nils-herrmann/clean_scirepeval huggingface dataset:
Using Multi-features and Ensemble Learning Method for Imbalanced Malware Classification

The ever-growing malware threats in the cyber spacecalls for techniques that are more effective than widely deployedsignature-based detection system. To counter large volumes ofmalware variants, machine learning techniques have been appliedfor automated malware classification. Despite these efforts haveachieved a certain success, the accuracy and efficiency stillremained inadequate to meet demand, especially when thesemachine learning techniques are used in the situation of multipleclass classification and imbalanced training data. Against thisbackdrop, the goal of this paper is to build a malware classificationsystem that could be used to improve the above mentionedsituation. Our system is based on multiple categories of staticfeatures and ensemble learning method. Compared to sometraditional systems it has the following advantages. Firstly, withmultiple categories of features, our system could classify malwareto their corresponding family effectively and efficiently and at thesame time avoid the influence of evasion in certain extent. Ourmethod don't need any unpacking process and extract featuresfrom the bytes file and disassembled asm file directly. Secondly, the system employed two efficient ensemble learning models, namely XGBoost and ExtraTreeClassifer, and also combinedstacking method to construct the final classifier. Finally, weexperimented our system with the dataset provided by Microsofthosted in Kaggle for malware classification competition, andthe final results show that our method could classify malwareto their family effectively and efficiently with the accuracy of0.9972 in training set and logloss of 0.00395 in testing set. Ourwork not only offer insights into how to use multiple features forclassification, but also shed light on how to develop a scalabletechniques for automated malware classification in practice.
Distance-Aware Encoding of Numerical Values for Privacy-Preserving Record Linkage

In this work, we propose Bit Vectors (BV), an accurate, distance-preserving encoding scheme for representing numerical data values in privacy-preserving tasks. Although many methods have been proposed in the literature for encoding strings, the problem of encoding numerical values has not been effectively addressed yet. In Privacy-Preserving Record Linkage (PPRL), a number of data custodians encode their records and submit them to a trusted third-party that is responsible to identify those records that refer to the same real-world entity. BV is supported by a strong theoretical foundation for embedding numerical values into an anonymization space in a way that preserves the initial distances. Key components of this embedding process are (a) the employed hash functions which, by utilizing random intervals, they allow for approximate matching, and (b) the threshold that is required by the distance computations, which we prove that can be specified in a way that guarantees accurate results.
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts

We present PubMed 200k RCT1, a new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3 million sentences. Each sentence of each abstract is labeled with their role in the abstract using one of the following classes: background, objective, method, result, or conclusion. The purpose of releasing this dataset is twofold. First, the majority of datasets for sequential shorttext classification (i.e., classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task. Second, from an application perspective, researchers need better tools to efficiently skim through the literature. Automatically classifying each sentence in an abstract would help researchers read abstracts more efficiently, especially in fields where abstracts may be long, such as the medical field.
Are test smells really harmful? An empirical study

Bad code smells have been defined as indicators of potential problems in source code. Techniques to identify and mitigate bad code smells have been proposed and studied. Recently bad test code smells (test smells for short) have been put forward as a kind of bad code smell specific to tests such a unit tests. What has been missing is empirical investigation into the prevalence and impact of bad test code smells. Two studies aimed at providing this missing empirical data are presented. The first study finds that there is a high diffusion of test smells in both open source and industrial software systems with 86 % of JUnit tests exhibiting at least one test smell and six tests having six distinct test smells. The second study provides evidence that test smells have a strong negative impact on program comprehension and maintenance. Highlights from this second study include the finding that comprehension is 30 % better in the absence of test smells.
Geometrical description and structural analysis of a modular timber structure

Born 1963; diploma in architecture and in civil engineering; Ph.D. in structural engineering RWTH Aachen; founder of Bureau d’études Weinand, Liège; professor at EPFL and director of the IBOIS/EPFL Lausanne; co-founder of SHEL Architecture Engineering and Production Design, Geneva. Olivier BAVEREL Associate Prof. Dr. Navier Research center, ENPC, Champs-sur-Marne ENSAG, France baverel@lami.enpc.fr
Parallel Grid Pooling for Data Augmentation

Convolutional neural network (CNN) architectures utilize downsampling layers, which restrict the subsequent layers to learn spatially invariant features while reducing computational costs. However, such a downsampling operation makes it impossible to use the full spectrum of input features. Motivated by this observation, we propose a novel layer called parallel grid pooling (PGP) which is applicable to various CNN models. PGP performs downsampling without discarding any intermediate feature. It works as data augmentation and is complementary to commonly used data augmentation techniques. Furthermore, we demonstrate that a dilated convolution can naturally be represented using PGP operations, which suggests that the dilated convolution can also be regarded as a type of data augmentation technique. Experimental results based on popular image classification benchmarks demonstrate the effectiveness of the proposed method. Code is available at: https://github.com/akitotakeki/pgp-chainer.
A high performance fingerprint liveness detection method based on quality related features

A new software-based liveness detection approach using a novel fingerprint parameterization based on quality related features is proposed. The system is tested on a highly challenging database comprising over 10,500 real and fake images acquired with five sensors of different technologies and covering a wide range of direct attack scenarios in terms of materials and procedures followed to generate the gummy fingers. The proposed solution proves to be robust to the multi-scenario dataset, and presents an overall rate of 90% correctly classified samples. Furthermore, the liveness detection method presented has the added advantage over previously studied techniques of needing just one image from a finger to decide whether it is real or fake. This last characteristic provides the method with very valuable features as it makes it less intrusive, more user friendly, faster and reduces its implementation costs.
Multiphysics Modeling and Impact of Thermal Boundary Resistance in Phase Change Memory Devices

Among the many emerging non-volatile memory technologies, chalcogenide (i.e. GeSbTe/GST) based phase change random access memory (PRAM) has shown particular promise. While accurate simulations are required for reducing programming current and enabling higher integration density, many challenges remain for improved simulation of PRAM cell operation including nanoscale thermal conduction and phase change. This work simulates the fully coupled electrical and thermal transport and phase change in 2D PRAM geometries, with specific attention to the impact of thermal boundary resistance between the GST and surrounding materials. For GST layer thicknesses between 25 and 75nm, the interface resistance reduces the predicted programming current and power by 31% and 53%, respectively, for a typical reset transition. The calculations also show the large sensitivity of programming voltage to the GST thermal conductivity. These results show the importance of temperature-dependent thermal properties of materials and interfaces in PRAM cells
Inositol supplement improves clinical pregnancy rate in infertile women undergoing ovulation induction for ICSI or IVF-ET

Pretreatment of myoinositol is a very new method that was evaluated in multiple small studies to manage poor ovarian response in assisted reproduction. This study was to determine the efficacy of myoinositol supplement in infertile women undergoing ovulation induction for intracytoplasmic sperm injection (ICSI) or in vitro fertilization embryo transfer (IVF-ET).



A meta-analysis and systematic review of published articles evaluating the efficacy of myo-inositol in patients undergoing ovulation induction for ICSI or IVF-ET was performed.



Seven trials with 935 women were included. Myoinositol supplement was associated with significantly improved clinical pregnancy rate [95% confidence interval (CI), 1.04-1.96; P = .03] and abortion rate (95% CI, 0.08-0.50; P = .0006). Meanwhile, Grade 1 embryos proportion (95% CI, 1.10-2.74; P = .02), germinal vescicle and degenerated oocytes retrieved (95% CI, 0.11-0.86; P = .02), and total amount of ovulation drugs (95% CI, -591.69 to -210.39; P = .001) were also improved in favor of myo-inositol. There were no significant difference in total oocytes retrieved, MII stage oocytes retrieved, stimulation days, and E2 peak level.



Myoinositol supplement increase clinical pregnancy rate in infertile women undergoing ovulation induction for ICSI or IVF-ET. It may improve the quality of embryos, and reduce the unsuitable oocytes and required amount of stimulation drugs.
DeepFruits: A Fruit Detection System Using Deep Neural Networks

This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0 . 807 to 0 . 838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.
Chapter 2 Wireless Power Transfer ( WPT ) for Electric Vehicles ( EVs ) — Present and Future Trends

Plug In Electric Vehicles in Smart Grids, Power Systems, DOI 10.1007/978-981-287-299-9_2 Abstract 100 year old gasoline engine technology vehicles have now become one of the major contributors of greenhouse gases. Plug-in Electric Vehicles (PEVs) have been proposed to achieve environmental friendly transportation. Even though the PEV usage is currently increasing, a technology breakthrough would be required to overcome battery related drawbacks. Although battery technology is evolving, drawbacks inherited with batteries such as; cost, size, weight, slower charging characteristic and low energy density would still be dominating constrains for development of EVs. Furthermore, PEVs have not been accepted as preferred choice by many consumers due to charging related issues. To address battery related limitations, the concept of dynamic Wireless Power Transfer (WPT) enabled EVs have been proposed in which EV is being charged while it is in motion. WPT enabled infrastructure has to be employed to achieve dynamic EV charging concept. The weight of the battery pack can be reduced as the required energy storage is lower if the vehicle can be powered wirelessly while driving. Stationary WPT charging where EV is charged wirelessly when it is stopped, is simpler than dynamic WPT in terms of design complexity. However, stationary WPT does not increase vehicle range compared to wired-PEVs. State-of-art WPT technology for future transportation is discussed in this chapter. Analysis of the WPT system and its performance indices are introduced. Modelling the WPT system using different methods such as equivalent circuit theory, two port network theory and coupled mode theory is described illustrating their own merits in Sect. 2.3. Both stationary and dynamic WPT for EV applications are illustrated in Sect. 2.4. Design challenges and optimization directions are analysed in Sect. 2.5. Adaptive tuning techniques such as adaptive impedance matching and
Smart homes and home health monitoring technologies for older adults: A systematic review

Around the world, populations are aging and there is a growing concern about ways that older adults can maintain their health and well-being while living in their homes.



The aim of this paper was to conduct a systematic literature review to determine: (1) the levels of technology readiness among older adults and, (2) evidence for smart homes and home-based health-monitoring technologies that support aging in place for older adults who have complex needs.



We identified and analyzed 48 of 1863 relevant papers. Our analyses found that: (1) technology-readiness level for smart homes and home health monitoring technologies is low; (2) the highest level of evidence is 1b (i.e., one randomized controlled trial with a PEDro score ≥6); smart homes and home health monitoring technologies are used to monitor activities of daily living, cognitive decline and mental health, and heart conditions in older adults with complex needs; (3) there is no evidence that smart homes and home health monitoring technologies help address disability prediction and health-related quality of life, or fall prevention; and (4) there is conflicting evidence that smart homes and home health monitoring technologies help address chronic obstructive pulmonary disease.



The level of technology readiness for smart homes and home health monitoring technologies is still low. The highest level of evidence found was in a study that supported home health technologies for use in monitoring activities of daily living, cognitive decline, mental health, and heart conditions in older adults with complex needs.
LoRa — A survey of recent research trends

Nowadays the Internet of Things (IoT) is one of the most promising application areas in information technology for future products and services. Therefore, it is not surprising that new technologies arise, which are marketed as “the most useful technology” for applications in IoT devices. In this study, we focus on the new communication technology called LoRaWAN. Our aim is to evaluate the usefulness of LoRaWAN technology in the field of IoT, and especially in sensor network solutions. The research methodology is a literature survey and primary articles were selected by means of a systematic literature review. More than fifty suitable research papers were identified. From these, the following research questions were formulated: “How to categorize LoRa research papers?” and “What are the application trends in LoRa?” This study presents a way to categorize research papers with diverse themes. In addition, this survey reveals the most recent trends in research and practical applications of LoRa. Finally, this study will give recommendations for researchers and practitioners on how the advantages of LoRa-based technologies could be fully exploited in the development of IoT systems and solutions.
RGB-D multi-view object detection with object proposals and shape context

We propose a novel approach for multi-view object detection in 3D scenes reconstructed from RGB-D sensor. We utilize shape based representation using local shape context descriptors along with the voting strategy which is supported by unsupervised object proposals generated from 3D point cloud data. Our algorithm starts with a single-view object detection where object proposals generated in 3D space are combined with object specific hypotheses generated by the voting strategy. To tackle the multi-view setting, the data association between multiple views enabled view registration and 3D object proposals. The evidence from multiple views is combined in simple bayesian setting. The approach is evaluated on the Washington RGB-D scenes datasets ,  containing several classes of objects in a table top setting. We evaluated our approach against the other state-of-the-art methods and demonstrated superior performance on the same dataset.
JS Gero and U Lindemann (eds), Human Behaviour in Design’05, 2005 Key Centre of Design Computing and Cognition, University of Sydney, pp 47-58 DESIGN BEHAVIOUR MEASUREMENT BY QUANTIFYING LINKOGRAPHY IN PROTOCOL STUDIES OF DESIGNING

This paper proposes approaches to measure linkography in protocol studies of designing. It outlines the ideas behind using clustering and Shannon’s entropy as measures of designing behaviour. Hypothetical cases are used to illustrate the methods. The paper concludes that these methods may form the basis of a new tool to assess designer behaviour in terms of chunking of design ideas and the opportunities for idea development.
Pineal melatonin: cell biology of its synthesis and of its physiological interactions.

UNTIL 35 yr ago, most scientists did not take research on the pineal gland seriously. The decade beginning in 1956, however, provided several discoveries that laid the foundation for what has become a very active area of investigation. These important early observations included the findings that, 1), the physiological activity of the pineal is influenced by the photoperiodic environment (1–5); 2), the gland contains a substance, N-acetyl-5-methoxytryptamine or melatonin, which has obvious endocrine capabilities (6, 7); 3), the function of the reproductive system in photoperiodically dependent rodents is inextricably linked to the physiology of the pineal gland (5, 8, 9); 4), the sympathetic innervation to the pineal is required for the gland to maintain its biosynthetic and endocrine activities (10, 11); and 5), the pineal gland can be rapidly removed from rodents with minimal damage to adjacent neural structures using a specially designed trephine (12).
Mining : Techniques and its Application

Text mining has become an exciting research field as it tries to discover valuable information from unstructured texts. The unstructured texts which contain vast amount of information cannot simply be used for further processing by computers. Therefore, exact processing methods, algorithms and techniques are vital in order to extract this valuable information which is completed by using text mining. In this paper, we have discussed general idea of text mining and comparison of its techniques. In addition, we briefly discuss a number of text mining applications which are used presently and in future.Text mining has become an exciting research field as it tries to discover valuable information from unstructured texts. The unstructured texts which contain vast amount of information cannot simply be used for further processing by computers. Therefore, exact processing methods, algorithms and techniques are vital in order to extract this valuable information which is completed by using text mining. In this paper, we have discussed general idea of text mining and comparison of its techniques. In addition, we briefly discuss a number of text mining applications which are used presently and in future.
Dissociating cognitive from affective theory of mind: A TMS study

"Theory of Mind" (ToM), i.e., the ability to infer other persons' mental states, is a key function of social cognition. It is increasingly recognized to form a multidimensional construct. One differentiation that has been proposed is that between cognitive and affective ToM, whose neural correlates remain to be identified. We aimed to ascertain the possible role of the right dorsolateral prefrontal cortex (DLPFC) for cognitive ToM as opposed to affective ToM processes.



1Hz repetitive transcranial magnetic stimulation (rTMS) was used to interfere offline with cortical function of the right DLPFC in healthy male subjects who subsequently had to perform a computerized task assessing cognitive and affective ToM.



RTMS over the right DLPFC induced a selective effect on cognitive but not affective ToM. More specifically, a significant acceleration of reaction times in cognitive ToM compared to affective ToM and control items was observed in the experimental (right DLPFC) compared to the control (vertex) rTMS stimulation condition.



Our findings provide evidence for the functional independence of cognitive from affective ToM. Furthermore, they point to an important role of the right DLPFC within neural networks mediating cognitive ToM. Possible underlying mechanisms of the acceleration of cognitive ToM processing under rTMS are discussed.
Capabilities , Processes , and Performance of Knowledge Management : A Structural Approach

The purpose of this study is to examine structural relationships among the capabilities, processes, and performance of knowledge management, and suggest strategic directions for the successful implementation of knowledge management. To serve this purpose, the authors conducted an extensive survey of 68 knowledge management-adopting Korean firms in diverse industries and collected 215 questionnaires. Analyzing hypothesized structural relationships with the data collected, they found that there exists statistically significant relationships among knowledge management capabilities, processes, and performance. The empirical results of this study also support the wellknown strategic hypothesis of the balanced scorecard (BSC).
Quantitative Analysis of the Full Bitcoin Transaction Graph

The Bitcoin scheme is a rare example of a large scale global payment system in which all the transactions are publicly accessible (but in an anonymous way). We downloaded the full history of this scheme, and analyzed many statistical properties of its associated transaction graph. In this paper we answer for the first time a variety of interesting questions about the typical behavior of users, how they acquire and how they spend their bitcoins, the balance of bitcoins they keep in their accounts, and how they move bitcoins between their various accounts in order to better protect their privacy. In addition, we isolated all the large transactions in the system, and discovered that almost all of them are closely related to a single large transaction that took place in November 2010, even though the associated users apparently tried to hide this fact with many strange looking long chains and fork-merge structures in the transaction graph.