nils-herrmann/clean_scirepeval huggingface dataset:
Automatic On-line Signature Verification

Automatic on-line signature verification is an intriguing intellectual challenge with many practical applications. I review the context of this problem and then describe my own approach to it, which breaks with tradition by relying primarily on the detailed shape of a signature for its automatic verification, rather than relying primarily on the pen dynamics during the production of the signature. I propose a robust, reliable, and elastic localshape-based model for handwritten on-line curves; this model is generated by first parameterizing each on-line curve over its normalized arc-length and then representing along the length of the curve, in a moving coordinate frame, measures of the curve within a sliding window that are analogous to the position of the center of mass, the torque exerted by a force, and the moments of inertia of a mass distribution about its center of mass. Further, I suggest the weighted and biased harmonic mean as a graceful mechanism of combining errors from multiple models of which at least one model is applicable but not necessarily more than one model is applicable, recommending that each signature be represented by multiple models, these models, perhaps, local and global, shape based and dynamics based. Finally, I outline a signature-verification algorithm that I have implemented and tested successfully both on databases and in live experiments.
Deep Reinforcement Learning From Raw Pixels in Doom

Using current reinforcement learning methods, it has recently become possible to learn to play unknown 3D games from raw pixels. In this work, we study the challenges that arise in such complex environments, and summarize current methods to approach these. We choose a task within the Doom game, that has not been approached yet. The goal for the agent is to fight enemies in a 3D world consisting of five rooms. We train the DQN and LSTMA3C algorithms on this task. Results show that both algorithms learn sensible policies, but fail to achieve high scores given the amount of training. We provide insights into the learned behavior, which can serve as a valuable starting point for further research in the Doom domain.
Elo Ratings for Structural Credit Assignment in Multiagent Systems

In this paper we investigate the applications of Elo ratings (originally designed for 2-player chess) to a heterogeneous nonlinear multiagent system to determine an agent’s overall impact on its team’s performance. Measuring this impact has been attempted in many different ways, including reward shaping; the generation of heirarchies, holarchies, and teams; mechanism design; and the creation of subgoals. We show that in a multiagent system, an Elo rating will accurately reflect the an agent’s ability to contribute positively to a team’s success with no need for any other feedback than a repeated binary win/loss signal. The Elo rating not only measures “personal” success, but simultaneously success in assisting other agents to perform favorably.
A Deep Face Identification Network Enhanced by Facial Attributes Prediction

In this paper, we propose a new deep framework which predicts facial attributes and leverage it as a soft modality to improve face identification performance. Our model is an end to end framework which consists of a convolutional neural network (CNN) whose output is fanned out into two separate branches; the first branch predicts facial attributes while the second branch identifies face images. Contrary to the existing multi-task methods which only use a shared CNN feature space to train these two tasks jointly, we fuse the predicted attributes with the features from the face modality in order to improve the face identification performance. Experimental results show that our model brings benefits to both face identification as well as facial attribute prediction performance, especially in the case of identity facial attributes such as gender prediction. We tested our model on two standard datasets annotated by identities and face attributes. Experimental results indicate that the proposed model outperforms most of the current existing face identification and attribute prediction methods.
Generator to grid adaptive synchronization technique based on reference model

The paralleling of synchronous generators is one of the main and frequently implemented power system control tasks. The significant importance of automatic implementation of this task is determined by the need of fast and reliable restoration of an electrical power system (EPS) after emergencies. The value of this task has been dramatically increasing during the last years with the growth of the penetration of distributed generation and emerging of the task of automatic intentional islanding and further resynchronization of distributed generators to the grid. However the matching algorithms of the currently existing synchronizers are heavily affected by rapidly changing operating conditions of EPS which increases the matching time, especially during restoration process. This paper proposes the synchronization technique which is based on the model reference adaptive control. The objective of the proposed technique is to increase the robustness of the synchronization process when the operating conditions of EPS are rapidly changed. The application of this technique allows reducing the time of synchronization process and improving its accuracy. The time domain simulation results obtained with software and hybrid software-hardware simulators prove the advantages of the proposed technique.
Study on an Agricultural Environment Monitoring Server System using Wireless Sensor Networks

This paper proposes an agricultural environment monitoring server system for monitoring information concerning an outdoors agricultural production environment utilizing Wireless Sensor Network (WSN) technology. The proposed agricultural environment monitoring server system collects environmental and soil information on the outdoors through WSN-based environmental and soil sensors, collects image information through CCTVs, and collects location information using GPS modules. This collected information is converted into a database through the agricultural environment monitoring server consisting of a sensor manager, which manages information collected from the WSN sensors, an image information manager, which manages image information collected from CCTVs, and a GPS manager, which processes location information of the agricultural environment monitoring server system, and provides it to producers. In addition, a solar cell-based power supply is implemented for the server system so that it could be used in agricultural environments with insufficient power infrastructure. This agricultural environment monitoring server system could even monitor the environmental information on the outdoors remotely, and it could be expected that the use of such a system could contribute to increasing crop yields and improving quality in the agricultural field by supporting the decision making of crop producers through analysis of the collected information.
Computer assisted surgery and therapy (CAST) for breast masses based on mammography

Breast cancer represents the most frequently diagnosed cancer in women. Mammography is the most commonly method for early detection of masses related to breast cancer. Correlation of information from multiple-view mammograms improves the performance of diagnosis by radiologists or by computer assisted systems. Detecting the location of masses accurately is highly important for radiologist to classify masses, for the surgeon to help in ease the procedure of an accurate surgery and in radio therapy process for less and efficient dose. In this paper, CAST is developed to accurately locate abnormal masses by quarter and clock segments. CAST will replace the manual localization method. The nipple is used as a reference point in localization process. A new simple nipple detection method is also proposed. The proposed CAST is examined on a new local database which was tested and classified by experts. The methodology achieved a sensitivity of 96% and specificity 73.33%.
Implicit crowds: optimization integrator for robust crowd simulation

Large multi-agent systems such as crowds involve inter-agent interactions that are typically anticipatory in nature, depending strongly on both the positions and the velocities of agents. We show how the nonlinear, anticipatory forces seen in multi-agent systems can be made compatible with recent work on energy-based formulations in physics-based animation, and propose a simple and effective optimization-based integration scheme for implicit integration of such systems. We apply this approach to crowd simulation by using a state-of-the-art model derived from a recent analysis of human crowd data, and adapting it to our framework. Our approach provides, for the first time, guaranteed collision-free motion while simultaneously maintaining high-quality collective behavior in a way that is insensitive to simulation parameters such as time step size and crowd density. These benefits are demonstrated through simulation results on various challenging scenarios and validation against real-world crowd data.
Effect of Change in Position and Back Massage on Pain Perception during First Stage of Labor.

Labor is one of the most painful events in a women's life. Frequent change in positions and back massage may be effective in reducing pain during the first stage of labor.



The focus of this study was to identify the impact of either change in position or back massage on pain perception during first stage of labor.



A quasi-experimental study.



Teaching hospital, Kurdistan Region, Iraq, November 2014 to October 2015.



Eighty women were interviewed as a study sample when admitted to the labor and delivery area and divided into three groups: 20 women received frequent changes in position (group A), 20 women received back massage (Group B), and 40 women constituted the control group (group C).



A structured interview questionnaire to collect background data was completed by the researcher in personal interviews with the mothers. The intervention was performed at three points in each group, and pain perception was measured after each intervention using the Face Pain Scale.



The mean rank of the difference in pain scores among the study groups was as follows after the first, second, and third interventions, respectively: group A-52.33, 47.00, 49.2; group B-32.8, 30.28, 30.38; group C-38.44, 42.36, 41.21. There were significant differences between groups A, B, and C after the first, second, and third interventions (p1 = .011, p2 = .042, p3 = .024).



Back massage may be a more effective pain management approach than change in position during the first stage of labor.
A Designer's Guide to Subresolution Lithography: Enabling the Impossible to Get to the 14-nm Node [Tutorial]

Device scaling with manufacturing methods that overcome the inherent limits of optical lithography is a constant focus of the microelectronics industry. The authors of this tutorial article review the state-of-the-art and major challenges of subresolution lithography, and discuss modern double-patterning lithography solutions and supporting EDA tools to surpass them.
Sensing and Modeling Human Behavior Using Social Media and Mobile Data

In the past years we have witnessed the emergence of the new discipline of computational social science, which promotes a new data-driven and computation-based approach to social sciences. In this article we discuss how the availability of new technologies such as online social media and mobile smartphones has allowed researchers to passively collect human behavioral data at a scale and a level of granularity that were just unthinkable some years ago. We also discuss how these digital traces can then be used to prove (or disprove) existing theories and develop new models of human behavior.
A modular hierarchical approach to 3D electron microscopy image segmentation

The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy.
Computing on the Edge of Chaos: Structure and Randomness in Encrypted Computation

This survey, aimed mainly at mathematicians rather than practitioners, covers recent developments in homomorphic encryption (computing on encrypted data) and program obfuscation (generating encrypted but functional programs). Current schemes for encrypted computation all use essentially the same “noisy” approach: they encrypt via a noisy encoding of the message, they decrypt using an “approximate” ring homomorphism, and in between they employ techniques to carefully control the noise as computations are performed. This noisy approach uses a delicate balance between structure and randomness: structure that allows correct computation despite the randomness of the encryption, and randomness that maintains privacy against the adversary despite the structure. While the noisy approach “works”, we need new techniques and insights, both to improve efficiency and to better understand encrypted computation conceptually.
Stock market trading rule discovery using pattern recognition and technical analysis

This study examines the potential profit of bull flag technical trading rules using a template matching technique based on pattern recognition for the Nasdaq Composite Index (NASDAQ) and Taiwan Weighted Index (TWI). To minimize measurement error due to data snooping, this study performed a series of experiments to test the effectiveness of the proposed method. The empirical results indicated that all of the technical trading rules correctly predict the direction of changes in the NASDAQ and TWI. This finding may provide investors with important information on asset allocation. Moreover, better bull flag template price fit is associated with higher average return. The empirical results demonstrated that the average return of trading rules conditioned on bull flag significantly better than buying every day for the study period, especially for TWI.
Discrete quadratic curvature energies

Efficient computation of curvature-based energies is important for practical implementations of geometric modeling and physical simulation applications. Building on a simple geometric observation, we provide a version of a curvature-based energy expressed in terms of the Laplace operator acting on the embedding of the surface. The corresponding energy--being quadratic in positions--gives rise to a constant Hessian in the context of isometric deformations. The resulting isometric bending model is shown to significantly speed up common cloth solvers, and when applied to geometric modeling situations built onWillmore flow to provide runtimes which are close to interactive rates.
The role of motivators in improving knowledge-sharing among academics

This research addresses a primary issue that involves motivating academics to share knowledge. Adapting the theory of reasoned action, this study examines the role of motivation that consists of intrinsic motivators (commitment; enjoyment in helping others) and extrinsic motivators (reputation; organizational rewards) to determine and explain the behaviour of Malaysian academics in sharing knowledge.  A self-administered questionnaire was distributed using a non-probability sampling technique. A total of 373 completed responses were collected with a total response rate of 38.2%. Analysis. The partial least squares analysis was used to analyse the data.  The results indicated that all five of the hypotheses were supported. Analysis of data from the five higher learning institutions in Malaysia found that commitment and enjoyment in helping others (i.e., intrinsic motivators) and reputation and organizational rewards (i.e., extrinsic motivators) have a positive and significant relationship with attitude towards knowledge-sharing. In addition, the findings revealed that intrinsic motivators are more influential than extrinsic motivators. This suggests that academics are influenced more by intrinsic motivators than by extrinsic motivators.  The findings provided an indication of the determinants in enhancing knowledgesharing intention among academics in higher education institutions through extrinsic and intrinsic motivators.
Knowledge Propagation in Contextualized Knowledge Repositories: An Experimental Evaluation - (Extended Paper)

As the interest in the representation of context dependent knowledge in the Semantic Web has been recognized, a number of logic based solutions have been proposed in this regard. In our recent works, in response to this need, we presented the description logic-based Contextualized Knowledge Repository (CKR) framework. CKR is not only a theoretical framework, but it has been effectively implemented over state-of-the-art tools for the management of Semantic Web data: inference inside and across contexts has been realized in the form of forward SPARQL-based rules over different RDF named graphs. In this paper we present the first evaluation results for such CKR implementation. In particular, in first experiment we study its scalability with respect to different reasoning regimes. In a second experiment we analyze the effects of knowledge propagation on the computation of inferences.
An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives

The Internet of Things (IoT) is emerging as a significant development in information technology, with the potential to increase convenience and efficiency in daily life. While the number of IoT service users has increased dramatically, little is understood about what motivates the continued use of such services. The primary objective of this study is to develop and refine a conceptual framework from the perspective of network externalities and privacy to provide a theoretical understanding of the motivations that drive continued use of IoT services. The proposed model was empirically evaluated using survey data collected from 508 users concerning their perceptions of IoT services. The results indicate network externalities play a significant role in influencing consumers' perception of usage benefits and thus adoption, whereas privacy concerns have a relatively weak effect on adoption. Implications for IS researchers and practice
Modeling and Analysis for Spectrum Handoffs in Cognitive Radio Networks

In this paper, we present an analytical framework to evaluate the latency performance of connection-based spectrum handoffs in cognitive radio (CR) networks. During the transmission period of a secondary connection, multiple interruptions from the primary users result in multiple spectrum handoffs and the need of predetermining a set of target channels for spectrum handoffs. To quantify the effects of channel obsolete issue on the target channel predetermination, we should consider the three key design features: 1) general service time distribution of the primary and secondary connections; 2) different operating channels in multiple handoffs; and 3) queuing delay due to channel contention from multiple secondary connections. To this end, we propose the preemptive resume priority (PRP) M/G/1 queuing network model to characterize the spectrum usage behaviors with all the three design features. This model aims to analyze the extended data delivery time of the secondary connections with proactively designed target channel sequences under various traffic arrival rates and service time distributions. These analytical results are applied to evaluate the latency performance of the connection-based spectrum handoff based on the target channel sequences mentioned in the IEEE 802.22 wireless regional area networks standard. Then, to reduce the extended data delivery time, a traffic-adaptive spectrum handoff is proposed, which changes the target channel sequence of spectrum handoffs based on traffic conditions. Compared to the existing target channel selection methods, this traffic-adaptive target channel selection approach can reduce the extended data transmission time by 35 percent, especially for the heavy traffic loads of the primary users.
Fox-Fordyce disease following axillary laser hair removal.

Fox-Fordyce disease (FFD) is a relatively rare entity with a typical clinical presentation. Numerous studies have described unifying histopathological features of FFD, which together suggest a defect in the follicular infundibulum resulting in follicular dilation with keratin plugging, subsequent apocrine duct obstruction, and apocrine gland dilation, with eventual extravasation of the apocrine secretions as the primary histopathogenic events in the evolution of the disease.



We describe a case of FFD that developed in a 41-year-old woman 3 months after completing a series of axillary laser hair removal treatments, and we detail the clinical and histopathological changes typical for FFD.



Because defective infundibular maturation has been suggested to play a central role in the evolution of FFD, the close temporal relationship of laser hair therapy with the development of FFD suggests a causal role, which we continue to explore.
Firearm Detection Using Wrist Worn Tri-Axis Accelerometer Signals

Gunshot detection traditionally has been a task performed with acoustic signal processing. While this type of detection can give cities, civil services and training institutes a method to identify specific locations of gunshots, the nature of acoustic detection may not provide the fine-grained detection accuracy and sufficient metrics for performance assessment. If however you examine a different signature of a gunshot, the recoil, detection of the same event with accelerometers can provide you with persona and firearm model level detection abilities. The functionality of accelerometer sensors in wrist worn devices have increased significantly in recent time. From fitness trackers to smart watches, accelerometers have been put to use in various activity recognition and detection applications. In this paper, we design an approach that is able to account for the variations in firearm generated recoil, as recorded by a wrist worn accelerometer, and helps categorize the impulse forces. Our experiments show that not only can wrist worn accelerometers detect the differences in handgun rifle and shotgun gunshots, but the individual models of firearms can be distinguished from each other. The application of this framework could be extended in the future to include real time detection embedded in smart devices to assist in firearms training and also help in crime detection and prosecution.