nils-herrmann/clean_scirepeval huggingface dataset:
Modeling and Calibration of Inertial and Vision Sensors

This paper is concerned with the problem of estimating the relative translation and orientation of an inertial measurement unit and a camera, which are rigidly connected. The key is to realize that this problem is in fact an instance of a standard problem within the area of system identification, referred to as a gray-box problem. We propose a new algorithm for estimating the relative translation and orientation, which does not require any additional hardware, except a piece of paper with a checkerboard pattern on it. The method is based on a physical model which can also be used in solving for example sensor fusion problems. The experimental results show that the method works well in practice, both for perspective and spherical cameras.
GAPLE: Generalizable Approaching Policy LEarning for Robotic Object Searching in Indoor Environment

We study the problem of learning a generalizable action policy for an intelligent agent to actively approach an object of interest in indoor environment solely from its visual inputs. While scene-driven or recognition-driven visual navigation has been widely studied, prior efforts suffer severely from the limited generalization capability. In this paper, we first argue the object searching task is environment dependent while the approaching ability is general. To learn a generalizable approaching policy, we present a novel solution dubbed as GAPLE which adopts two channels of visual features: depth and semantic segmentation, as the inputs to the policy learning module. The empirical studies conducted on the House3D dataset as well as on a physical platform in a real world scenario validate our hypothesis, and we further provide indepth qualitative analysis.
The Computational-Linguistic Approach to Forensic Authorship Attribution

This article examines the diversity of methods in authorship attribution through a lens which focuses attention on a single common element. The current state of authorship attribution study is spread throughout so many academic and non -academic disciplines that it is nigh impossible to describe all of the various assumptions about language and authorship. The disciplines involved in authorship attributions range over Classics, Biblical exegesis, Paleography, Communication and Rhetoric, English literary criticism, Handwriting examination, General Linguistics, Sociolinguistics, Computational Linguistics, Statistics, and Machine Learning. Given the breadth of this list, it is no wonder that the current state of authorship attribution appears to be a jumbled mass of multiple contrasts.
Improvement of blast-induced fragmentation and crusher efficiency by means of optimized drilling and blasting in Aitik

Improvement of blast-induced fragmentation and crusher efficiency by means of optimized drilling and blasting in Aitik.  The thesis project presented in this report was conducted in Boliden's Aitik mine; thereby I wish to gratefully thank Boliden Mines for their financial and technical support. I would like to express my very great appreciation to Ulf Nyberg, my supervisor at Luleå University of Technology, and Evgeny Novikov, my supervisor in Boliden for their patient guidance, technical support and valuable suggestions on this project. Useful advice given by Dr. Daniel Johansson is also greatly appreciated; I wish to acknowledge the constructive recommendations provided by Nikolaos Petropoulos as well. My special thanks are extended to the staff of Boliden Mines for all their help and technical support in Aitik. I am particularly grateful for the assistance given by Torbjörn Krigsman, Nils Johansson and Peter Palo. I would also like to acknowledge the help provided collection in Aitik mine. I would also like to thank Forcit company for their assistance with the collection of the data, my special thanks goes to Per-Arne Kortelainen for all his contribution. Finally my deep gratitude goes to my parents for their invaluable support, patience and encouragement throughout my academic studies.  Rock blasting is one of the most dominating operations in open pit mining efficiency. As many downstream processes depend on the blast-induced fragmentation, an optimized blasting strategy can influence the total revenue of a mine to a large extent. Boliden Aitik mine in northern Sweden is one of the largest copper mines in Europe. The annual production of the mine is expected to reach 36 million tonnes of ore in 2014; so continuous efforts are being made to boost the production. Highly automated equipment and new processing plant, in addition to new crushers, have sufficient capacity to reach the production goals; the current obstacle in the process of production increase is a bottleneck in crushers caused by oversize boulders. Boulders require extra efforts for secondary blasting or hammer breakage and if entered the crushers, they cause downtimes. Therefore a more evenly distributed fragmentation with less oversize material can be advantageous. Furthermore, a better fragmentation can cause a reduction in energy costs by demanding less amounts of crushing energy. In order to achieve a more favorable fragmentation, two alternative blast designs in addition to a reference design were tested and the results were evaluated and compared to the …
Rosetta: Large Scale System for Text Detection and Recognition in Images

In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta , designed to process images uploaded daily at Facebook scale. Sharing of image content has become one of the primary ways to communicate information among internet users within social networks such as Facebook, and the understanding of such media, including its textual information, is of paramount importance to facilitate search and recommendation applications. We present modeling techniques for efficient detection and recognition of text in images and describe Rosetta 's system architecture. We perform extensive evaluation of presented technologies, explain useful practical approaches to build an OCR system at scale, and provide insightful intuitions as to why and how certain components work based on the lessons learnt during the development and deployment of the system.
Advantages and Challenges of Using Augmented Reality for Library Orientations in an Academic/Research Library Setting

This study aims to make an inquiry regarding the advantages and challenges of integrating augmented reality (AR) into the library orientation programs of academic/research libraries. With the vast number of emerging technologies that are currently being introduced to the library world, it is essential for academic librarians to fully utilize these technologies to their advantage. However, it is also of equal importance for them to first make careful analysis and research before deciding whether to adopt a certain technology or not. AR offers a strategic medium through which librarians can attach digital information to real-world objects and simply let patrons interact with them. It is a channel that librarians can utilize in order to disseminate information and guide patrons in their studies or researches. And while it is expected for AR to grow tremendously in the next few years, it becomes more inevitable for academic librarians to acquire related IT skills in order to further improve the services they offer in their respective colleges and universities. The study shall employ the pragmatic approach to research, conducting an extensive review of available literature on AR as used in academic libraries, designing a prototype to illustrate how AR can be integrated to an existing library orientation program, and performing surveys and interviews on patrons and librarians who used it. This study can serve as a guide in order for academic librarians to assess whether implementing AR in their respective libraries will be beneficial to them or not.
Digital Forensics : Emerging Trends and Analysis of Counter-Security Environment

Corporates and organizations across the globe are spending huge sums on information security as they are reporting an increase in security related incidents. The proliferation of cloud, social network and multiple mobile device usage is on one side represent an opportunity and benefits to the organisation and on other side have posed new challenges for those policing cybercrimes. Cybercriminals have devised more sophisticated and targeted methods/techniques to trap victim and breach security setups. The emergence of highly technical nature of digital crimes has created a new branch of science known as digital forensics. Digital Forensics is the field of forensics science that deals with digital crimes and crimes involving computers. This paper focuses on briefing of digital forensics, various phases of digital forensics, digital forensics tools and its comparisons, and emerging trends and issues in this fascinated area.  Digital forensics, Digital evidence, Digital forensics tools, Network intrusion, Information security,
Genetic neural network based data mining in prediction of heart disease using risk factors

Data mining techniques have been widely used in clinical decision support systems for prediction and diagnosis of various diseases with good accuracy. These techniques have been very effective in designing clinical support systems because of their ability to discover hidden patterns and relationships in medical data. One of the most important applications of such systems is in diagnosis of heart diseases because it is one of the leading causes of deaths all over the world. Almost all systems that predict heart diseases use clinical dataset having parameters and inputs from complex tests conducted in labs. None of the system predicts heart diseases based on risk factors such as age, family history, diabetes, hypertension, high cholesterol, tobacco smoking, alcohol intake, obesity or physical inactivity, etc. Heart disease patients have lot of these visible risk factors in common which can be used very effectively for diagnosis. System based on such risk factors would not only help medical professionals but it would give patients a warning about the probable presence of heart disease even before he visits a hospital or goes for costly medical checkups. Hence this paper presents a technique for prediction of heart disease using major risk factors. This technique involves two most successful data mining tools, neural networks and genetic algorithms. The hybrid system implemented uses the global optimization advantage of genetic algorithm for initialization of neural network weights. The learning is fast, more stable and accurate as compared to back propagation. The system was implemented in Matlab and predicts the risk of heart disease with an accuracy of 89%.
Information security knowledge and behavior: An adapted model of technology acceptance

Information security risks have become a significant concern for users of computer information technology. However, users' behavior of acceptance and actual use of available information security solutions has not been commensurate with the level of their information security concerns. Traditional technology acceptance theory (TAM) emphasizes the factors of perceived usefulness and perceived ease of use in acceptance of technology. There has been little research focus and consensus on the role of knowledge in user adoptions of information security solutions. This paper proposes a new and adapted model of technology acceptance that focuses on the relationship between users' knowledge of information security and their behavioral intention to use information security solutions. This study employs a survey method that measures users' knowledge of information security and their attitude and intention toward using information security solutions. Statistical analysis of the results indicates a positive correlation between user knowledge of information security and user intention to adopt and use information security solutions.
What Is the Evidence to Support the Use of Therapeutic Gardens for the Elderly?

Horticulture therapy employs plants and gardening activities in therapeutic and rehabilitation activities and could be utilized to improve the quality of life of the worldwide aging population, possibly reducing costs for long-term, assisted living and dementia unit residents. Preliminary studies have reported the benefits of horticultural therapy and garden settings in reduction of pain, improvement in attention, lessening of stress, modulation of agitation, lowering of as needed medications, antipsychotics and reduction of falls. This is especially relevant for both the United States and the Republic of Korea since aging is occurring at an unprecedented rate, with Korea experiencing some of the world's greatest increases in elderly populations. In support of the role of nature as a therapeutic modality in geriatrics, most of the existing studies of garden settings have utilized views of nature or indoor plants with sparse studies employing therapeutic gardens and rehabilitation greenhouses. With few controlled clinical trials demonstrating the positive or negative effects of the use of garden settings for the rehabilitation of the aging populations, a more vigorous quantitative analysis of the benefits is long overdue. This literature review presents the data supporting future studies of the effects of natural settings for the long term care and rehabilitation of the elderly having the medical and mental health problems frequently occurring with aging.
Database architecture for content-based image retrieval

This paper describes visual interaction mechanisms for image database systems. The typical mechanisms for visual interactions are query by visual example (QVE) and query by subjective descriptions (QBD). The former includes a sketch retrieval function and a similarity retrieval function, and the latter includes a sense retrieval function. We adopt both an image model and a user model to interpret and operate the contents of image data from user's viewpoint. The image model describes the graphical features of image data, while the user model reflects the visual perception processes of the user. These models, automatically created by image analysis and statistical learning, are referred as abstract indexes stored in relational tables. These algorithms are developed on our experimental database system, the TRADEMARK and the ART MUSEUM.
Force calibration of KUKA LWR-like robots including embedded joint torque sensors and robot structure

The Kuka LWR is equipped with torque sensors mounted into the actuated joints. Each torque sensor is calibrated separately before it is mounted on the robot. This needs a second calibration at the last stage of the assembling of the robot in order to take into account the effect of the robot structure through it's jacobian matrix. This final calibration is necessary to improve the accuracy of the estimation of the interaction wrench of the robot with its environment. However, the proposed calibration techniques are usually complicated, time-consuming, and must be carried out before assembling the sensors on the robot. In this paper, a simple and fast method for calibrating the sensors once they are assembled on the robot is presented. The method is based on the least squares solution of an over-determined linear system obtained with the robot inverse dynamic identification model in which are included the sensor gains. This model is calculated with available sensor measurement and joint position sampled data while the robot is tracking some reference trajectories without load on the robot and some trajectories with a known payload fixed on the robot. The method is experimentally validated on the Kuka LWR4+ but can be applied to any similar kind of robot equipped with joint torque sensors.
Security lapses and the omission of information security measures: A threat control model and empirical test

Organizations and individuals are increasingly impacted by misuses of information that result from security lapses. Most of the cumulative research on information security has investigated the technical side of this critical issue, but securing organizational systems has its grounding in personal behavior. The fact remains that even with implementing mandatory controls, the application of computing defenses has not kept pace with abusers’ attempts to undermine them. Studies of information security contravention behaviors have focused on some aspects of security lapses and have provided some behavioral recommendations such as punishment of offenders or ethics training. While this research has provided some insight on information security contravention, they leave incomplete our understanding of the omission of information security measures among people who know how to protect their systems but fail to do so. Yet carelessness with information and failure to take available precautions contributes to significant civil losses and even to crimes. Explanatory theory to guide research that might help to answer important questions about how to treat this omission problem lacks empirical testing. This empirical study uses protection motivation theory to articulate and test a threat control model to validate assumptions and better understand the ‘‘knowing-doing” gap, so that more effective interventions can be developed.  (2008) 2799–2816
Enhancing data security using DES-based cryptography and DCT-based steganography

The security and privacy of the data transmitted is an important aspect of the exchange of information on the Internet network. Cryptography and Steganography are two of the most commonly used digital data security techniques. In this research, we proposed the combination of the cryptographic method with Data Encryption Standard (DES) algorithm and the steganographic method with Discrete Cosine Transform (DCT) to develop a digital data security application. The application can be used to secure document data in Word, Excel, Powerpoint or PDF format. Data encrypted with DES algorithm and further hidden in image cover using DCT algorithm. The results showed that the quality of the image that has been inserted (stego-image) is still in a good category with an average PSNR value of 46.9 dB. Also, the experiment results show that the average computational time of 0.75 millisecond/byte, an average size increase of 4.79 times and a success rate of 58%. This research can help solve the problem of data and information security that will be sent through a public network like the internet.
Research on Flipped Classroom Design and Implication Based on Edmodo Platform

Based on the limitations of traditional English class, an English listening class was designed by Edmodo platform through making use of the advantages of flipped classroom. On this class, students will carry out online autonomous learning before class, teacher will guide students learning collaboratively in class, as well as after-school reflection and summary will be realized. By analyzing teaching effect on flipped classroom, it can provide reference and teaching model for English listening classes in local universities.
SceneNN: A Scene Meshes Dataset with aNNotations

Several RGB-D datasets have been publicized over the past few years for facilitating research in computer vision and robotics. However, the lack of comprehensive and fine-grained annotation in these RGB-D datasets has posed challenges to their widespread usage. In this paper, we introduce SceneNN, an RGB-D scene dataset consisting of 100 scenes. All scenes are reconstructed into triangle meshes and have per-vertex and per-pixel annotation. We further enriched the dataset with fine-grained information such as axis-aligned bounding boxes, oriented bounding boxes, and object poses. We used the dataset as a benchmark to evaluate the state-of-the-art methods on relevant research problems such as intrinsic decomposition and shape completion. Our dataset and annotation tools are available at http://www.scenenn.net.
HADM: Hybrid Analysis for Detection of Malware

Android is the most popular mobile operating system with a market share of over 80% [23]. Due to its popularity and also its open source nature, Android is now the platform most targeted by malware, creating an urgent need for effective defense mechanisms to protect Android-enabled devices. In this paper, we propose a novel Android malware classification method called HADM, Hybrid Analysis for Detection of Malware. We first extract static and dynamic information, and convert this information into vector-based representations. It has been shown that combining advanced features derived by deep learning with the original features provides significant gains . Therefore, we feed both the original dynamic and static feature vector sets to a Deep Neural Network (DNN) which outputs a new set of features. These features are then concatenated with the original features to construct DNN vector sets. Different kernels are then applied onto the DNN vector sets. We also convert the dynamic information into graph-based representations and apply graph kernels onto the graph sets. Learning results from various vector and graph feature sets are combined using hierarchical Multiple Kernel Learning (MKL)  to build a final hybrid classifier.
Improving a credit card fraud detection system using genetic algorithm

In this study we undertook the credit card fraud detection problem of a bank and tried to improve the performance of an existing solution. In doing so, we did not undertake the typical objective of maximizing the number of correctly classified transactions but rather we defined a new objective function where the misclassification costs are variable and thus, correct classification of some transactions are more important than correctly classifying the others. For this purpose we made an application of genetic algorithms which is a novel one in the related literature both in terms of the application domain and the cross-over operator used. The algorithm is applied to real life data where the savings obtained are almost three times the current practice.
Thermal history of the Earth and its petrological expression

T.M. Harrison Non-arc basalts of Archean and Proterozoic age have model primary magmas that exhibit mantle potential temperatures TP that increase from 1350 °C at the present to a maximum of ∼1500–1600 °C at 2.5–3.0 Ga. The overall trend of these temperatures converges smoothly to that of the present-day MORB source, supporting the interpretation that the non-arc basalts formed by the melting of hot ambient mantle, not mantle plumes, and that they can constrain the thermal history of the Earth. These petrological results are very similar to those predicted by thermal models characterized by a low Urey ratio and more sluggish mantle convection in the past. We infer that the mantle was warming in deep Archean–Hadean time because internal heating exceeded surface heat loss, and it has been cooling from 2.5 to 3.0 Ga to the present. Non-arc Precambrian basalts are likely to be similar to those that formed oceanic crust and erupted on continents. It is estimated that ∼25–35 km of oceanic crust formed in the ancient Earth by about 30% melting of hot ambient mantle. In contrast, komatiite parental magmas reveal TP that are higher than those of non-arc basalts, consistent with the hot plume model. However, the associated excess magmatism was minor and oceanic plateaus, if they existed, would have had subtle bathymetric variations, unlike those of Phanerozoic oceanic plateaus. Primary magmas of Precambrian ambient mantle had 18–24% MgO, and they left behind residues of harzburgite that are now found as xenoliths of cratonic mantle. We infer that primary basaltic partial melts having 10–13% MgO are a feature of Phanerozoic magmatism, not of the early Earth, which may be why modern-day analogs of oceanic crust have not been reported in Archean greenstone belts.