE) beacon can maintain and improve the accuracy of PDR. Moreover, a BLE beacon can be initialized from any user position in an indoor environment. The random and unpredictable positions of pedestrians inevitably result in the degradation of navigation guidance systems’ data. To rectify this problem, we have implemented activity recognition technology to notify the proposed system so as to provide a more accurate heading estimate. This study proposes a Personal Navigation System (PNS) based on this technology; it can estimate navigation solutions in real time and combines the advantages of PDR and Bluetooth positioning technology. A series of experiments were conducted to evaluate the accuracy of the system and the efficacy of our proposed algorithms. Preliminary results show the average relative precision of PDR to be about 2.5%, when using a mobile hand-held device. The error of initial position from 2-D beacon positioning is less than two meters. The proposed system works well without post-processing, and the multi-sensor activity recognition system can determine the placement of the device when it is being carried or used by someone with close to 100% accuracy.
Argo: enabling the development of bespoke workflows and services for disease annotation

Argo (http://argo.nactem.ac.uk) is a generic text mining workbench that can cater to a variety of use cases, including the semi-automatic annotation of literature. It enables its technical users to build their own customised text mining solutions by providing a wide array of interoperable and configurable elementary components that can be seamlessly integrated into processing workflows. With Argo's graphical annotation interface, domain experts can then make use of the workflows' automatically generated output to curate information of interest. With the continuously rising need to understand the aetiology of diseases as well as the demand for their informed diagnosis and personalised treatment, the curation of disease-relevant information from medical and clinical documents has become an indispensable scientific activity. In the Fifth BioCreative Challenge Evaluation Workshop (BioCreative V), there was substantial interest in the mining of literature for disease-relevant information. Apart from a panel discussion focussed on disease annotations, the chemical-disease relations (CDR) track was also organised to foster the sharing and advancement of disease annotation tools and resources. This article presents the application of Argo’s capabilities to the literature-based annotation of diseases. As part of our participation in BioCreative V’s User Interactive Track (IAT), we demonstrated and evaluated Argo’s suitability to the semi-automatic curation of chronic obstructive pulmonary disease (COPD) phenotypes. Furthermore, the workbench facilitated the development of some of the CDR track’s top-performing web services for normalising disease mentions against the Medical Subject Headings (MeSH) database. In this work, we highlight Argo’s support for developing various types of bespoke workflows ranging from ones which enabled us to easily incorporate information from various databases, to those which train and apply machine learning-based concept recognition models, through to user-interactive ones which allow human curators to manually provide their corrections to automatically generated annotations. Our participation in the BioCreative V challenges shows Argo’s potential as an enabling technology for curating disease and phenotypic information from literature. Database URL: http://argo.nactem. ac.uk