nils-herrmann/clean_scirepeval huggingface dataset:
You can't not believe everything you read.

Can people comprehend assertions without believing them? Descartes (1644/1984) suggested that people can and should, whereas Spinoza (1677/1982) suggested that people should but cannot. Three experiments support the hypothesis that comprehension includes an initial belief in the information comprehended. Ss were exposed to false information about a criminal defendant (Experiments 1 and 2) or a college student (Experiment 3). Some Ss were exposed to this information while under load (Experiments 1 and 2) or time pressure (Experiment 3). Ss made judgments about the target (sentencing decisions or liking judgments). Both load and time pressure caused Ss to believe the false information and to use it in making consequential decisions about the target. In Spinozan terms, both manipulations prevented Ss from "unbelieving" the false information they automatically believed during comprehension.
A new palm vein matching method based on ICP algorithm

Palm vein as a new and promising biometric feature has drawn lots of research and industry attention. Palm vein owns many outstanding characteristics, such as liveness detection and hard to forgery. Matching is a key step in palm vein verification. However, due to the problem of image rotation, shift and noise, current matching methods are difficult to reach high accuracy. This paper proposed an efficient refinement method for palm vein matching by adopting the iterative closest point (ICP) algorithm, which can accurately align the rotation and shift variations introduced in data acquisition. The experiment results on a large self-collected database show that the proposed method is effective and can greatly improve the accuracy of palm vein verification.
Active current balancing for parallel-connected silicon carbide MOSFETs

In high power applications of silicon carbide (SiC) MOSFETs where parallelism is employed, current unbalance can occur and affect the performance and reliability of the power devices. In this paper, factors which cause current unbalance in these devices are analyzed. Among them, the threshold voltage mismatch is identified as a major factor for dynamic current unbalance. The threshold distribution of SiC MOSFETs is investigated, and its effect on current balance is studied in experiments. Based on these analyses, an active current balancing scheme is proposed. It is able to sense the unbalanced current and eliminate it by actively controlling the gate drive signal to each device. The features of fine time resolution and low complexity make this scheme attractive to a wide variety of wide-band-gap device applications. Experimental and simulation results verify the feasibility and effectiveness of the proposed scheme.
Clone-resistant vehicular RKE by deploying SUC

Many automotive Remote Keyless Entry (RKE) systems have been successfully attacked in the last few years. The security of RKE systems is still a sensitive and crucial issue for vehicular industry. The problem needs to be essentially addressed by the fundamental fabrication weakness namely: "the manufacture can clone his own fabricated keys" and create equal units. This should not be possible. The fact that: "the only safe secret is the one which nobody knows" leads to proposing the fabrication of physically "Secret Unknown Cipher" (SUC) to serve as a clone-resistant security anchor (module) for the RKE system. Each fabricated key, in its ultimate case, is then equivalent to an individual unclonable DNA-like biological entity. We preset a protocol exhibiting mutual authentication properties to manage a complete RKE system. It is based on a digital hardware token incorporating an SUC module designed to fit to the vehicular industrial mass-production environment at reasonable costs.
Real time DDoS detection using fuzzy estimators

We propose a method for DDoS detection by constructing a fuzzy estimator on the mean packet inter arrival times. We divided the problem into two challenges, the first being the actual detection of the DDoS event taking place and the second being the identification of the offending IP addresses. We have imposed strict real time constraints for the first challenge and more relaxed constraints for the identification of addresses. Through empirical evaluation we confirmed that the detection can be completed within improved real time limits and that by using fuzzy estimators instead of crisp statistical descriptors we can avoid the shortcomings posed by assumptions on the model distribution of the traffic. In addition we managed to obtain results under a 3 sec detection window. a 2012 Elsevier Ltd. All rights reserved.
Integrated pathology and radiology learning for a musculoskeletal system module: an example of interdisciplinary integrated form

Many curricula integrate radiology with anatomy courses but none of these curricula adopt integration of pathology with radiology as interdisciplinary form at the undergraduate level. The aim of the current study was to identify the outcome of interdisciplinary integrated course of pathology and radiology in musculoskeletal system module (MSK).



A comparative interventional study was conducted on 60 students representing a whole class of the third year of level V. MSK and gastrointestinal module (GIT) were selected as study and control module, respectively, as being adopted for the same level/allocated hours, enriched with many subject areas for both fields, and availability of learning resources for both. A planned interdisciplinary integrated course for MSK pathology and radiology was implemented in the pathology lab. The subject area was selected and taught for both fields in consecutive ways by pathology and radiology experts. After teaching, gross/histopathologic specimens and radiology imaging/reports were distributed over benches and the students investigated the same. Conversely, in GIT control module, both fields were delivered separately, and no interdisciplinary form of integration occurred. Students' scores for both fields were filtered from the objective structured practical exam, quiz, and final exam. Students' marks and satisfaction were subjected to multiple comparisons using independent student's t-test. SPSS version 17 was used.



Significances were obtained between total marks of students for both modules and between radiology courses for both with P=0.0152 and 0.0199, respectively. Number of students who achieved >80% in MSK was 20 and 26 compared to 15 and 17 in GIT for pathology and radiology, respectively. Student satisfaction was high for interdisciplinary integration in MSK with significant difference obtained between MSK and GIT.



The integration of both fields augments student performance for both. This experience must encourage curriculum committee to globalize it over all other modules.
MedRec: Using Blockchain for Medical Data Access and Permission Management

Years of heavy regulation and bureaucratic inefficiency have slowed innovation for electronic medical records (EMRs). We now face a critical need for such innovation, as personalization and data science prompt patients to engage in the details of their healthcare and restore agency over their medical data. In this paper, we propose MedRec: a novel, decentralized record management system to handle EMRs, using blockchain technology. Our system gives patients a comprehensive, immutable log and easy access to their medical information across providers and treatment sites. Leveraging unique blockchain properties, MedRec manages authentication, confidentiality, accountability and data sharing- crucial considerations when handling sensitive information. A modular design integrates with providers' existing, local data storage solutions, facilitating interoperability and making our system convenient and adaptable. We incentivize medical stakeholders (researchers, public health authorities, etc.) to participate in the network as blockchain “miners”. This provides them with access to aggregate, anonymized data as mining rewards, in return for sustaining and securing the network via Proof of Work. MedRec thus enables the emergence of data economics, supplying big data to empower researchers while engaging patients and providers in the choice to release metadata. The purpose of this short paper is to expose, prior to field tests, a working prototype through which we analyze and discuss our approach.
Multiview Differential Geometry of Curves

The field of multiple view geometry has seen tremendous progress in reconstruction and calibration due to methods for extracting reliable point features and key developments in projective geometry. Point features, however, are not available in certain applications and result in unstructured point cloud reconstructions. General image curves provide a complementary feature when keypoints are scarce, and result in 3D curve geometry, but face challenges not addressed by the usual projective geometry of points and algebraic curves. We address these challenges by laying the theoretical foundations of a framework based on the differential geometry of general curves, including stationary curves, occluding contours, and non-rigid curves, aiming at stereo correspondence, camera estimation (including calibration, pose, and multiview epipolar geometry), and 3D reconstruction given measured image curves. By gathering previous results into a cohesive theory, novel results were made possible, yielding three contributions. First, we derive the differential geometry of an image curve (tangent, curvature, curvature derivative) from that of the underlying space curve (tangent, curvature, curvature derivative, torsion). Second, we derive the differential geometry of a space curve from that of two corresponding image curves. Third, the differential motion of an image curve is derived from camera motion and the differential geometry and motion of the space curve. The availability of such a theory enables novel curve-based multiview reconstruction and camera estimation systems to augment existing point-based approaches. This theory has been used to reconstruct a “3D curve sketch”, to determine camera pose from local curve geometry, and tracking; other developments are underway.
Man-At-The-End attacks: Analysis, taxonomy, human aspects, motivation and future directions

Man-At-The-End (MATE) attacks and fortifications are difficult to analyze, model, and evaluate predominantly for three reasons: firstly, the attacker is human and, therefore, utilizes motivation, creativity, and ingenuity. Secondly, the attacker has limitless and authorized access to the target. Thirdly, all major protections stand up to a determined attacker till a certain period of time. Digital assets range from business to personal use, from consumer devices to home networks, the public Internet, the cloud, and the Internet of Things – where traditional computer and network security are inadequate to address MATE attacks. MATE is fundamentally a hard problem. Much of the extant focus to deal with MATE attacks is purely technical; though security is more than just a technical issue. The main objective of the paper is to mitigate the consequences of MATE attacks through the human element of security and highlight the need for this element to form a part of a holistic security strategy alongside the necessary techniques and technologies. This paper contributes by taking software protection (SP) research to a new realm of challenges. Moreover, the paper elaborates the concept of MATE attacks, the different forms, and the analysis of MATE versus insider threats to present a thematic taxonomy of a MATE attack. The ensuing paper also highlights the fundamental concept of digital assets, and the core protection mechanisms and their qualitative comparison against MATE attacks. Finally, we present state-of-the-art trends and cutting-edge future research directions by taking into account only the human aspects for young researchers and professionals. & 2014 Elsevier Ltd. All rights reserved.
A Modular Architecture for Hybrid Planning with Theories

Planning technology has made huge strides, alongside other combinatorial optimisation solving technologies, over the past decade. Automated planning systems now exist for temporal and metric problems, including management of continuous time and concurrency, continuous numeric resources and action costs [3, 1, 2, 12, 7, 8, 11, 9]. There is an increasing interest in combining planners with specialised solvers, such as optimisation alogorithms, to achieve a hybrid form of planning. In this context, the relationship between planning and model-checking, planning and constraint-solving and planning and control are all being clarified. Synergies between different optimisation modelling and solving paradigms can be exploited to achieve new capabilities and improved performance of solvers. An example of this is recent work exploiting the developments in SAT solving, SAT Modulo Theories, in which atoms can be built from predicates, functions and constants whose interpretations are provided through external theory modules [10, 5]. In planning, extension to support external modules allows a much richer expression of preconditions and state variables. A motivation for exploring this idea is that the increased expressiveness can allow planners to work with models of application domains using specialised solvers, necessary for reasoning within those applications, alongside the generic solving cores developed in the planning community. Since this is a common requirement of planning applications, it is important to provide clean and well-understood methods for linking planners to external libraries, choosing heuristics and exchanging constraints. In this talk we present the Planning Modulo Theories paradigm, first proposed in 2012 , describing how the paradigm has been extended to incorporate the latest advances in temporal planning. We discuss how the use of constraint reasoning can provide an additional source of powerful solving capabilities within this framework. In general, constraint solvers prune choices from the search space by inference, while most modern planners focus on heuristic guidance of the search towards good choices. Complex interactions in resourceconstrained models can be obscure, making heuristic evaluation of states much more difficult, while at the same time offering more opportunity for leverage
Cytokine removal with high cut-off membrane: review of literature.

During the last decade, blood purification therapies have been proposed as an effective therapy to control the cytokines dysregulation in systemic inflammatory syndromes. Among them, the treatment with high cut-off membranes is characterized by larger pore size and more effective clearance for middle molecular weight molecules (cytokines). In this paper, we performed a thoughtful review of the literature on HCO being used for blood purification indications in all systemic inflammation syndromes. Clinical and experimental studies show that the use of high effluent flows in a pure diffusive treatment effectively removes serum cytokines with a safe profile in albumin clearance. In clinical studies, the removal of these inflammatory mediators is associated with a significant improvement in hemodynamic condition, oxygenation indices, and organ dysfunction.
Facial expression recognition based on Local Binary Patterns: A comprehensive study

Automatic facial expression analysis is an interesting and challenging problem, and impacts important applications in many areas such as human–computer interaction and data-driven animation. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. In this paper, we empirically evaluate facial representation based on statistical local features, Local Binary Patterns, for person-independent facial expression recognition. Different machine learning methods are systematically examined on several databases. Extensive experiments illustrate that LBP features are effective and efficient for facial expression recognition. We further formulate Boosted-LBP to extract the most discriminant LBP features, and the best recognition performance is obtained by using Support Vector Machine classifiers with Boosted-LBP features. Moreover, we investigate LBP features for low-resolution facial expression recognition, which is a critical problem but seldom addressed in the existing work. We observe in our experiments that LBP features perform stably and robustly over a useful range of low resolutions of face images, and yield promising performance in compressed low-resolution video sequences captured in real-world environments.
ParaPIM: a parallel processing-in-memory accelerator for binary-weight deep neural networks

Recent algorithmic progression has brought competitive classification accuracy despite constraining neural networks to binary weights (+1/-1). These findings show remarkable optimization opportunities to eliminate the need for computationally-intensive multiplications, reducing memory access and storage. In this paper, we present ParaPIM architecture, which transforms current Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) sub-arrays to massively parallel computational units capable of running inferences for Binary-Weight Deep Neural Networks (BWNNs). ParaPIM's in-situ computing architecture can be leveraged to greatly reduce energy consumption dealing with convolutional layers, accelerate BWNNs inference, eliminate unnecessary off-chip accesses and provide ultra-high internal bandwidth. The device-to-architecture co-simulation results indicate ~4x higher energy efficiency and 7.3x speedup over recent processing-in-DRAM acceleration, or roughly 5x higher energy-efficiency and 20.5x speedup over recent ASIC approaches, while maintaining inference accuracy comparable to baseline designs.
Traffic lights detection and state estimation using Hidden Markov Models

The detection of a traffic light on the road is important for the safety of persons who occupy a vehicle, in a normal vehicles or an autonomous land vehicle. In normal vehicle, a system that helps a driver to perceive the details of traffic signals, necessary to drive, could be critical in a delicate driving manoeuvre (i.e crossing an intersection of roads). Furthermore, traffic lights detection by an autonomous vehicle is a special case of perception, because it is important for the control that the autonomous vehicle must take. Multiples authors have used image processing as a base for achieving traffic light detection. However, the image processing presents a problem regarding conditions for capturing scenes, and therefore, the traffic light detection is affected. For this reason, this paper proposes a method that links the image processing with an estimation state routine formed by Hidden Markov Models (HMM). This method helps to determine the current state of the traffic light detected, based on the obtained states by image processing, aiming to obtain the best performance in the determination of the traffic light states. With the proposed method in this paper, we obtained 90.55% of accuracy in the detection of the traffic light state, versus a 78.54% obtained using solely image processing. The recognition of traffic lights using image processing still has a large dependence on the capture conditions of each frame from the video camera. In this context, the addition of a pre-processing stage before image processing could contribute to improve this aspect, and could provide a better results in determining the traffic light state.
3D Ego-Pose Estimation via Imitation Learning

Ego-pose estimation, i.e., estimating a person’s 3D pose with a single wearable camera, has many potential applications in activity monitoring. For these applications, both accurate and physically plausible estimates are desired, with the latter often overlooked by existing work. Traditional computer vision-based approaches using temporal smoothing only take into account the kinematics of the motion without considering the physics that underlies the dynamics of motion, which leads to pose estimates that are physically invalid. Motivated by this, we propose a novel control-based approach to model human motion with physics simulation and use imitation learning to learn a videoconditioned control policy for ego-pose estimation. Our imitation learning framework allows us to perform domain adaption to transfer our policy trained on simulation data to real-world data. Our experiments with real egocentric videos show that our method can estimate both accurate and physically plausible 3D ego-pose sequences without observing the cameras wearer’s body.
A stochastic neighborhood search approach for airport gate assignment problem

An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for _ Istanbul Atatürk Airport.