nils-herrmann/clean_scirepeval huggingface dataset:
Random Register Renaming to Foil DPA

Techniques such as DPA and SPA can be used to find the secret keys stored in smart-cards. These techniques have caused concern for they can allow people to recharge their stored value smartcards (in effect printing money), or illegally use phone or digital TV services. We propose an addition to current processors which will counteract these techniques. By randomising register usage, we can hide the secret key stored in a smartcard. The extension we propose can be added to existing processors, and is transparent to the algorithm.
Liveness and spoofing in fingerprint identification: issues and challenges

The fingerprint liveness detection refers to the inspection of the finger characteristics to ensure whether the input finger is live or artificial. A number of fingerprint identification systems are used widely and implemented at various important places such as border and immigration services. However, it is not declared by the manufacturers of these systems whether liveness detection is actually implemented. Possible measures to detect liveness are only proposed in patents and published literature. There are three major schemes, which are reported in fingerprint liveness literature. These coupled with the additional hardware, software, or combination of fingerprint with other identifications is aimed to verify the liveness in submitted fingerprints. The hardware-based methods use auxiliary sensors to detect the biological and physiological measurements from finger, whereas software-based methods utilize changes in physical properties that take place in early stages of pressing the finger. In this paper, various fingerprint liveness detection methods, which are categorized as voluntary and involuntary, are explored. These categories are based on determining the presence of a user by different responses from either voluntary (e.g. passwords or multiple biometrics) or involuntary (e.g. pulse oximetry or blood pressure) liveness detections. The main objective of this paper is to critically review the voluntary and involuntary fingerprint liveness detection techniques proposed in the literature, and discuss their effectiveness and possible limitations.  Liveness Detection, Spoofing, Fingerprint, Biometric Security, Voluntary and Involuntary Technique
Real-time CPU-based large-scale 3D mesh reconstruction

In Robotics, especially in this era of autonomous driving, mapping is one key ability of a robot to be able to navigate through an environment, localize on it and analyze its traversability. To allow for real-time execution on constrained hardware, the map usually estimated by feature-based or semidense SLAM algorithms is a sparse point cloud; a richer and more complete representation of the environment is desirable. Existing dense mapping algorithms require extensive use of GPU computing and they hardly scale to large environments; incremental algorithms from sparse points still represent an effective solution when light computational effort is needed and big sequences have to be processed in real-time. In this paper we improved and extended the state of the art incremental manifold mesh algorithm proposed in  and extended in . While these algorithms do not achieve real-time and they embed points from SLAM or Structure from Motion only when their position is fixed, in this paper we propose the first incremental algorithm able to reconstruct a manifold mesh in real-time through single core CPU processing which is also able to modify the mesh according to 3D points updates from the underlying SLAM algorithm. We tested our algorithm against two state of the art incremental mesh mapping systems on the KITTI dataset, and we showed that, while accuracy is comparable, our approach is able to reach real-time performances thanks to an order of magnitude speed-up.
Development of High Density Fan Out Wafer Level Package (HD FOWLP) with Multi-layer Fine Pitch RDL for Mobile Applications

Recently, Fan-out Wafer Level Packaging (FOWLP) has been emerged as a promising technology to meet the ever increasing demands of the consumer electronic products. However, conventional FOWLP technology is limited to small size packages with single chip and Low to Mid-range Input/ Output (I/O) count due to die shift, warpage and RDL scaling issues. In this paper, we are presenting new RDL-First FOWLP approach which enables RDL scaling, overcomes the die shift, die protrusion and warpage challenges of conventional FOWLP, and extend the FOWLP technology for multi-chip and high I/O count package applications. RDL-First FOWLP process integration flow was demonstrated and fabricated test vehicles of large multi-chip package of 20 x 20 mm2 with 3 layers fine pitch RDL of LW/LS of 2μm/2μm and ~2400 package I/Os. Two Through Mold Interconnections (TMI) fabrication approaches (tall Cu pillar and vertical Cu wire) were evaluated on this platform for Package-on-Package (PoP) application. Backside RDL process on over molded Chip-to-Wafer (C2W) with carrier wafer was demonstrated for PoP applications. Laser de-bonding and sacrificial release layer material cleaning processes were established, and successfully used in the integration flow to fabricate the test vehicles. Assembly processes were optimized and successfully demonstrated large multi-chip RDL-first FOWLP package and PoP assembly on test boards. The large multi-chip FOWLP packages samples were passed JEDEC component level test Moisture Sensitivity Test  and 30 drops of board level drop test, and results will be presented.
CMC: A Pragmatic Approach to Model Checking Real Code

Many system errors do not emerge unless some intricate sequence of events occurs. In practice, this means that most systems have errors that only trigger after days or weeks of execution. Model checking [4] is an effective way to find such subtle errors. It takes a simplified description of the code and exhaustively tests it on all inputs, using techniques to explore vast state spaces efficiently. Unfortunately, while model checking systems code would be wonderful, it is almost never done in practice: building models is just too hard. It can take significantly more time to write a model than it did to write the code. Furthermore, by checking an abstraction of the code rather than the code itself, it is easy to miss errors.The paper's first contribution is a new model checker, CMC, which checks C and C++ implementations directly, eliminating the need for a separate abstract description of the system behavior. This has two major advantages: it reduces the effort to use model checking, and it reduces missed errors as well as time-wasting false error reports resulting from inconsistencies between the abstract description and the actual implementation. In addition, changes in the implementation can be checked immediately without updating a high-level description.The paper's second contribution is demonstrating that CMC works well on real code by applying it to three implementations of the Ad-hoc On-demand Distance Vector (AODV) networking protocol [7]. We found 34 distinct errors (roughly one bug per 328 lines of code), including a bug in the AODV specification itself. Given our experience building systems, it appears that the approach will work well in other contexts, and especially well for other networking protocols.
Neural Machine Translation with Source-Side Latent Graph Parsing

This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural machine translation model, and thus the parser is optimized according to the translation objective. In experiments, we first show that our model compares favorably with state-of-the-art sequential and pipelined syntax-based NMT models. We also show that the performance of our model can be further improved by pretraining it with a small amount of treebank annotations. Our final ensemble model significantly outperforms the previous best models on the standard Englishto-Japanese translation dataset.
Process mining can be applied to software too!

Modern information systems produce tremendous amounts of event data. The area of process mining deals with extracting knowledge from this data. Real-life processes can be effectively discovered, analyzed and optimized with the help of mature process mining techniques. There is a variety of process mining case studies and experience reports from such business areas as healthcare, public, transportation and education. Although nowadays, these techniques are mostly used for discovering business processes.
 The goal of this industrial paper is to show that process mining can be applied to software too. Here we present and analyze our experiences on applying process mining in different productive software systems used in the touristic domain. Process models and user interface workflows underlie the functional specifications of the systems we experiment with. When the systems are utilized, user interaction is recorded in event logs. After applying process mining methods to these logs, process and user interface flow models are automatically derived. These resulting models provide insight regarding the real usage of the software, motivate the changes in the functional specifications, enable usability improvements and software redesign.
 Thus, with the help of our examples we demonstrate that process mining facilitates new forms of software analysis. The user interaction with almost every software system can be mined in order to improve the software and to monitor and measure its real usage.
Technical Demonstration on Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes

We propose a framework for automatic modeling, detection, and tracking of 3D objects with a Kinect. The detection part is mainly based on the recent template-based LINEMOD approach  for object detection. We show how to build the templates automatically from 3D models, and how to estimate the 6 degrees-of-freedom pose accurately and in real-time. The pose estimation and the color information allow us to check the detection hypotheses and improves the correct detection rate by 13% with respect to the original LINEMOD. These many improvements make our framework suitable for object manipulation in Robotics applications. Moreover we propose a new dataset made of 15 registered, 1100+ frame video sequences of 15 various objects for the evaluation of future competing methods.  Each detected object is augmented with its 3D model. We also show the corresponding coordinate systems.
Opinion mining in Twitter: How to make use of sarcasm to enhance sentiment analysis

Opinion mining and sentiment analysis refer to the identification and the aggregation of attitudes or opinions expressed by internet users towards a specific topic. However, due to the limitation in terms of characters (i.e. 140 characters per tweet) and the use of informal language, the state-of-the-art approaches of sentiment analysis present lower performances in Twitter than that when they are applied on longer texts. Moreover, presence of sarcasm makes the task even more challenging. Sarcasm is when a person conveys implicit information, usually the opposite of what is said, within the message he transmits. In this paper we propose a method that makes use of a minimal set of features, yet, efficiently classifies tweets regardless of their topic. We also study the importance of detecting sarcastic tweets automatically, and demonstrate how the accuracy of sentiment analysis can be enhanced knowing which tweets are sarcastic and which are not.
Efficient Forest Fire Detection System : A Spatial Data Mining and Image Processing Based Approach

The drastic ascent in the volume of spatial data owes its growth to the technical advancements in technologies that aid in spatial data acquisition, mass storage and network interconnection. Thus the necessity for automated detection of spatial knowledge from voluminous spatial data arises. Fire plays a vital role in a majority of the forest ecosystems. Forest fires are serious ecological threats that result in deterioration of economy and environment apart from jeopardizing human lives. Thus forest fires need to be detected as early as possible in order to inhibit from being spread. This paper intends to detect forest fires from the forest spatial data. The approach makes use of spatial data mining, image processing and artificial intelligence techniques for the detection of fires. A fuzzy rule base is formed for the detection of fires, from the spatial data with the presence of fires. The digital images from the spatial data are converted to YCbCr color space and then segmented by employing anisotropic diffusion to identify fire regions. Subsequently, a fuzzy set is created with the color space values of the fire regions. Further, fuzzy rules are derived on basis of fuzzy logic reasoning. Extensive experimental assessment on publicly available spatial data illustrated that the proposed approach efficiently detects forest fires.
Migrating to internet-based e-commerce: Factors affecting e-commerce adoption and migration at the firm level

Web technology has enabled e-commerce. However, in our review of the literature, we found little research on how firms can better position themselves when adopting e-commerce for revenue generation. Drawing upon technology diffusion theory, we developed a conceptual model for assessing e-commerce adoption and migration, incorporating six factors unique to e-commerce. A series of propositions were then developed. Survey data of 1036 firms in a broad range of industries were collected and used to test our model. Our analysis based on multi-nominal logistic regression demonstrated that technology integration, web functionalities, web spending, and partner usage were significant adoption predictors. The model showed that these variables could successfully differentiate non-adopters from adopters. Further, the migration model demonstrated that web functionalities, web spending, and integration of externally oriented inter-organizational systems tend to be the most influential drivers in firms’ migration toward e-commerce, while firm size, partner usage, electronic data interchange (EDI) usage, and perceived obstacles were found to negatively affect ecommerce migration. This suggests that large firms, as well as those that have been relying on outsourcing or EDI, tended to be slow to migrate to the internet platform.
Off-road Path Following using Region Classification and Geometric Projection Constraints

We describe a realtime system for finding and tracking unstructured paths in off-road conditions. The system was designed as part of the recent Darpa Grand Challenge and was tested over hundreds of miles of off-road driving. The unique feature of our approach is to combine geometric projection used for recovering Pitch and Yaw with Learning approaches for identifying familiar "drivable" regions in the scene. The region-based component segments the image to "path" and "non-path" regions based on texture analysis borne out of a learning-by-examples principle. The boundary-based component looks for the path bounding lines assuming a geometric model of a planar pathway bounded by parallel edges taken by a perspective camera. The combined effect of both sub-systems forms a robust system capable of finding the path even in situations where the vehicle is positioned out of the path - a situation which is not common for human drivers but is relevant for autonomous driving where the vehicle may find itself occasionally veering out of the path.
Machine Learning algorithms : a study on noise sensitivity

In this study, results of a variety of ML algorithms are tested against artificially polluted datasets with noise. Two noise models are tested, each of these studied on a range of noise levels from 0 to 50algorithm, a linear regression algorithm, a decision tree, a M5 algorithm, a decision table classifier, a voting interval scheme as well as a hyper pipes classifier. The study is based on an environmental field of application employing data from two air quality prediction problems, a toxicity classification problem and four artificially produced datasets. The results contain evaluation of classification criteria for every algorithm and noise level for the noise sensitivity study. The results suggest that the best algorithms per problem in terms of showing the lower RMS error are the decision table and the linear regression, for classification and regression problems respectively.
Design of LQR and PID controllers for the self balancing unicycle robot

This paper focus on the dynamic modeling and the LQR and PID controllers for the self balancing unicycle robot. The mechanism of the unicycle robot is designed. The pitching and rolling balance could be achieved by the driving of the motor on the wheel and the balance weight on the body of robot. The dynamic equations of the robot are presented based on the Routh equation. On this basis, the LQR and PID controllers of the unicycle robot are proposed. The experimentations of balance control are showed through the Simulink toolbox of Matlab. The simulation results show that the robot could achieve self balancing after a short period of time by the designed controllers. According to comparing the results, the errors of PID controller are relatively smaller than LQR. The response speed of LQR controller is faster than PID. At last a kind of LQR&PID controller is proposed. This controller has the advantages of both LQR and PID controllers.
DeepMath - Deep Sequence Models for Premise Selection

We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics. We propose a two stage approach for this task that yields good results for the premise selection task on the Mizar corpus while avoiding the handengineered features of existing state-of-the-art models. To our knowledge, this is the first time deep learning has been applied to theorem proving on a large scale.
Transformational leadership and moral reasoning.

Terms such as moral and ethical leadership are used widely in theory, yet little systematic research has related a sociomoral dimension to leadership in organizations. This study investigated whether managers' moral reasoning (n = 132) was associated with the transformational and transactional leadership behaviors they exhibited as perceived by their subordinates (n = 407). Managers completed the Defining Issues Test (J. R. Rest, 1990), whereas their subordinates completed the Multifactor Leadership Questionnaire (B. M. Bass & B. J. Avolio, 1995). Analysis of covariance indicated that managers scoring in the highest group of the moral-reasoning distribution exhibited more transformational leadership behaviors than leaders scoring in the lowest group. As expected, there was no relationship between moral-reasoning group and transactional leadership behaviors. Implications for leadership development are discussed.
Weighted nonnegative matrix factorization

Nonnegative matrix factorization (NMF) is a widely-used method for low-rank approximation (LRA) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity constraints are imposed on factor matrices in the decomposition. A large body of past work on NMF has focused on the case where the data matrix is complete. In practice, however, we often encounter with an incomplete data matrix where some entries are missing (e.g., a user-rating matrix). Weighted low-rank approximation (WLRA) has been studied to handle incomplete data matrix. However, there is only few work on weighted nonnegative matrix factorization (WNMF) that is WLRA with nonnegativity constraints. Existing WNMF methods are limited to a direct extension of NMF multiplicative updates, which suffer from slow convergence while the implementation is easy. In this paper we develop relatively fast and scalable algorithms for WNMF, borrowed from well-studied optimization techniques: (1) alternating nonnegative least squares; (2) generalized expectation maximization. Numerical experiments on MovieLens and Netflix prize datasets confirm the useful behavior of our methods, in a task of collaborative prediction.
High-Efficiency Digitally Controlled Charge Equalizer for Series-Connected Cells Based on Switching Converter and Super-Capacitor

The charge stored in series-connected lithium batteries needs to be well equalized between the elements of the series. We present here an innovative lithium-battery cell-to-cell active equalizer capable of moving charge between series-connected cells using a super-capacitor as an energy tank. The system temporarily stores the charge drawn from a cell in the super-capacitor, then the charge is moved into another cell without wasting energy as it happens in passive equalization. The architecture of the system which employs a digitally-controlled switching converter is compared with the state of the art, then fully investigated, together with the methodology used in its design. The performance of the system is described by presenting and discussing the experimental results of laboratory tests. The most innovative and attractive aspect of the proposed system is its very high efficiency, which is over 90%.
The role of multidimensional social capital in crowdfunding: A comparative study in China and US

How does an entrepreneur’s social network impact crowdfunding? Based on social capital theory, we developed a research model and conducted a comparative study using objective data collected from China and the U.S. We found that an entrepreneur’s social network ties, obligations to fund other entrepreneurs, and the shared meaning of the crowdfunding project between the entrepreneur and the sponsors had significant effects on crowdfunding performance in both China and the U.S. The predictive power of the three dimensions of social capital was stronger in China than it was in the U.S. Obligation also had a greater impact in China.  This study is supported by the Natural Science Foundation of China (71302186), the Chinese Ministry of Education Humanities and Social Sciences Young Scholar Fund (12YJCZH306), the China National Social Sciences Fund (11AZD077), and the Fundamental Research Funds for the Central Universities (JBK120505).  Corresponding author.  E-mail addresses: haichao_zheng@163.com (H. Zheng), dli@d.umn.edu (D. Li), kaitlynwu@swufe.edu.cn (J. Wu), xuyun@swufe.edu.cn (Y. Xu).