nils-herrmann/clean_scirepeval huggingface dataset:
A machine learning approach for linux malware detection

The increasing number of malware is becoming a serious threat to the private data as well as to the expensive computer resources. Linux is a Unix based machine and gained popularity in recent years. The malware attack targeting Linux has been increased recently and the existing malware detection methods are insufficient to detect malware efficiently. We are introducing a novel approach using machine learning for identifying malicious Executable Linkable Files. The system calls are extracted dynamically using system call tracer Strace. In this approach we identified best feature set of benign and malware specimens to built classification model that can classify malware and benign efficiently. The experimental results are promising which depict a classification accuracy of 97% to identify malicious samples.
Continuous Angle-based Outlier Detection on High-dimensional Data Streams

Outlier detection over data streams is an increasingly important task in data mining. Traditional distance-based data stream outlier detection is unsuitable for high-dimensional data sets, since the discrimination of distances between different data points becomes rather poor in high dimensional space. ABOD (Angle-based Outlier Detection) is an effective approach to detecting outliers in high-dimensional space. In this paper, the problem of continuous ABOD over data streams is studied. Generally, only a few data objects may change their states during two consecutive timestamps. Therefore, we propose several incremental angle-based outlier detection approaches over data streams based on ABOD and its variants that provide visible speed-up without loss of accuracy. Firstly, the basic ideas of these incremental algorithms are introduced. Then, we explain the time complexity of them. Finally, we use synthetic data streams to prove their efficiency.
2.5D building modeling with topology control

2.5D building reconstruction aims at creating building models composed of complex roofs and vertical walls. In this paper, we define 2.5D building topology as a set of roof features, wall features, and point features; together with the associations between them. Based on this definition, we extend 2.5D dual contouring into a 2.5D modeling method with topology control. Comparing with the previous method, we put less restrictions on the adaptive simplification process. We show results under intense geometry simplifications. Our results preserve significant topology structures while the number of triangles is comparable to that of manually created models or primitive-based models.
Automatic fuzzy ontology generation for semantic Web

Ontology is an effective conceptualism commonly used for the semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA (fuzzy ontology generation framework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: fuzzy formal concept analysis, concept hierarchy generation, and fuzzy ontology generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed
Software rejuvenation scheduling using accelerated life testing

A number of studies have reported the phenomenon of “Software aging”, caused by resource exhaustion and characterized by progressive software performance degradation. In this article, we carry out an experimental study of software aging and rejuvenation for an on-line bookstore application, following the standard configuration of TPC-W benchmark. While real website is used for the bookstore, the clients are emulated. In order to reduce the time to application failures caused by memory leaks, we use the accelerated life testing (ALT) approach. We then select the Weibull time to failure distribution at normal level, to be used in a semi-Markov process, to compute the optimal software rejuvenation trigger interval. Since the validation of optimal rejuvenation trigger interval with emulated browsers will take an inordinate long time, we develop a simulation model to validate the ALT experimental results, and also estimate the steady-state availability to cross-validate the results of the semi-Markov availability model.
A UWB Unidirectional Antenna With Dual-Polarization

A novel ±45° dual-polarized unidirectional antenna element is presented, consisting of two cross center-fed tapered mono-loops and two cross electric dipoles located against a reflector for ultrawideband applications. The operation principle of the antenna including the use of elliptically tapered transmission line for transiting the unbalanced energy to the balanced energy is described. Designs with different reflectors-planar or conical-are investigated. A measured overlapped impedance bandwidth of 126% (SWR <; 2) is demonstrated. Due to the complementary nature of the structure, the antenna has a relatively stable broadside radiation pattern with low cross polarization and low back lobe radiation over the operating band. The measured gain of the proposed antenna varies from 4 to 13 dBi and 7 to 14.5 dBi for port 1 and port 2, respectively, over the operating band, when mounted against a conical backed reflector. The measured coupling between the two ports is below -25 dB over the operating band.
A 0.28 THz Power-Generation and Beam-Steering Array in CMOS Based on Distributed Active Radiators

In this paper, we present a scalable transmitter architecture for power generation and beam-steering at THz frequencies using a centralized frequency reference, sub-harmonic signal distribution, and local phase control. The power generation and radiator core is based on a novel method called distributed active radiation, which enables high conversion efficiency from DC to radiated terahertz power above fmax of a technology. The design evolution of the distributed active radiator (DAR) follows from an inverse design approach, where metal surface currents at different harmonics are formulated in the silicon chip for the desired electromagnetic field profiles. Circuits and passives are then designed conjointly to synthesize and control the surface currents. The DAR consists of a self-oscillating active electromagnetic structure, comprising of two loops which sustain out-of-phase currents at the fundamental frequency and in-phase currents at the second harmonic. The fundamental signal, thus gets, spatially filtered, while the second harmonic is radiated selectively, thereby consolidating signal generation, frequency multiplication, radiation of desired harmonic and filtration of undesired harmonics simultaneously in a small silicon footprint. A two-dimensional 4×4 radiating array implemented in 45 nm SOI CMOS (without high-resistivity substrate) radiates with an EIRP of +9.4 dBm at 0.28 THz and beam-steers in 2D over 80° in both azimuth and elevation. The chip occupies 2.7 mm × 2.7 mm and dissipates 820 mW of DC power. To the best of the authors' knowledge, this is the first reported integrated beam-scanning array at THz frequencies in silicon.
CQADupStack: A Benchmark Data Set for Community Question-Answering Research

This paper presents a benchmark dataset, CQADupStack, for use in community question-answering (cQA) research. It contains threads from twelve StackExchange subforums, annotated with duplicate question information. We provide pre-defined training and test splits, both for retrieval and classification experiments, to ensure maximum comparability between different studies using the set. Furthermore, it comes with a script to manipulate the data in various ways. We give an analysis of the data in the set, and report benchmark results on a duplicate question retrieval task using well established retrieval models.
Sustainability and ICT – An overview of the field

Sustainable development requires the decoupling of economic growth from environmental impacts and from the use of natural resources. This article gives an overview of existing approaches to using Information and Communication Technology (ICT) in the service of sustainability: Environmental Informatics, Green ICT, and Sustainable Human-Computer Interaction (HCI). These approaches are then discussed in the context of the Jevons paradox, an economic argument implying that technological efficiency alone will not produce sustainability. This consideration leads to the conclusion that a combination of efficiency and sufficiency strategies is the most effective way to stimulate innovations which will unleash ICT’s potential to support sustainability.
Binary malware image classification using machine learning with local binary pattern

Malware classification is a critical part in the cyber-security. Traditional methodologies for the malware classification typically use static analysis and dynamic analysis to identify malware. In this paper, a malware classification methodology based on its binary image and extracting local binary pattern (LBP) features is proposed. First, malware images are reorganized into 3 by 3 grids which is mainly used to extract LBP feature. Second, the LBP is implemented on the malware images to extract features in that it is useful in pattern or texture classification. Finally, Tensorflow, a library for machine learning, is applied to classify malware images with the LBP feature. Performance comparison results among different classifiers with different image descriptors such as GIST, a spatial envelop, and the LBP demonstrate that our proposed approach outperforms others.
High frequency half-bridge converter using multilayered coreless Printed Circuit Board step-down power transformer

This paper introduces high frequency half bridge DC-DC converter, using multilayered coreless Printed Circuit Board (PCB) step down power transformer. The converter is simulated and then implemented on a PCB. Complementary Pulse Width Modulated (PWM) signals are generated to turn on high and low side Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) alternately by a micro-controller. The isolated gate drive signals are provided to the high side MOSFET by using high frequency coreless PCB isolation transformer. We tested the converter for switching frequency range of 2 to 3 MHz, and for maximum input voltage up-to 170 V. The maximum output power achieved is 40 W and the maximum energy efficiency is approximately 82 %.
SIT: A Lightweight Encryption Algorithm for Secure Internet of Things

The Internet of Things (IoT) being a promising technology of the future is expected to connect billions of devices. The increased number of communication is expected to generate mountains of data and the security of data can be a threat. The devices in the architecture are essentially smaller in size and low powered. Conventional encryption algorithms are generally computationally expensive due to their complexity and requires many rounds to encrypt, essentially wasting the constrained energy of the gadgets. Less complex algorithm, however, may compromise the desired integrity. In this paper we propose a lightweight encryption algorithm named as Secure IoT (SIT). It is a 64-bit block cipher and requires 64-bit key to encrypt the data. The architecture of the algorithm is a mixture of feistel and a uniform substitution-permutation network. Simulations result shows the algorithm provides substantial security in just five encryption rounds. The hardware implementation of the algorithm is done on a low cost 8-bit micro-controller and the results of code size, memory utilization and encryption/decryption execution cycles are compared with benchmark encryption algorithms. The MATLAB code for relevant simulations is available online at https://goo.gl/Uw7E0W. Keywords—IoT; Security; Encryption; Wireless Sensor Network WSN; Khazad
iBCM: Interactive Bayesian Case Model Empowering Humans via Intuitive Interaction

Clustering methods optimize the partitioning of data points with respect to an internal metric, such as likelihood, in order to approximate the goodness of clustering. However, this internal metric does not necessarily translate into effective clustering from the user’s perspective. This work presents the interactive Bayesian Case Model (iBCM), a model that opens a communication channel between the clustering model and the user. Users can provide direct input to iBCM in order to achieve effective clustering results, and iBCM optimizes the clustering by creating a balance between what the data indicate and what makes the most sense to the user. This model provides feedback for users and does not assume any prior knowledge of machine learning on their part. We provide quantitative evidence that users are able to obtain more satisfactory clustering results through iBCM than without an interactive model. We also demonstrate the use of this method in a real-world setting where computer language class teachers utilize iBCM to cluster students’ coding assignments for grading.
Simplification and optimization of i-vector extraction

This paper introduces some simplifications to the i-vector speaker recognition systems. I-vector extraction as well as training of the i-vector extractor can be an expensive task both in terms of memory and speed. Under certain assumptions, the formulas for i-vector extraction—also used in i-vector extractor training—can be simplified and lead to a faster and memory more efficient code. The first assumption is that the GMM component alignment is constant across utterances and is given by the UBM GMM weights. The second assumption is that the i-vector extractor matrix can be linearly transformed so that its per-Gaussian components are orthogonal. We use PCA and HLDA to estimate this transform.
ROC curve equivalence using the Kolmogorov-Smirnov test

This paper describes a simple, non-parametric and generic test of the equivalence of Receiver Operating Characteristic (ROC) curves based on a modified Kolmogorov-Smirnov (KS) test. The test is described in relation to the commonly used techniques such as the Area Under the ROC curve (AUC) and the Neyman-Pearson method. We first review how the KS test is used to test the null hypotheses that the class labels predicted by a classifier are no better than random. We then propose an interval mapping technique that allows us to use two KS tests to test the null hypothesis that two classifiers have ROC curves that are equivalent. We demonstrate that this test discriminates different ROC curves both when one curve dominates another and when the curves cross and so are not discriminated by AUC. The interval mapping technique is then used to demonstrate that, although AUC has its limitations, it can be a model-independent and coherent measure of classifier performance.
Developing multiple hypotheses in behavioral ecology

Researchers in behavioral ecology are increasingly turning to research methods that allow the simultaneous evaluation of hypotheses. This approach has great potential to increase our scientific understanding, but researchers interested in the approach should be aware of its long and somewhat contentious history. Also, prior to implementing multiple hypothesis evaluation, researchers should be aware of the importance of clearly specifying a priori hypotheses. This is one of the more difficult aspects of research based on multiple hypothesis evaluation, and we outline and provide examples of three approaches for doing so. Finally, multiple hypothesis evaluation has some limitations important to behavioral ecologists; we discuss two practical issues behavioral ecologists are likely to face.
Reliability of measurements of muscle tone and muscle power in stroke patients.

to establish the reliability of the modified Ashworth scale for measuring muscle tone in a range of muscle groups (elbow, wrist, knee and ankle; flexors and extensors) and of the Medical Research Council scale for measuring muscle power in the same muscle groups and their direct antagonists.



a cross-sectional study involving repeated measures by two raters. We estimated reliability using the kappa statistic with quadratic weights (Kw).



an acute stroke ward, a stroke rehabilitation unit and a continuing care facility.



people admitted to hospital with an acute stroke-35 patients, median age 73 (interquartile range 65-80), 20 men and 15 women.



inter- and intra-rater agreement for the measurement of power was good to very good for all tested muscle groups (Kw = 0.84-0.96, Kw = 0.70-0.96). Inter- and intra-rater agreement for the measurement of tone in the elbow, wrist and knee flexors was good to very good (Kw = 0.73-0.96, Kw = 0.77-0.94). Inter- and intra-rater agreement for the measurement of tone in the ankle plantarflexors was moderate to good (Kw = 0.45-0.51, Kw = 0.59-0.64).



the Medical Research Council scale was reliable in the tested muscle groups. The modified Ashworth scale demonstrated reliability in all tested muscle groups except the ankle plantarflexors. If reliable measurement of tone at the ankle is required for a specific purpose (e.g. to measure the effect of therapeutic intervention), further work will be necessary.
A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.
Paying Attention to Descriptions Generated by Image Captioning Models

To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene. In this paper, we study the agreement between bottom-up saliency-based visual attention and object referrals in scene description constructs. We investigate the properties of human-written descriptions and machine-generated ones. We then propose a saliency-boosted image captioning model in order to investigate benefits from low-level cues in language models. We learn that (1) humans mention more salient objects earlier than less salient ones in their descriptions, (2) the better a captioning model performs, the better attention agreement it has with human descriptions, (3) the proposed saliencyboosted model, compared to its baseline form, does not improve significantly on the MS COCO database, indicating explicit bottom-up boosting does not help when the task is well learnt and tuned on a data, (4) a better generalization is, however, observed for the saliency-boosted model on unseen data.
Soil Deformation Models for Real-Time Simulation: A Hybrid Approach

The simulation of soil deformation in real-time is a challenging task. Realizing the strengths and weaknesses of particle and mesh-based approaches we propose a hybrid model that combines both. Together with an adaptive sampling method, which effectively reduces the number of particles in the simulation, and a selective update technique our method is applicable in real-time VR environments. Furthermore, in order to account for the high degree of dynamics in soil behavior we consider soil as non-homogeneous and account for its degree of compaction. By incorporating soil mechanical formulations in our model and considering several physically plausible parameters the presented method allows for the simulation of soil as the material empirically investigated by civil engineers and soil mechanicians for decades.
Optimising thermal efficiency of direct contact membrane distillation by brine recycling for small-scale seawater desalination Revised Manuscript Submitted to Desalination

A technique to optimise thermal efficiency using brine recycling during direct contact membrane distillation (DCMD) of seawater was investigated. By returning the hot brine to the feed tank, the system water recovery could be increased and the sensible heat of the hot brine was recovered to improve thermal efficiency. The results show that in the optimal water recovery range of 20 to 60% facilitated by brine recycling, the specific thermal energy consumption of the process could be reduced by more than half. It is also noteworthy that within this optimal water recovery range, the risk of membrane scaling is negligible DCMD of seawater at a constant water recovery of 70% was achieved for over 24. h without any scale formation on the membrane surface. In contrast, severe membrane scaling was observed when water recovery reached 80%. In addition to water recovery, other operating conditions such as feed temperature and water circulation rates could influence the process thermal efficiency. Increasing the feed temperature and reducing the circulation flow rates increased thermal efficiency. Increasing the feed temperature could also mitigate the negative effect of elevated feed concentration on the distillate flux, particularly at a high water recovery. Disciplines Engineering | Science and Technology Studies Publication Details Duong, H. C., Cooper, P., Nelemans, B., Cath, T. Y. & Nghiem, L. D. (2015). Optimising thermal efficiency of direct contact membrane distillation by brine recycling for small-scale seawater desalination. Desalination, 374 1-9. This journal article is available at Research Online: http://ro.uow.edu.au/eispapers/4237 1 Optimising thermal efficiency of direct contact membrane distillation by brine recycling for small-scale seawater desalination Revised Manuscript
Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless sensor networks (WSNs) are the main components of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in machine-to-machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.