nils-herrmann/clean_scirepeval huggingface dataset:
Real-Time Object Detection for Unmanned Aerial Vehicles based on Cloud-based Convolutional Neural Networks

Real-time object detection is crucial for many applications of Unmanned Aerial Vehicles (UAVs) such as reconnaissance and surveillance, search-and-rescue, and infrastructure inspection. In the last few years, Convolutional Neural Networks (CNNs) have emerged as a powerful class of models for recognizing image content, and are widely considered in the computer vision community to be the de facto standard approach for most problems. However, object detection based on CNNs is extremely computationally demanding, typically requiring high-end Graphics Processing Units (GPUs) that require too much power and weight, especially for a lightweight and low-cost drone. In this paper, we propose moving the computation to an off-board computing cloud. We apply Regions with CNNs (R-CNNs), a state-of-the-art algorithm, to detect not one or two but hundreds of object types in near real-time.
A dissimilarity measure for the k-Modes clustering algorithm

Clustering is one of the most important data mining techniques that partitions data according to some similarity criterion. The problems of clustering categorical data have attracted much attention from the data mining research community recently. As the extension of the k-Means algorithm, the k-Modes algorithm has been widely applied to categorical data clustering by replacing means with modes. In this paper, the limitations of the simple matching dissimilarity measure and Ng’s dissimilarity measure are analyzed using some illustrative examples. Based on the idea of biological and genetic taxonomy and rough membership function, a new dissimilarity measure for the k-Modes algorithm is defined. A distinct characteristic of the new dissimilarity measure is to take account of the distribution of attribute values on the whole universe. A convergence study and time complexity of the k-Modes algorithm based on new dissimilarity measure indicates that it can be effectively used for large data sets. The results of comparative experiments on synthetic data sets and five real data sets from UCI show the effectiveness of the new dissimilarity measure, especially on data sets with biological and genetic taxonomy information.
Pedicle screw-rod fixation: a feasible treatment for dogs with severe degenerative lumbosacral stenosis

Degenerative lumbosacral stenosis is a common problem in large breed dogs. For severe degenerative lumbosacral stenosis, conservative treatment is often not effective and surgical intervention remains as the last treatment option. The objective of this retrospective study was to assess the middle to long term outcome of treatment of severe degenerative lumbosacral stenosis with pedicle screw-rod fixation with or without evidence of radiological discospondylitis.



Twelve client-owned dogs with severe degenerative lumbosacral stenosis underwent pedicle screw-rod fixation of the lumbosacral junction. During long term follow-up, dogs were monitored by clinical evaluation, diagnostic imaging, force plate analysis, and by using questionnaires to owners. Clinical evaluation, force plate data, and responses to questionnaires completed by the owners showed resolution (n = 8) or improvement (n = 4) of clinical signs after pedicle screw-rod fixation in 12 dogs. There were no implant failures, however, no interbody vertebral bone fusion of the lumbosacral junction was observed in the follow-up period. Four dogs developed mild recurrent low back pain that could easily be controlled by pain medication and an altered exercise regime.



Pedicle screw-rod fixation offers a surgical treatment option for large breed dogs with severe degenerative lumbosacral stenosis with or without evidence of radiological discospondylitis in which no other treatment is available. Pedicle screw-rod fixation alone does not result in interbody vertebral bone fusion between L7 and S1.
Assessment of item-writing flaws in multiple-choice questions.

This study evaluated the quality of multiple-choice questions used in a hospital's e-learning system. Constructing well-written questions is fraught with difficulty, and item-writing flaws are common. Study results revealed that most items contained flaws and were written at the knowledge/comprehension level. Few items had linked objectives, and no association was found between the presence of objectives and flaws. Recommendations include education for writing test questions.
A Bayesian Network Model for Optimizing Advertisements Allocation in Intermediate Online Targeted Advertising

Intermediate online targeted advertising (IOTA) is a new business model for online targeted advertising. Posting the right banner advertisement to the right web user at the right time is what advertisements allocation does in IOTA business model. This research uses probability theory to build a theoretical model based on Bayesian network to optimize advertisements allocation. The Bayesian network model allows us to calculate the probability that Web user will click the banner based on historical data. And these can help us to make optimal decision in advertisements allocation. Data availability is also be discussed in this paper. An experiment base on practical data is run to verify the feasibility of the Bayesian network model.
Decision Support Systems: From the Past to the Future

Information Systems researchers and technologists have built and investigated Decision Support Systems (DSS) for almost 40 years. This article is a narrative overview of the history of Decision Support Systems (DSS) and a means of gathering more first-hand accounts about the history of DSS. Readers are asked to comment upon the stimulus narrative titled “A Brief History of Decision Support Systems” that has been read by thousands of visitors to DSSResources.COM. Also, the stimulus narrative has been reviewed by a number of key actors who created the history of DSS. The narrative is divided into four sections: The Early Years – 1964-1975; Developing DSS Theory – 1976-1982; Expanding the Scope of Decision Support – 1979-1989; and A Technology Shift – 1990-1995.
Iris Template Protection based on Enhanced Hill Cipher

Biometric is uses to identify authorized person based on specific physiological or behavioral features. Template protection is a crucial requirement when designing an authentication system, where the template could be modified by attacker. Hill Cipher is a block cipher and symmetric key algorithm it has several advantages such as simplicity, high speed and high throughput can be used to protect Biometric Template. Unfortunately, Hill Cipher has some disadvantages such as takes smaller sizes of blocks, very simple and vulnerable for exhaustive key search attack and known plain text attack, also the key matrix which entered should be invertible. This paper proposed an enhancement to overcome these drawbacks of Hill Cipher by using a large and random key with large data block, beside overcome the Invertible-key Matrix problem. The efficiency of encryption has been checked out by Normalized Correlation Coefficient (NCC) and running time.
Integrated model of visual processing

Cortical processing of visual information requires that information be exchanged between neurons coding for distant regions in the visual field. It is argued that feedback connections are the best candidates for such rapid long-distance interconnections. In the integrated model, information arriving in the cortex from the magnocellular layers of the lateral geniculate nucleus is first sent and processed in the parietal cortex that is very rapidly activated by a visual stimulus. Results from this first-pass computation are then sent back by feedback connections to areas V1 and V2 that act as 'active black-boards' for the rest of the visual cortical areas: information retroinjected from the parietal cortex is used to guide further processing of parvocellular and koniocellular information in the inferotemporal cortex.
An FPGA-based accelerator implementation for deep convolutional neural networks

Deep convolutional neural networks (CNN) is highly efficient in image recognition tasks such as MNIST digit recognition. Accelerators based on FPGA platform are proposed since general purpose processor is disappointing in terms of performance when dealing with recognition tasks. Recently, an optimized FPGA-based accelerator design (work 1) has been proposed claiming best performance compared with existing implementations. But as the author acknowledged, performance could be better if fixed point presentation and computation elements had been used. Inspired by its methodology in implementing the Alexnet convolutional neural network, we implement a 5-layer accelerator for MNIST digit recognition task using the same Vivado HLS tool but using 11-bits fixed point precision on a Virtex7 FPGA. We compare performance on FPGA platform with the performance of the target CNN on MATLAB/CPU platform; we reach a speedup of 16.42. Our implementation runs at 150MHz and reaches a peak performance of 16.58 GMACS. Since our target CNN is simpler, we use much less resource than work 1 has used.
GMRES : A GENERALIZED MINIMAL RESIDUAL ALGORITHM FOR SOLVING NONSYMMETRIC LINEAR

We present an iterative method for solving linear systems, which has the property of minimizing at every step the norm of the residual vector over a Krylov subspace. The algorithm is derived from the Arnoldi process for constructing an /2-orthogonal basis of Krylov subspaces. It can be considered as a generalization of Paige and Saunders' MINRES algorithm and is theoretically equivalent to the Generalized Conjugate Residual (GCR) method and to ORTHODIR. The new algorithm presents several advantages over GCR and ORTHODIR.
Novel and Simple High-Frequency Single-Port Vector Network Analyzer

Portable, accurate, and relatively inexpensive high-frequency vector network analyzers (VNAs) have great utility for a wide range of applications, encompassing microwave circuit characterization, reflectometry, imaging, material characterization, and nondestructive testing to name a few. To meet the rising demand for VNAs possessing the aforementioned attributes, we present a novel and simple VNA design based on a standing-wave probing device and an electronically controllable phase shifter. The phase shifter is inserted between a device under test (DUT) and a standing-wave probing device. The complex reflection coefficient of the DUT is then obtained from multiple standing-wave voltage measurements taken for several different values of the phase shift. The proposed VNA design eliminates the need for expensive heterodyne detection schemes required for tuned-receiver-based VNA designs. Compared with previously developed VNAs that operate based on performing multiple power measurements, the proposed VNA utilizes a single power detector without the need for multiport hybrid couplers. In this paper, the efficacy of the proposed VNA is demonstrated via numerical simulations and experimental measurements. For this purpose, measurements of various DUTs obtained using an X-band (8.2-12.4 GHz) prototype VNA are presented and compared with results obtained using an Agilent HP8510C VNA. The results show that the proposed VNA provides highly accurate vector measurements with typical errors on the order of 0.02 and 1° for magnitude and phase, respectively.
Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment

A robust face detection technique along with mouth localization, processing every frame in real time (video rate), is presented. Moreover, it is exploited for motion analysis onsite to verify "liveness" as well as to achieve lip reading of digits. A methodological novelty is the suggested quantized angle features ("quangles") being designed for illumination invariance without the need for preprocessing (e.g., histogram equalization). This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. A major benefit is reduced processing time (i.e., that the training of effective cascaded classifiers is feasible in very short time, less than 1 h for data sets of order 104). Scale invariance is implemented through the use of an image scale pyramid. We propose "liveness" verification barriers as applications for which a significant amount of computation is avoided when estimating motion. Novel strategies to avert advanced spoofing attempts (e.g., replayed videos which include person utterances) are demonstrated. We present favorable results on face detection for the YALE face test set and competitive results for the CMU-MIT frontal face test set as well as on "liveness" verification barriers.
Pick-by-vision: bringing HMD-based augmented reality into the warehouse

The technology of Augmented Reality exists for several decades and it is attributed the potential to provide an ideal, efficient and intuitive way of presenting information. However it is not yet widely used. This is because the realization of such Augmented Reality systems requires to solve many principal problems from various areas. Much progress has been made in solving problems of core technologies, which enables us now to intensively explore the development of Augmented Reality applications. As an exemplary industrial use case for this exploration, I selected the order picking process in logistics applications. This thesis reports on the development of an application to support this task, by iteratively improving Augmented Reality-based metaphors. In such order picking tasks, workers collect sets of items from assortments in warehouses according to work orders. This order picking process has been subject to optimization for a long time, as it occurs a million times a day in industrial life. For this Augmented Reality application development, workers have been equipped with mobile hardware, consisting of a wearable computer (in a back-pack) and tracked head-mounted displays (HMDs). This thesis presents the iterative approach of exploring, evaluating and refining the Augmented Reality system, focusing on usability and utility. It starts in a simple laboratory setup and goes up to a realistic industrial setup in a factory hall. The Augmented Reality visualization shown in the HMD was the main subject of optimization in this thesis. Overall, the task was challenging, as workers have to be guided on different levels, from very coarse to very fine granularity and accuracy. The resulting setup consists of a combined and adaptive visualization to precisely and efficiently guide the user, even if the actual target of the augmentation is not always in the field of view of the HMD. A side-effect of this iterative evaluation and refinement of visualizations in an industrial setup is the report on many lessons learned and an advice on the way Augmented Reality user interfaces should be improved and refined.
Planning High-Quality Grasps Using Mean Curvature Object Skeletons

In this letter, we present a grasp planner that integrates two sources of information to generate robust grasps for a robotic hand. First, the topological information of the object model is incorporated by building the mean curvature skeleton and segmenting the object accordingly in order to identify object regions which are suitable for grasping. We show how this information can be used to derive different grasping strategies, which also allows to distinguish between precision and power grasps. Second, the local surface structure is investigated to construct feasible and robust grasping poses by aligning the hand according to the local object shape. We apply the approach to a wide variety of object models of the KIT and the YCB real-world object model databases and evaluate it with several robotic hands. The results show that the skeleton-based grasp planner is capable to generate high-quality grasps in an efficient manner. In addition, we evaluate how robust the planned grasps are against hand positioning errors as they occur in real-world applications due to perception and actuation inaccuracies. The evaluation shows that the majority of the generated grasps are of high quality since they can be successfully applied even when the hand is not exactly positioned.
Welding inverter for optimal arc characteristics is laterally more sensitive to input materials

Continuous evolution in process and in equipment in arc welding improves compatibility in energy domain. It helps optimize resources such as energy, materials, labor etc, and yield desired productivity and improved quality engineering goals. Modern inverter technology provides multitude of benefits virtually to each entity associated with the process. Efficiency, settling time of output variables and compactness and light weight are major pre-requisites of arc welding equipment. They are all achieved if switching frequency of inverter is kept high. Generally, higher switching frequency is achieved through soft switching. This paper elaborates that input-material-sensitive hard-switched topology is functionally superior to soft-switched topologies and generate superior design for manufacturability issues. It achieves power density ideal for welding power range.
A Flexible and Wearable Human Stress Monitoring Patch

A human stress monitoring patch integrates three sensors of skin temperature, skin conductance, and pulsewave in the size of stamp (25 mm × 15 mm × 72 μm) in order to enhance wearing comfort with small skin contact area and high flexibility. The skin contact area is minimized through the invention of an integrated multi-layer structure and the associated microfabrication process; thus being reduced to 1/125 of that of the conventional single-layer multiple sensors. The patch flexibility is increased mainly by the development of flexible pulsewave sensor, made of a flexible piezoelectric membrane supported by a perforated polyimide membrane. In the human physiological range, the fabricated stress patch measures skin temperature with the sensitivity of 0.31 Ω/°C, skin conductance with the sensitivity of 0.28 μV/0.02 μS, and pulse wave with the response time of 70 msec. The skin-attachable stress patch, capable to detect multimodal bio-signals, shows potential for application to wearable emotion monitoring.
Fast-Converging Conditional Generative Adversarial Networks for Image Synthesis

Building on top of the success of generative adversarial networks (GANs), conditional GANs attempt to better direct the data generation process by conditioning with certain additional information. Inspired by the most recent AC-GAN, in this paper we propose a fast-converging conditional GAN (FC-GAN). In addition to the real/fake classifier used in vanilla GANs, our discriminator has an advanced auxiliary classifier which distinguishes each real class from an extra ‘fake’ class. The ‘fake’ class avoids mixing generated data with real data, which can potentially confuse the classification of real data as AC-GAN does, and makes the advanced auxiliary classifier behave as another real/fake classifier. As a result, FC-GAN can accelerate the process of differentiation of all classes, thus boost the convergence speed. Experimental results on image synthesis demonstrate our model is competitive in the quality of images generated while achieving a faster convergence rate.
A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

Image cropping aims at improving the aesthetic quality of images by adjusting their composition. Most weakly supervised cropping methods (without bounding box supervision) rely on the sliding window mechanism. The sliding window mechanism requires fixed aspect ratios and limits the cropping region with arbitrary size. Moreover, the sliding window method usually produces tens of thousands of windows on the input image which is very time-consuming. Motivated by these challenges, we firstly formulate the aesthetic image cropping as a sequential decision-making process and propose a weakly supervised Aesthetics Aware Reinforcement Learning (A2-RL) framework to address this problem. Particularly, the proposed method develops an aesthetics aware reward function which especially benefits image cropping. Similar to human's decision making, we use a comprehensive state representation including both the current observation and the historical experience. We train the agent using the actor-critic architecture in an end-to-end manner. The agent is evaluated on several popular unseen cropping datasets. Experiment results show that our method achieves the state-of-the-art performance with much fewer candidate windows and much less time compared with previous weakly supervised methods.
Package-Aware Scheduling of FaaS Functions

We consider the problem of scheduling small cloud functions on serverless computing platforms. Fast deployment and execution of these functions is critical, for example, for microservices architectures. However, functions that require large packages or libraries are bloated and start slowly. A solution is to cache packages at the worker nodes instead of bundling them with the functions. However, existing FaaS schedulers are vanilla load balancers, agnostic of any packages that may have been cached in response to prior function executions, and cannot reap the benefits of package caching (other than by chance). To address this problem, we propose a package-aware scheduling algorithm that tries to assign functions that require the same package to the same worker node. Our algorithm increases the hit rate of the package cache and, as a result, reduces the latency of the cloud functions. At the same time, we consider the load sustained by the workers and actively seek to avoid imbalance beyond a configurable threshold. Our preliminary evaluation shows that, even with our limited exploration of the configuration space so-far, we can achieve 66% performance improvement at the cost of a (manageable) higher node imbalance.
Security protocols in a nutshell

Security protocols are building blocks in secure communications. Security protocols deploy some security mechanisms to provide certain security services. Security protocols are considered abstract when analyzed. They might involve more vulnerabilities when implemented. This manuscript provides a holistic study on security protocols. It reviews foundations of security protocols, taxonomy of attacks on security protocols and their implementations, and different methods and models for security analysis of protocols. Specifically, it clarifies differences between information-theoretic and computational security, and computational and symbolic models. Furthermore, a survey on computational security models for authenticated key exchange (AKE) and passwordauthenticated key exchange (PAKE) protocols, as the most important and well-studied type of security protocols, is provided.
Ideal carrier profile control for high-speed switching of 1200 V IGBTs

A novel 1200 V Insulated Gate Bipolar Transistor (IGBT) for high-speed switching that combines Shorted Dummy-cell (SD) to control carrier extraction at the emitter side and P/P- collector to reduce hole injection from the backside is proposed. The SD-IGBT with P/P- collector has achieved 37 % reduction of turn-off power dissipation compared with a conventional Floating Dummy-cell (FD) IGBT. The SD-IGBT with P/P- collector also has high turn-off current capability because it extracts carriers uniformly from the dummy-cell. These results show the proposed device has a ideal carrier profile for high-speed switching.