nils-herrmann/clean_scirepeval huggingface dataset:
Probability of Random Correspondence for Fingerprints

Individuality of fingerprints can be quantified by computing the probabilistic metrics for measuring the degree of fingerprint individuality. In this paper, we present a novel individuality evaluation approach to estimate the probability of random correspondence (PRC). Three generative models are developed respectively to represent the distribution of fingerprint features: ridge flow, minutiae and minutiae together with ridge points. A mathematical model that computes the PRCs are derived based on the generative models. Three metrics are discussed in this paper: (i) PRC of two samples, (ii) PRC among a random set of n samples (nPRC) and (iii) PRC between a specific sample among n others (specific nPRC). Experimental results show that the theoretical estimates of fingerprint individuality using our model consistently follow the empirical values based on the NIST4 database.
Overview of the Photovoltaic System Topologies

— Due to increased interest for solar energy harvesting systems in recent years the number of developed system types is large. In order to choose the optimal one for future system development the analysis of common architectures being used for photovoltaic systems has been done. The paper contains the small description of different converter architectures and analysis of prototyped or simulated systems proposed in the references. Systems of different distribution level are observed from more integrated to more distributed topologies. Distribution level comes hand-to-hand to the overall system efficiency. Less distributed systems in case of low irradiation disturbance show better performance due to higher efficiency that could be achieved relatively easy. More distributed systems have better performance in cases of the frequent partial shading for example in building-integrated photovoltaic systems when there are relatively high objects (like trees, chimneys and other buildings) close to the PV-panel mounting place that can partially shade the PV. But this type of systems have large number of small-power converters that usually has smaller efficiency as the design of effective and small-power converters is hard technical task. The reason of better performance is that distributed PV-systems have larger number of maximum power point tracking converters and therefore increase the harvest of energy by keeping the photovoltaic power generation process optimal. This paper is made for choice of most suitable system topology for future PV-system implementation.
Depth inpainting by tensor voting.

Depth maps captured by range scanning devices or by using optical cameras often suffer from missing regions due to occlusions, reflectivity, limited scanning area, sensor imperfections, etc. In this paper, we propose a fast and reliable algorithm for depth map inpainting using the tensor voting (TV) framework. For less complex missing regions, local edge and depth information is utilized for synthesizing missing values. The depth variations are modeled by local planes using 3D TV, and missing values are estimated using plane equations. For large and complex missing regions, we collect and evaluate depth estimates from self-similar (training) datasets. We align the depth maps of the training set with the target (defective) depth map and evaluate the goodness of depth estimates among candidate values using 3D TV. We demonstrate the effectiveness of the proposed approaches on real as well as synthetic data.
Interactive simulation of needle insertion models

A novel interactive virtual needle insertion simulation is presented. The simulation models are based on measured planar tissue deformations and needle insertion forces. Since the force-displacement relationship is only of interest along the needle shaft, a condensation technique is shown to reduce the computational complexity of linear simulation models significantly. As the needle penetrates or is withdrawn from the tissue model, the boundary conditions that determine the tissue and needle motion change. Boundary condition and local material coordinate changes are facilitated by fast low-rank matrix updates. A large-strain elastic needle model is coupled to the tissue models to account for needle deflection and bending during simulated insertion. A haptic environment, based on these novel interactive simulation techniques, allows users to manipulate a three-degree-of-freedom virtual needle as it penetrates virtual tissue models, while experiencing steering torques and lateral needle forces through a planar haptic interface.
CMOS T/R Switch Design: Towards Ultra-Wideband and Higher Frequency

This paper presents the comprehensive considerations of CMOS transmit/receive (T/R) switch design towards ultra-wideband and over 15-GHz frequencies. Techniques for minimizing parasitics and increasing linearity are discussed. A customized transistor layout is proposed for T/R switch design and its effects on insertion loss and isolation are studied. The analysis shows that a series-only architecture using the customized transistor layout achieves better insertion loss and reasonable isolation. A double-well body-floating technique is proposed and its effects are discussed. A differential switch architecture without shunt arms is designed and verified by experimental results. Fabricated in 0.13-mum triple-well CMOS, the T/R switch exhibits less than 2 dB insertion loss and higher than 21 dB isolation up to 20 GHz. With resistive body floating and differential architecture, the high linearity is of ultra-wideband characteristic, more than 30-dBm power 1-dB compression point (P1dB) is obtained up to 20 GHz in only 0.03 mm2 active die area.
Long-term stability of anterior open-bite treatment by intrusion of maxillary posterior teeth.

Anterior open bite results from the combined influences of skeletal, dental, functional, and habitual factors. The long-term stability of anterior open bite corrected with absolute anchorage has not been thoroughly investigated. The purpose of this study was to examine the long-term stability of anterior open-bite correction with intrusion of the maxillary posterior teeth.



Nine adults with anterior open bite were treated by intrusion of the maxillary posterior teeth. Lateral cephalographs were taken immediately before and after treatment, 1 year posttreatment, and 3 years posttreatment to evaluate the postintrusion stability of the maxillary posterior teeth.



On average, the maxillary first molars were intruded by 2.39 mm (P<0.01) during treatment and erupted by 0.45 mm (P<0.05) at the 3-year follow-up, for a relapse rate of 22.88%. Eighty percent of the total relapse of the intruded maxillary first molars occurred during the first year of retention. Incisal overbite increased by a mean of 5.56 mm (P<0.001) during treatment and decreased by a mean of 1.20 mm (P<0.05) by the end of the 3-year follow-up period, for a relapse rate of 17.00%. Incisal overbite significantly relapsed during the first year of retention (P<0.05) but did not exhibit significant recurrence between the 1-year and 3-year follow-ups.



Most relapse occurred during the first year of retention. Thus, it is reasonable to conclude that the application of an appropriate retention method during this period clearly enhances the long-term stability of the treatment.
RELATIONSHIP OF PERSONALITY TRAITS AND COUNTERPRODUCTIVE WORK BEHAVIORS : THE MEDIATING EFFECTS OF JOB SATISFACTION

This study used path analysis to test a model that posits that relevant personality traits will have both direct relationships with counterproductive work behaviors (CPBs) and indirect relationships to CPBs through the mediating effects of job satisfaction. Based on a sample (n = 141) of customer service employees, results generally supported the hypothesized model for both bossand self-rated CPBs. Agreeableness had a direct relationship with interpersonal counterproductive work behaviors (CPB-I); Conscientiousness had a direct relationship with organizational counterproductive work behaviors (CPB-O); and, job satisfaction had a direct relationship to both CPB-I and CPB-O. In addition, job satisfaction partially mediated the relationship between Agreeableness and both CPB-O and CPB-I. Overall, results show that personality traits differentially predict CPBs and that employees’ attitudes about their jobs explain, in part, these personality–behavior associations.
Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks

The next-generation wireless networks are evolving into very complex systems because of the very diversified service requirements, heterogeneity in applications, devices, and networks. The network operators need to make the best use of the available resources, for example, power, spectrum, as well as infrastructures. Traditional networking approaches, i.e., reactive, centrally-managed, one-size-fits-all approaches, and conventional data analysis tools that have limited capability (space and time) are not competent anymore and cannot satisfy and serve that future complex networks regarding operation and optimization cost effectively. A novel paradigm of proactive, self-aware, self-adaptive, and predictive networking is much needed. The network operators have access to large amounts of data, especially from the network and the subscribers. Systematic exploitation of the big data dramatically helps in making the system smart, intelligent, and facilitates efficient as well as cost-effective operation and optimization. We envision data-driven next-generation wireless networks, where the network operators employ advanced data analytics, machine learning (ML), and artificial intelligence. We discuss the data sources and strong drivers for the adoption of the data analytics, and the role of ML, artificial intelligence in making the system intelligent regarding being self-aware, self-adaptive, proactive and prescriptive. A set of network design and optimization schemes are presented concerning data analytics. This paper concludes with a discussion of challenges and the benefits of adopting big data analytics, ML, and artificial intelligence in the next-generation communication systems.
A Deeper Look at FFT and Winograd Convolutions

Since convolutional layers are computationally expensive and dominate the total execution time of modern deep ConvNets [13, 16, 18, 19], many efforts have been made to improve the performance of the convolutional primitives for CPUs [1, 7, 20, 25, 27], GPUs [4, 8, 15, 21] or both [26]. Initially, several approaches using FFT–based convolutions were proposed [15, 21, 25, 26]. Recent work by Lavin et al. on Winograd–based convolutions  demonstrated a great speedup, which shifted the focus from FFT–based to Winograd–based implementations, as it became widely accepted that the Winograd–based approach provides greater reduction in the number of operations required by the algorithm, especially for small kernels (e.g. 3 × 3). A well optimized manycore CPU implementation [3, 12] of the Winograd approach can improve the performances by more than 3X. The main reduction in operations in the Winograd method, compared to FFT, comes from the fact that it works with real numbers. However, due to its numerical instability, the Winograd method can only use small tile (transform) sizes [7, 14, 22], which result in a larger amount of required data movement to and from memory. In contrast, the FFT–based method does not suffer from such instability, thus larger tile sizes can be used, which can partially reduce the number of required operations and greatly reduce the amount of data movements; these savings can, in certain cases, offset the increase in the number of operations due to complex arithmetic. These observations raise the question, under what conditions the Winograd-based approach performs better than the FFT– based approach and vice versa, and how to compare the two approaches.
A closer look at recognition-based graphical passwords on mobile devices

Graphical password systems based on the recognition of photographs are candidates to alleviate current over-reliance on alphanumeric passwords and PINs. However, despite being based on a simple concept -- and user evaluations consistently reporting impressive memory retention -- only one commercial example exists and overall take-up is low. Barriers to uptake include a perceived vulnerability to observation attacks; issues regarding deployability; and the impact of innocuous design decisions on security not being formalized. Our contribution is to dissect each of these issues in the context of mobile devices -- a particularly suitable application domain due to their increasing significance, and high potential to attract unauthorized access. This produces: 1) A novel yet simple solution to the intersection attack that permits greater variability in login challenges; 2) Detailed analysis of the shoulder surfing threat that considers both simulated and human testing; 3) A first look at image processing techniques to contribute towards automated photograph filtering. We operationalize our observations and gather data in a field context where decentralized mechanisms of varying entropy were installed on the personal devices of participants. Across two working weeks success rates collected from users of a high entropy version were similar to those of a low entropy version at 77%, and login durations decreased significantly across the study.
Active Contours without Edges for Vector-Valued Images

In this paper, we propose an active contour algorithm for object detection in vectorvalued images (such as RGB or multispectral). The model is an extension of the scalar Chan–Vese algorithm to the vector-valued case . The model minimizes a Mumford–Shah functional over the length of the contour, plus the sum of the fitting error over each component of the vector-valued image. Like the Chan–Vese model, our vector-valued model can detect edges both with or without gradient. We show examples where our model detects vector-valued objects which are undetectable in any scalar representation. For instance, objects with different missing parts in different channels are completely detected (such as occlusion). Also, in color images, objects which are invisible in each channel or in intensity can be detected by our algorithm. Finally, the model is robust with respect to noise, requiring no a priori denoising step.
Quantum Structure in Cognition: Fundamentals and Applications

Experiments in cognitive science and decision theory show that the ways in which people combine concepts and make decisions cannot be described by classical logic and probability theory. This has serious implications for applied disciplines such as information retrieval, artificial intelligence and robotics. Inspired by a mathematical formalism that generalizes quantum mechanics the authors have constructed a contextual framework for both concept representation and decision making, together with quantum models that are in strong alignment with experimental data. The results can be interpreted by assuming the existence in human thought of a double-layered structure, a classical logical thought and a quantum conceptual thought, the latter being responsible of the above paradoxes and nonclassical effects. The presence of a quantum structure in cognition is relevant, for it shows that quantum mechanics provides not only a useful modeling tool for experimental data but also supplies a structural model for human and artificial thought processes. This approach has strong connections with theories formalizing meaning, such as semantic analysis, and has also a deep impact on computer science, information retrieval and artificial intelligence. More specifically, the links with information retrieval are discussed in this paper.
Jeddah Historical Building Information Modelling "JHBIM" – Object Library

The theory of using Building Information Modelling " BIM " has been used in several Heritage places in the worldwide, in the case of conserving, documenting, managing, and creating full engineering drawings and information. However, one of the most serious issues that facing many experts in order to use the Historical Building Information Modelling " HBIM " , is creating the complicated architectural elements of these Historical buildings. In fact, many of these outstanding architectural elements have been designed and created in the site to fit the exact location. Similarly, this issue has been faced the experts in Old Jeddah in order to use the BIM method for Old Jeddah historical Building. Moreover, The Saudi Arabian City has a long history as it contains large number of historic houses and buildings that were built since the 16 th century. Furthermore, the BIM model of the historical building in Old Jeddah always take a lot of time, due to the unique of Hijazi architectural elements and no such elements library, which have been took a lot of time to be modelled. This paper will focus on building the Hijazi architectural elements library based on laser scanner and image survey data. This solution will reduce the time to complete the HBIM model and offering in depth and rich digital architectural elements library to be used in any heritage projects in Al-Balad district, Jeddah City.
A Surgical Palpation Probe With 6-Axis Force/Torque Sensing Capability for Minimally Invasive Surgery

A novel surgical palpation probe installing a miniature 6-axis force/torque (F/T) sensor is presented for robot-assisted minimally invasive surgery. The 6-axis F/T sensor is developed by using the capacitive transduction principle and a novel capacitance sensing method. The sensor consists of only three parts, namely a sensing printed circuit board, a deformable part, and a base part. The simple configuration leads to simpler manufacturing and assembly processes in conjunction with high durability and low weight. In this study, a surgical instrument installed with a surgical palpation probe is implemented. The 6-axis F/T sensing capability of the probe has been experimentally validated by comparing it with a reference 6-axis F/T sensor. Finally, a vivo tissue palpation task is performed in a simulated surgical environment with an animal organ and a relatively hard simulated cancer buried under the surface of the organ.
Vectorized total variation defined by weighted L infinity norm for utilizing inter channel dependency

Vectorized total variation (VTV) is very successful convex regularizer to solve various color image recovery problems. Despite the fact that color channels of natural color images are closely related, existing variants of VTV can not utilize this prior efficiently. We proposed L∞-VTV as a convex regularizer can penalize the violation of such inter-channel dependency by employing weighted L∞ (L-infty) norm. We also introduce an effective algorithm for an image denoising problem using L∞-VTV. Experimental results shows that our proposed method can outperform the conventional methods.
Distributed Graph Routing for WirelessHART Networks

Communication reliability in a Wireless Sensor and Actuator Network (WSAN) has a high impact on stability of industrial process monitoring and control. To make reliable and real-time communication in highly unreliable environments, industrial WSANs such as those based on WirelessHART adopt graph routing. In graph routing, each packet is scheduled on multiple time slots using multiple channels, on multiple links along multiple paths on a routing graph between a source and a destination. While high redundancy is crucial to reliable communication, determining and maintaining graph routing is challenging in terms of execution time and energy consumption for resource constrained WSAN. Existing graph routing algorithms use centralized approach, do not scale well in terms of these metrics, and are less suitable under network dynamics. To address these limitations, we propose the first distributed graph routing protocol for WirelessHART networks. Our distributed protocol is based on the Bellman-Ford Algorithm, and generates all routing graphs together using a single algorithm. We prove that our proposed graph routing can include a path between a source and a destination with cost (in terms of hop-count) at most 3 times the optimal cost. We implemented our proposed routing algorithm on TinyOS and evaluated through experiments on TelosB motes and simulations using TOSSIM. The results show that it is scalable and consumes at least 40% less energy and needs at least 65% less time at the cost of 1kB of extra memory compared to the state-of-the-art centralized approach for generating routing graphs.
A New Method for Optimization of Dynamic Ride Sharing System

Dynamic ridesharing is a profitable way to reduce traffic and carbon emissions by providing an opportunity for a flexible and affordable service that utilizes vehicle seating space. Matching of ride seeker requests with the rides, distributed over the roads is a tedious work. While fulfilling the request of all passengers, the total travel distance of the trip may get increased. Therefore, this article proposes optimal dynamic ridesharing system which matches rides and requests in real time by satisfying multiple participant constraints (e.g. time bounds, availability of empty seat, maximum allowed deviation distance and minimized route ride) to minimize the total travel distance. To efficiently match ride givers and riders we are proposing a novel dynamic ride matching algorithm MRB (Minimal route bisearching algorithm) considering all above mentioned constraints. We demonstrate working of our algorithm by developing a prototype and evaluated our system on GPS (Global positioning system) trajectories of Lahore city dataset. Evaluated results are compared with existing algorithms which shows that our system significantly reduces the travel distance and computation cost in comparison with other recent ride searching methods to maximize efficiency.
VOLAP: A Scalable Distributed Real-Time OLAP System for High-Velocity Data

This paper presents VelocityOLAP (VOLAP), a distributed real-time OLAP system for high-velocity data. VOLAP makes use of dimension hierarchies, is highly scalable, exploits both multi-core and multi-processor parallelism, and can guarantee serializable execution of insert and query operations. In contrast to other high performance OLAP systems such as SAP HANA or IBM Netezza that rely on vertical scaling or special purpose hardware, VOLAP supports cost-efficient horizontal scaling on commodity hardware or modest cloud instances. Experiments on 20 Amazon EC2 nodes with TPC-DS data show that VOLAP is capable of bulk ingesting data at over 600 thousand items per second, and processing streams of interspersed insertions and aggregate queries at a rate of approximately 50 thousand insertions and 20 thousand aggregate queries per second with a database of 1 billion items. VOLAP is designed to support applications that perform large aggregate queries, and provides similar high performance for aggregations ranging from a few items to nearly the entire database.