nils-herrmann/clean_scirepeval huggingface dataset:
Monocular Pedestrian Detection: Survey and Experiments

Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade, HOG/linSVM, NN/LRF, and combined shape-texture detection. Experiments are performed on an extensive data set captured onboard a vehicle driving through urban environment. The data set includes many thousands of training samples as well as a 27-minute test sequence involving more than 20,000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The data set (8.5 GB) is made public for benchmarking purposes.
IoT Security and Privacy Issues

Internet of Things (IoT) represents the future of technology where objects will be able to communicate with each other and connect to the internet to create self-configuration, intelligent system. Beside IoT development benefits, there is still great doubt about security and privacy which are considered major challenges facing the adaption and development of IoT architecture. Security and privacy issues are the most critical challenge that needs to be addressed in each IoT layer and it is not fully addressed yet. There have been many studies propose solutions to address security issues. Protecting IoT require appropriate security framework that covers all IoT layer-security issues. This paper presents a review of security and privacy issues and challenges in IoT. It also shed a light on security requirements relevant to IoT environment and some proposed solutions.
1 . RELATED WORK 1 . 1 3 D Reconstruction

Creating and animating realistic 3D human faces is an important element of virtual reality, video games, and other areas that involve interactive 3D graphics. In this paper, we propose a system to generate photorealistic 3D blendshape-based face models automatically using only a single consumer RGB-D sensor. The capture and processing requires no artistic expertise to operate, takes 15 seconds to capture and generate a single facial expression, and approximately 1 minute of processing time per expression to transform it into a blendshape model. Our main contributions include a complete end-to-end pipeline for capturing and generating photorealistic blendshape models automatically and a registration method that solves dense correspondences between two face scans by utilizing facial landmarks detection and optical flows. We demonstrate the effectiveness of the proposed method by capturing different human subjects with a variety of sensors and puppeteering their 3D faces with real-time facial performance retargeting. The rapid nature of our method allows for just-in-time construction of a digital face. To that end, we also integrated our pipeline with a virtual reality facial performance capture system that allows dynamic embodiment of the generated faces despite partial occlusion of the user’s real face by the head-mounted display.
Resistance to change and the adoption of digital libraries: An integrative model

In this paper we extend earlier work on the role of the personality trait of Resistance to Change (RTC) in the adoption of digital libraries. We present an integrative study, drawing on a number of research streams, including IT adoption, social psychology, and digital library acceptance. Using structural equation modeling, we confirm RTC as a direct antecedent of effort expectancy. In addition, we also find that by affecting computer anxiety and result demonstrability, RTC acts as an indirect antecedent to both effort expectancy and performance expectancy, which in turn determine user intention to adopt digital library technology. Implications for research and practice are discussed.
Fusion of Magnetic and Visual Sensors for Indoor Localization: Infrastructure-Free and More Effective

Accurate and infrastructure-free indoor positioning can be very useful in a variety of applications. However, most existing approaches (e.g., WiFi and infrared-based methods) for indoor localization heavily rely on infrastructure, which is neither scalable nor pervasively available. In this paper, we propose a novel indoor localization and tracking approach, termed VMag, that does not require any infrastructure assistance. The user can be localized while simply holding a smartphone. To the best of our knowledge, the proposed method is the first exploration of fusing geomagnetic and visual sensing for indoor localization. More specifically, we conduct an in-depth study on both the advantageous properties and the challenges in leveraging the geomagnetic field and visual images for indoor localization. Based on these studies, we design a context-aware particle filtering framework to track the user with the goal of maximizing the positioning accuracy. We also introduce a neural-network-based method to extract deep features for the purpose of indoor positioning. We have conducted extensive experiments on four different indoor settings including a laboratory, a garage, a canteen, and an office building. Experimental results demonstrate the superior performance of VMag over the state of the art with these four indoor settings.
Viewing Real-World Faces in 3D

We present a data-driven method for estimating the 3D shapes of faces viewed in single, unconstrained photos (aka "in-the-wild"). Our method was designed with an emphasis on robustness and efficiency - with the explicit goal of deployment in real-world applications which reconstruct and display faces in 3D. Our key observation is that for many practical applications, warping the shape of a reference face to match the appearance of a query, is enough to produce realistic impressions of the query's 3D shape. Doing so, however, requires matching visual features between the (possibly very different) query and reference images, while ensuring that a plausible face shape is produced. To this end, we describe an optimization process which seeks to maximize the similarity of appearances and depths, jointly, to those of a reference model. We describe our system for monocular face shape reconstruction and present both qualitative and quantitative experiments, comparing our method against alternative systems, and demonstrating its capabilities. Finally, as a testament to its suitability for real-world applications, we offer an open, on-line implementation of our system, providing unique means of instant 3D viewing of faces appearing in web photos.
Event-Based Multi-level Service Monitoring

Due to the growing pervasiveness of the service paradigm, modern systems are now often built as software as a service, and tend to exploit underlying platforms and virtualized resources also provided as services. Managing such systems requires that we be aware of the behaviors of all the different layers, and of the strong dependencies that exist between them. In this paper we present the Multi-layer Collection and Constraint Language (mlCCL). It allows us to define how to collect, aggregate, and analyze runtime data in a multi-layered system. We also present ECoWare, a framework for event correlation and aggregation that supports mlCCL, and provides a dashboard for on-line and off-line drill-down analyses of collected data. Empirical assessment shows that the impact of the approach on runtime performance is negligible.
Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation

Recently, there has been a dramatic proliferation in the number of blogs; however, little is published about what motivates people to participate in blog activities. Based on the theory of reasoned action, we developed a model involving technology acceptance, knowledge sharing and social influences. A survey of 212 blog participants found strong support for the model. The results indicated that ease of use and enjoyment, and knowledge sharing (altruism and reputation) were positively related to attitude toward blogging, and accounted for 78% of the variance. On the other hand, social factors (community identification) and attitude toward blogging significantly influenced a blog participant’s intention to continue to use blogs. Together they explain 83% of the variance of intention to blog.
AESvisual: A Visualization Tool for the AES Cipher

This paper describes a visualization tool AESvisual that helps students learn and instructors teach the AES cipher. The software allows the user to visualize all the major steps of AES encryption and decryption. The demo mode is useful and efficient for classroom presentation and the practice mode provides the user with an environment to practice AES encryption with error checking. AESvisual is quite versatile, providing support for both beginners learning how to encrypt and decrypt, and also for the more advanced users wishing to see all the details, including the GF(28) addition and multiplication operations. Classroom evaluation of the tool was positive.
Laser ranging : a critical review of usual techniques for distance measurement

Marc Rioux National Research Council, Canada Institute for Information Technology Visual Information Technology Ottawa, Canada, K1A 0R6 Abstract. We review some usual laser range finding techniques for industrial applications. After outlining the basic principles of triangulation and time of flight [pulsed, phase-shift and frequency modulated continuous wave (FMCW)], we discuss their respective fundamental limitations. Selected examples of traditional and new applications are also briefly presented.
A Robust Horn Ridge Gap Waveguide Launcher for Metal Strip Grating Leaky Wave Antenna

An H-polarized leaky wave antenna (LWA) employing metal strip grating (MSG) is presented. A novel feeding system based on the ridge gap waveguide (RGW) technology feeds the proposed antenna. The proposed feeding mechanism is made up of a profiled Quasi-TEM horn ridge gap waveguide, which is designed to have the same aperture width and provide uniform field excitation to the leaky wave antenna. The design is performed to provide proper matching between the launcher and the MSG to improve the antenna radiation efficiency. The proposed antenna is operating at 28.5 GHz with a scanning angle capability of -20 ° achieving a realized gain of 24 dBi. Several numerical results using point-matching and aperture distribution method are used to analyze the proposed LWA. Moreover, measurement results are presented to illustrate the effectiveness of the novel feeding system functioning as the LWA launcher.
Space-time sketching of character animation

We present a space-time abstraction for the sketch-based design of character animation. It allows animators to draft a full coordinated motion using a single stroke called the space-time curve (STC). From the STC we compute a dynamic line of action (DLOA) that drives the motion of a 3D character through projective constraints. Our dynamic models for the line's motion are entirely geometric, require no pre-existing data, and allow full artistic control. The resulting DLOA can be refined by over-sketching strokes along the space-time curve, or by composing another DLOA on top leading to control over complex motions with few strokes. Additionally, the resulting dynamic line of action can be applied to arbitrary body parts or characters. To match a 3D character to the 2D line over time, we introduce a robust matching algorithm based on closed-form solutions, yielding a tight match while allowing squash and stretch of the character's skeleton. Our experiments show that space-time sketching has the potential of bringing animation design within the reach of beginners while saving time for skilled artists.
Unleashing Mayhem on Binary Code

In this paper we present Mayhem, a new system for automatically finding exploitable bugs in binary (i.e., executable) programs. Every bug reported by Mayhem is accompanied by a working shell-spawning exploit. The working exploits ensure soundness and that each bug report is security-critical and actionable. Mayhem works on raw binary code without debugging information. To make exploit generation possible at the binary-level, Mayhem addresses two major technical challenges: actively managing execution paths without exhausting memory, and reasoning about symbolic memory indices, where a load or a store address depends on user input. To this end, we propose two novel techniques: 1) hybrid symbolic execution for combining online and offline (concolic) execution to maximize the benefits of both techniques, and 2) index-based memory modeling, a technique that allows Mayhem to efficiently reason about symbolic memory at the binary level. We used Mayhem to find and demonstrate 29 exploitable vulnerabilities in both Linux and Windows programs, 2 of which were previously undocumented.
Motion-based detection and tracking in 3D LiDAR scans

Robots are expected to operate autonomously in increasingly complex scenarios such as crowded streets or heavy traffic situations. Perceiving the dynamics of moving objects in the environment is crucial for safe and smart navigation and therefore a key enabler for autonomous driving. In this paper we present a novel model-free approach for detecting and tracking dynamic objects in 3D LiDAR scans obtained by a moving sensor. Our method only relies on motion cues and does not require any prior information about the objects. We sequentially detect multiple motions in the scene and segment objects using a Bayesian approach. For robustly tracking objects, we utilize their estimated motion models. We present extensive quantitative results based on publicly available datasets and show that our approach outperforms the state of the art.
A foundation for representing and querying moving objects

Spatio-temporal databases deal with geometries changing over time. The goal of our work is to provide a DBMS data model and query language capable of handling such time-dependent geometries, including those changing continuously that describe moving objects. Two fundamental abstractions are moving point and moving region, describing objects for which only the time-dependent position, or position and extent, respectively, are of interest. We propose to present such time-dependent geometries as attribute data types with suitable operations, that is, to provide an abstract data type extension to a DBMS data model and query language. This paper presents a design of such a system of abstract data types. It turns out that besides the main types of interest, moving point and moving region, a relatively large number of auxiliary data types are needed. For example, one needs a line type to represent the projection of a moving point into the plane, or a “moving real” to represent the time-dependent distance of two points. It then becomes crucial to achieve (i) orthogonality in the design of the system, i.e., type constructors can be applied unifomly; (ii) genericity and consistency of operations, i.e., operations range over as many types as possible and behave consistently; and (iii) closure and consistency between structure and operations of nontemporal and related temporal types. Satisfying these goal leads to a simple and expressive system of abstract data types that may be integrated into a query language to yield a powerful language for querying spatio-temporal data, including moving objects. The paper formally defines the types and operations, offers detailed insight into the considerations that went into the design, and exemplifies the use of the abstract data types using SQL. The paper offers a precise and conceptually clean foundation for implementing a spatio-temporal DBMS extension.
1.8GHz printed bow-tie dipole rectenna with voltage quadrupler for RF energy harvesting

This paper presents printed bow-tie dipole rectenna which works at frequency of 1.8GHz for RF energy harvesting application. The proposed rectenna consists of receiver printed bow-tie dipole antenna and rectifier circuit acts as voltage quadrupler. The bow-tie dipole antenna is chosen due to its simply design, wide bandwidth, and low weight. An FR4 epoxy dielectric substrate with the thickness of 1.6mm is used to deploy the bow-tie dipole antenna. Meanwhile, the rectifier circuit with voltage quadrupler which is constructed of diodes and capacitors is expected to produce high enough output voltage usable for supplying small power device. The characterization through simulation and measurement shows that four bow-tie dipole antennas have satisfied the required specification at frequency of 1.8GHz and are producible the output voltage of 65mV–250mV after being rectified and processed into voltage quadrupler.
The Cityscapes Dataset for Semantic Urban Scene Understanding

Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.
Ischiofemoral Impingement Syndrome

Ischiofemoral impingement syndrome is known as one of the causes of hip pain due to impingement of ischium and femur, and usually correlated with trauma or operation. We report a rare case of ischiofemoral impingement syndrome that has no history of trauma or surgery. A 48-year-old female patient was referred for 2 months history of the left hip pain, radiating to lower extremity with a hip snapping sensation. She had no history of trauma or surgery at or around the hip joint and femur. The magnetic resonance imaging (MRI) of the lumbar spine showed no abnormality, except diffuse bulging disc without cord compression at the lumbosacral area. Electrophysiologic study was normal, and there were no neurologic abnormalities compatible with the lumbosacral radiculopathy or spinal stenosis. Hip MRI revealed quadratus femoris muscle edema with concurrent narrowing of the ischiofemoral space. The distance of ischiofemoral space and quadratus femoris space were narrow. It was compatible with ischiofemoral impingement syndrome. After treatment with nonsteroidal anti-inflammatory drugs, physical therapy, and exercise program, the patient's pain was relieved and the snapping was improved. To our knowledge, this is the first reported case of a nontraumatic, noniatrogenic ischiofemoral impingement syndrome, and also the first case to be treated by a nonsurgical method in the Republic of Korea.
Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity

Semantic similarity is an essential component of many Natural Language Processing applications. However, prior methods for computing semantic similarity often operate at different levels, e.g., single words or entire documents, which requires adapting the method for each data type. We present a unified approach to semantic similarity that operates at multiple levels, all the way from comparing word senses to comparing text documents. Our method leverages a common probabilistic representation over word senses in order to compare different types of linguistic data. This unified representation shows state-ofthe-art performance on three tasks: semantic textual similarity, word similarity, and word sense coarsening.
A hybrid medium access control protocol for underwater wireless networks

Underwater networks allow investigation of many areas of the world not easily accessed by humans, but offer interesting challenges to device and protocol designers due to the unique channel conditions present when using acoustic communications. The high transmit power of acoustic transceivers makes the medium access protocol a primary focus point for reducing energy consumption in energy limited underwater devices. Scheduled protocols use very little power by eliminating collisions, but cannot adapt to changing traffic conditions in the same way as random protocols. We attempt to bridge these two ideas by dividing time into scheduled and unscheduled access periods in order to yield the benefits of each protocol. We show that this technique increases the bits delivered per energy unit in many cases of interest. Additionally, the hybrid technique provides low latency for a wider range of traffic rates than either of the two protocols when considered individually. We also investigate some of the design tradeoffs to consider when using a hybrid protocol.
Improvements to Frank-Wolfe optimization for multi-detector multi-object tracking

This paper proposes a novel formulation for the multiobject tracking-by-detection paradigm for two (or more) input detectors. Using full-body and heads detections, the fusion helps to recover heavily occluded persons and to reduce false positives. The assignment of the two input features to a person and the extraction of the trajectories is commonly solved from one binary quadratic program (BQP). Due to the computational complexity of the NP-hard QP, we approximate the solution using the Frank-Wolfe algorithm. We propose several improvements to this solver affecting better minimization and shorter computations, compared to off-the-shelf BQP-solvers and the standard FrankWolfe algorithm. Evaluation on pedestrian tracking is provided for multiple scenarios, showing improved tracking quality over single input feature trackers and standard QPsolvers. Finally we present the performance of our tracker on the challenging MOT16 benchmark, being comparable to state-of-the-art trackers.
Analysis and control of a rubbertuator arm

The control of light-weight compliant robot arms is cumbersome due to the fact that their Coriolis forces are large, and the forces exerted by the relatively weak actuators may change in time as the result of external (e.g. temperature) influences. We describe and analyse the behaviour of a light-weight robot arm, the SoftArm robot. It is found that the hysteretic force-position relationship of the arm can be explained from its structure. This knowledge is used in the construction of a neural-network-based controller. Experiments show that the network is able to control the robot arm accurately after a training session of only a few minutes.