nils-herrmann/clean_scirepeval huggingface dataset:
3DensiNet: A Robust Neural Network Architecture towards 3D Volumetric Object Prediction from 2D Image

3D volumetric object generation/prediction from single 2D image is a quite challenging but meaningful task in 3D visual computing. In this paper, we propose a novel neural network architecture, named "3DensiNet", which uses density heat-map as an intermediate supervision tool for 2D-to-3D transformation. Specifically, we firstly present a 2D density heat-map to 3D volumetric object encoding-decoding network, which outperforms classical 3D autoencoder. Then we show that using 2D image to predict its density heat-map via a 2D to 2D encoding-decoding network is feasible. In addition, we leverage adversarial loss to fine tune our network, which improves the generated/predicted 3D voxel objects to be more similar to the ground truth voxel object. Experimental results on 3D volumetric prediction from 2D images demonstrates superior performance of 3DensiNet over other state-of-the-art techniques in handling 3D volumetric object generation/prediction from single 2D image.
Adaptive multimedia streaming in information-centric networks

ICN has received a lot of attention in recent years, and is a promising approach for the Future Internet design. As multimedia is the dominating traffic in today's and (most likely) the Future Internet, it is important to consider this type of data transmission in the context of ICN. In particular, the adaptive streaming of multimedia content is a promising approach for usage within ICN, as the client has full control over the streaming session and has the possibility to adapt the multimedia stream to its context (e.g. network conditions, device capabilities), which is compatible with the paradigms adopted by ICN. In this article we investigate the implementation of adaptive multimedia streaming within networks adopting the ICN approach. In particular, we present our approach based on the recently ratified ISO/IEC MPEG standard Dynamic Adaptive Streaming over HTTP and the ICN representative Content-Centric Networking, including baseline evaluations and open research challenges.
The User's Mental Model of an Information Retrieval System: An Experiment on a Prototype Online Catalog

An empirical study was performed to train naive subjects in the use of a prototype Boolean logic-based information retrieval system on a bibliographic database. Subjects were undergraduates with little or no prior computing experience. Subjects trained with a conceptual model of the system performed better than subjects trained with procedural instructions, but only on complex, problem-solving tasks. Performance was equal on simple tasks. Differences in patterns of interaction with the system (based on a stochastic process model) showed parallel results. Most subjects were able to articulate some description of the system's operation, but few articulated a model similar to the card catalog analogy provided in training. Eleven of 43 subjects were unable to achieve minimal competency in system use. The failure rate was equal between training conditions and genders; the only differences found between those passing and failing the benchmark test were academic major and in frequency of library use.
Real-Time Autocalibration of MEMS Accelerometers

In this paper, the self-calibration of micromechanical acceleration sensors is considered, specifically, based solely on user-generated movement data without the support of laboratory equipment or external sources. The autocalibration algorithm itself uses the fact that under static conditions, the squared norm of the measured sensor signal should match the magnitude of the gravity vector. The resulting nonlinear optimization problem is solved using robust statistical linearization instead of the common analytical linearization for computing bias and scale factors of the accelerometer. To control the forgetting rate of the calibration algorithm, artificial process noise models are developed and compared with conventional ones. The calibration methodology is tested using arbitrarily captured acceleration profiles of the human daily routine and shows that the developed algorithm can significantly reject any misconfiguration of the acceleration sensor.
struc2vec: Learning Node Representations from Structural Identity

Structural identity is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. Structural identity has been studied in theory and practice over the past decades, but only recently has it been addressed with representational learning techniques. This work presents struc2vec, a novel and flexible framework for learning latent representations for the structural identity of nodes. struc2vec uses a hierarchy to measure node similarity at different scales, and constructs a multilayer graph to encode structural similarities and generate structural context for nodes. Numerical experiments indicate that state-of-the-art techniques for learning node representations fail in capturing stronger notions of structural identity, while struc2vec exhibits much superior performance in this task, as it overcomes limitations of prior approaches. As a consequence, numerical experiments indicate that struc2vec improves performance on classification tasks that depend more on structural identity.
Radiomics: Images Are More than Pictures, They Are Data

In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
Intrinsic camera and hand-eye calibration for a robot vision system using a point marker

Accurate robot camera calibration is a requirement for vision guided robots to perform precision assembly tasks. In this paper, we address the problem of doing intrinsic camera and hand-eye calibration on a robot vision system using a single point marker. This removes the need for using bulky special purpose calibration objects, and also facilitates on line accuracy checking and re-calibration when needed, without altering the robots production environment. The proposed solution provides a calibration routine that produces high quality results on par with the robot accuracy and completes a calibration in 3 minutes without need of manual intervention. We also present a method for automatic testing of camera calibration accuracy. Results from experimental verification on the dual arm concept robot FRIDA are presented.
Learning Combinatorial Optimization Algorithms over Graphs

The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error. Can we automate this challenging, tedious process, and learn the algorithms instead? In many real-world applications, it is typically the case that the same optimization problem is solved again and again on a regular basis, maintaining the same problem structure but differing in the data. This provides an opportunity for learning heuristic algorithms that exploit the structure of such recurring problems. In this paper, we propose a unique combination of reinforcement learning and graph embedding to address this challenge. The learned greedy policy behaves like a meta-algorithm that incrementally constructs a solution, and the action is determined by the output of a graph embedding network capturing the current state of the solution. We show that our framework can be applied to a diverse range of optimization problems over graphs, and learns effective algorithms for the Minimum Vertex Cover, Maximum Cut and Traveling Salesman problems.
Detection Algorithms for Communication Systems Using Deep Learning

The design and analysis of communication systems typically rely on the development of mathematical models that describe the underlying communication channel, which dictates the relationship between the transmitted and the received signals. However, in some systems, such as molecular communication systems where chemical signals are used for transfer of information, it is not possible to accurately model this relationship. In these scenarios, because of the lack of mathematical channel models, a completely new approach to design and analysis is required. In this work, we focus on one important aspect of communication systems, the detection algorithms, and demonstrate that by borrowing tools from deep learning, it is possible to train detectors that perform well, without any knowledge of the underlying channel models. We evaluate these algorithms using experimental data that is collected by a chemical communication platform, where the channel model is unknown and difficult to model analytically. We show that deep learning algorithms perform significantly better than a simple detector that was used in previous works, which also did not assume any knowledge of the channel.
A Vision-Based Parking Lot Management System

Goals of parking lot management system include counting the number of parked vehicles, monitoring the changes of the parked vehicles over the time, and identifying the stalls available. To decrease the cost of the production, an integrated vision-based system is a good choice. In this paper, we propose a vision-based parking management system to manage an outdoor parking lot by four cameras set up at loft of buildings around it, sending information, including real-time display, to database of ITS center via internet. This system enables drivers to find parking spaces available or monitoring the parking lot where they parked their cars easily by wireless communication device. To increase accuracy, in the beginning, color manage is done to all input images, maintaining color consistency. Then, an adaptive parking lot background model is generated. The adequate color of each parking space is found out using statistical method in color image sequences captured by a camera, and foreground is extracted based on color information. The result will be further modified by shadow detection based on luminance analysis. Vision-based parking management system can manage large area by just several cameras. Adjusting position of the camera can easily make this system suitable for most cases. Besides, this system is endurable and is easy-installed because of its simple equipment.
DeepLaser: Practical Fault Attack on Deep Neural Networks.

As deep learning systems are widely adopted in safetyand securitycritical applications, such as autonomous vehicles, banking systems, etc., malicious faults and attacks become a tremendous concern, which potentially could lead to catastrophic consequences. In this paper, we initiate the first study of leveraging physical fault injection attacks on Deep Neural Networks (DNNs), by using laser injection technique on embedded systems. In particular, our exploratory study targets four widely used activation functions in DNNs development, that are the general main building block of DNNs that creates non-linear behaviors – ReLu, softmax, sigmoid, and tanh. Our results show that by targeting these functions, it is possible to achieve a misclassification by injecting faults into the hidden layer of the network. Such result can have practical implications for realworld applications, where faults can be introduced by simpler means (such as altering the supply voltage).
Secure communication for the Internet of Things - a comparison of link-layer security and IPsec for 6LoWPAN

The future Internet is an IPv6 network interconnecting traditional computers and a large number of smart objects. This Internet of Things (IoT) will be the foundation of many services and our daily life will depend on its availability and reliable operation. Therefore, among many other issues, the challenge of implementing secure communication in the IoT must be addressed. In the traditional Internet, IPsec is the established and tested way of securing networks. It is therefore reasonable to explore the option of using IPsec as a security mechanism for the IoT. Smart objects are generally added to the Internet using IPv6 over Low-power Wireless Personal Area Networks (6LoWPAN), which defines IP communication for resource-constrained networks. Thus, to provide security for the IoT based on the trusted and tested IPsec mechanism, it is necessary to define an IPsec extension of 6LoWPAN. In this paper, we present such a 6LoWPAN/IPsec extension and show the viability of this approach. We describe our 6LoWPAN/IPsec implementation, which we evaluate and compare with our implementation of IEEE 802.15.4 link-layer security. We also show that it is possible to reuse crypto hardware within existing IEEE 802.15.4 transceivers for 6LoWPAN/IPsec. The evaluation results show that IPsec is a feasible option for securing the IoT in terms of packet size, energy consumption, memory usage, and processing time. Furthermore, we demonstrate that in contrast to common belief, IPsec scales better than link-layer security as the data size and the number of hops grow, resulting in time and energy savings.
An LPV Modeling and Identification Approach to Leakage Detection in High Pressure Natural Gas Transportation Networks

In this paper a new approach to gas leakage detection in high pressure natural gas transportation networks is proposed. The pipeline is modelled as a Linear Parameter Varying (LPV) System driven by the source node massflow with the gas inventory variation in the pipe (linepack variation, proportional to the pressure variation) as the scheduling parameter. The massflow at the offtake node is taken as the system output. The system is identified by the Successive Approximations LPV System Subspace Identification Algorithm which is also described in this paper. The leakage is detected using a Kalman filter where the fault is treated as an augmented state. Given that the gas linepack can be estimated from the massflow balance equation, a differential method is proposed to improve the leakage detector effectiveness. A small section of a gas pipeline crossing Portugal in the direction South to North is used as a case study. LPV models are identified from normal operational data and their accuracy is analyzed. The proposed LPV Kalman filter based methods are compared with a standard mass balance method in a simulated 10% leakage detection scenario. The Differential Kalman Filter method proved to be highly efficient.
From Intrusion Detection to an Intrusion Response System: Fundamentals, Requirements, and Future Directions

In the past few decades, the rise in attacks on communication devices in networks has resulted in a reduction of network functionality, throughput, and performance. To detect and mitigate these network attacks, researchers, academicians, and practitioners developed Intrusion Detection Systems (IDSs) with automatic response systems. The response system is considered an important component of IDS, since without a timely response IDSs may not function properly in countering various attacks, especially on a real-time basis. To respond appropriately, IDSs should select the optimal response option according to the type of network attack. This research study provides a complete survey of IDSs and Intrusion Response Systems (IRSs) on the basis of our in-depth understanding of the response option for different types of network attacks. Knowledge of the path from IDS to IRS can assist network administrators and network staffs in understanding how to tackle different attacks with state-of-the-art technologies.
MnasNet: Platform-Aware Neural Architecture Search for Mobile

Designing convolutional neural networks (CNN) models for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant effort has been dedicated to design and improve mobile models on all three dimensions, it is challenging to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated neural architecture search approach for designing resourceconstrained mobile CNN models. We propose to explicitly incorporate latency information into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike in previous work, where mobile latency is considered via another, often inaccurate proxy (e.g., FLOPS), in our experiments, we directly measure real-world inference latency by executing the model on a particular platform, e.g., Pixel phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that permits layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our model achieves 74.0% top-1 accuracy with 76ms latency on a Pixel phone, which is 1.5× faster than MobileNetV2 (Sandler et al. 2018) and 2.4× faster than NASNet (Zoph et al. 2018) with the same top-1 accuracy. On the COCO object detection task, our model family achieves both higher mAP quality and lower latency than MobileNets.
Channel Estimation and Equalization for 5 G Wireless Communication Systems

In this thesis, channel estimation techniques are studied and investigated for a novel multicarrier modulation scheme, Universal Filtered Multi-Carrier (UFMC). UFMC (a.k.a. UFOFDM) is considered as a candidate for the 5th Generation of wireless communication systems, which aims at replacing OFDM and enhances system robustness and performance in relaxed synchronization condition e.g. time-frequency misalignment. Thus, it may more efficiently support Machine Type Communication (MTC) and Internet of Things (IoT), which are considered as challenging applications for next generation of wireless communication systems. There exist many methods of channel estimation, time-frequency synchronization and equalization for classical CP-OFDM systems. Pilot-aided methods known from CP-OFDM are adopted and applied to UFMC systems. The performance of UFMC is then compared with CP-OFDM.
A hybrid deep architecture for robotic grasp detection

The robotic grasp detection is a great challenge in the area of robotics. Previous work mainly employs the visual approaches to solve this problem. In this paper, a hybrid deep architecture combining the visual and tactile sensing for robotic grasp detection is proposed. We have demonstrated that the visual sensing and tactile sensing are complementary to each other and important for the robotic grasping. A new THU grasp dataset has also been collected which contains the visual, tactile and grasp configuration information. The experiments conducted on a public grasp dataset and our collected dataset show that the performance of the proposed model is superior to state of the art methods. The results also indicate that the tactile data could help to enable the network to learn better visual features for the robotic grasp detection task.
A Survey and Evaluation of FPGA High-Level Synthesis Tools

High-level synthesis (HLS) is increasingly popular for the design of high-performance and energy-efficient heterogeneous systems, shortening time-to-market and addressing today's system complexity. HLS allows designers to work at a higher-level of abstraction by using a software program to specify the hardware functionality. Additionally, HLS is particularly interesting for designing field-programmable gate array circuits, where hardware implementations can be easily refined and replaced in the target device. Recent years have seen much activity in the HLS research community, with a plethora of HLS tool offerings, from both industry and academia. All these tools may have different input languages, perform different internal optimizations, and produce results of different quality, even for the very same input description. Hence, it is challenging to compare their performance and understand which is the best for the hardware to be implemented. We present a comprehensive analysis of recent HLS tools, as well as overview the areas of active interest in the HLS research community. We also present a first-published methodology to evaluate different HLS tools. We use our methodology to compare one commercial and three academic tools on a common set of C benchmarks, aiming at performing an in-depth evaluation in terms of performance and the use of resources.
A Novel Transformerless Interleaved High Step-Down Conversion Ratio DC–DC Converter With Low Switch Voltage Stress

In this paper, a novel transformerless interleaved high step-down conversion ratio dc-dc converter with low switch voltage stress is proposed. In the proposed converter, two input capacitors are series-charged by the input voltage and parallel-discharged by a new two-phase interleaved buck converter for providing a much higher step-down conversion ratio without adopting an extreme short duty cycle. Based on the capacitive voltage division, the main objectives of the new voltage-divider circuit in the converter are for both storing energy in the blocking capacitors for increasing the step-down conversion ratio and reducing voltage stresses of active switches. As a result, the proposed converter topology possesses the low switch voltage stress characteristic. This will allow one to choose lower voltage rating MOSFETs to reduce both switching and conduction losses, and the overall efficiency is consequently improved. Moreover, due to the charge balance of the blocking capacitor, the converter features automatic uniform current sharing characteristic of the interleaved phases without adding extra circuitry or complex control methods. The operation principles and relevant analysis of the proposed converter are presented in this paper. Finally, a 400-V input voltage, 25-V output voltage, and 400-W output power prototype circuit is implemented in the laboratory to verify the performance.
An RC Oscillator With Comparator Offset Cancellation

A fully-integrated 18.5 kHz RC time-constant-based oscillator is designed in 65 nm CMOS for sleep-mode timers in wireless sensors. A comparator offset cancellation scheme achieves 4× to 25× temperature stability improvement, leading to an accuracy of ±0.18% to ±0.55% over -40 to 90 °C. Sub-threshold operation and low-swing oscillations result in ultra-low power consumption of 130 nW. The architecture also provides timing noise suppression, leading to 10× reduction in long-term Allan deviation. It is measured to have a stability of 20 ppm or better for measurement intervals over 0.5 s. The oscillator also has a fast startup-time, with the period settling in 4 cycles.