nils-herrmann/clean_scirepeval huggingface dataset:
Attentional convolutional neural networks for object tracking

As low-altitude airspace opens up, aeronautical surveillance based Unmanned Aerial Vehicle (UAV) has started to be widely used in the transportation system. Visual object tracking plays an important role in aeronautical surveillance for its accuracy and timeliness. Although traditional trackers have made great progress, they still tend to fail in complex scenes, such as occlusions, illumination variations, background clutter, and etc. In order to make use of appearance features to distinguish the object and surroundings, we propose a novel architecture called attentional convolutional neural networks (ACNN) in conjunction with offline training and online learning for object tracking. ACNN consists of a trunk equipped with attention blocks that highlight the interesting object, and several branches, which are respectively responsible for specific training sequences. In the tracking stage, all branches are removed and a new fully-connected (fc) layer is added to accomplish binary classification. We regard the candidate with the highest probability as current target. Extensive experimental results on public benchmark show that our method performs outstandingly against state-of-the-art methods. In addition, we have also investigated the relationship between the number of network layers and tracking performance for its practical use.
Full 3D touchless fingerprint recognition: Sensor, database and baseline performance

One of the fields that still today remains largely unexplored in biometrics is 3D fingerprint recognition. This gap is mainly explained by the lack of scanners capable of acquiring on a touchless, fast, reliable and repeatable way, accurate fingerprint 3D spatial models. As such, full 3D fingerprint data with which to produce research and advance this field is almost nonexistent. If such acquisition process was possible, it could represent the beginning of a real paradigm shift in the way fingerprint recognition is performed. The present paper is a first promising step to address the fascinating challenge of 3D fingerprint acquisition and recognition. It presents a new full 3D touchless fingerprint scanner, a new database with 1,000 3D finger-print models, a new segmentation method based on the additional spatial information provided by the models, and initial baseline verification results.
Network Congestion Control - Managing Internet Traffic

Want to learn more about the background and concepts of Internet congestion control? This indispensable text draws a sketch of the future in an easily comprehensible fashion. Special attention is placed on explaining the how and why of congestion control mechanisms complex issues so far hardly understood outside the congestion control research community. A chapter on Internet Traffic Management from the perspective of an Internet Service Provider demonstrates how the theory of congestion control impacts on the practicalities of service delivery.
Tackling Low Resolution for Better Scene Understanding

Complete scene understanding has been an aspiration of computer vision since its very early days. It has applications in autonomous navigation, aerial imaging, surveillance, human-computer interaction among several other active areas of research. While many methods since the advent of deep learning have taken performance in several scene understanding tasks to respectable levels, the tasks are far from being solved. One problem that plagues scene understanding is low-resolution. Convolutional Neural Networks that achieve impressive results on high resolution struggle when confronted with low resolution because of the inability to learn hierarchical features and weakening of signal with depth. In this thesis, we study the low resolution and suggest approaches that can overcome its consequences on three popular tasks object detection, in-the-wild face recognition, and semantic segmentation. The popular object detectors were designed for, trained, and benchmarked on datasets that have a strong bias towards medium and large sized objects. When these methods are finetuned and tested on a dataset of small objects, they perform miserably. The most successful detection algorithms follow a two-stage pipeline: the first which quickly generates regions of interest that are likely to contain the object and the second, which classifies these proposal regions. We aim to adapt both these stages for the case of small objects; the first by modifying anchor box generation based on theoretical considerations, and the second using a simple-yet-effective super-resolution step. Motivated by the success of being able to detect small objects, we study the problem of detecting and recognising objects with huge variations in resolution, in the problem of face recognition in semistructured scenes. Semi-structured scenes like social settings are more challenging than regular ones: there are several more faces of vastly different scales, there are large variations in illumination, pose and expression, and the existing datasets do not capture these variations. We address the unique challenges in this setting by (i) benchmarking popular methods for the problem of face detection, and (ii) proposing a method based on resolution-specific networks to handle different scales. Semantic segmentation is a more challenging localisation task where the goal is to assign a semantic class label to every pixel in the image. Solving such a problem is crucial for self-driving cars where we need sharper boundaries for roads, obstacles and paraphernalia. For want of a higher receptive field and a more global view of the image, CNN networks forgo resolution. This results in poor segmentation of complex boundaries, small and thin objects. We propose prefixing a super-resolution step before semantic segmentation. Through experiments, we show that a performance boost can be obtained on the popular streetview segmentation dataset, CityScapes.
GENETIC ALGORITHM-BASED PROPORTIONAL DERIVATIVE CONTROLLER FOR THE DEVELOPMENT OF ACTIVE SUSPENSION SYSTEM

Suspension system design plays an important role in improving passenger ride comfort. Traditionally automotive suspension designs have been a compromise between the three conflicting criteria of passenger comfort, suspension travel and road holding ability. Demands for better ride comfort and controllability of road vehicles like passenger cars have motivated to develop new type of suspension systems like active suspension. Ride comfort is directly related to the acceleration sensed by passengers when traveling on a rough road. Road holding ability is associated with the contact forces of the tires and the road surface. These contact forces are assumed to depend linearly on the tire deflection. Suspension travel refers to the relative displacement between the sprung and the unsprung masses. The present work aims at developing an active suspension for the quarter car model of a passenger car to improve its performance by using a proportional derivative (PD) controller in a closed loop circuit with suspension travel as feedback. In PD controller the proportional term provides inputs that correct for “current” errors. Derivative term provides “anticipation” of upcoming changes. The controller design deals with the selection of the parameters proportional and derivative gain (Kp, Kd). These gain values have been optimized to minimize the sprung mass acceleration using genetic algorithm with the constraints of suspension travel and tire deflection. To verify the performance of active suspension system we simulated the resulting closed loop system for a sinusoidal, step and random road profile using MATLAB and compared the active suspension with passive suspension. The results show that the performance of the active system has improved as both the peak overshoot of sprung mass acceleration, suspension travel and tire deflection have reduced compared to passive system.
Segmentation of Instances by Hashing.

We propose a novel approach to address the Simultaneous Detection and Segmentation problem introduced in . Using the hierarchical structures first presented in  we use an efficient and accurate procedure that exploits the hierarchy feature information using Locality Sensitive Hashing. We build on recent work that utilizes convolutional neural networks to detect bounding boxes in an image (Faster R-CNN [11]) and then use the top similar hierarchical region that best fits each bounding box after hashing, we call this approach HashBox. We then refine our final segmentation results by automatic hierarchy pruning. HashBox introduces a train-free alternative to Hypercolumns [7]. We conduct extensive experiments on Pascal VOC 2012 segmentation dataset, showing that HashBox gives competitive state-of-the-art object segmentations.
Software defined network — Architectures

Software Defined Network (SDN) is the latest network architecture in which the data and control planes do not reside on the same networking element. The control of packet forwarding in this architecture is taken out and is carried out by a programmable software component, the controller, whereas the forwarding elements are only used as packet moving devices that are driven by the controller. SDN architecture also provides Open APIs from both control and data planes. In order to provide communication between the controller and the forwarding hardware among many available protocols, OpenFlow (OF), is generally regarded as a standardized protocol for SDN. Open APIs for communication between the controller and applications enable development of network management applications easy. Therefore, SDN makes it possible to program the network thus provide numerous benefits. As a result, various vendors have developed SDN architectures. This paper summarizes as well as compares most of the common SDN architectures available till date.
Minimizing Computation in Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have been successfully used for many computer vision applications. It would be beneficial to these applications if the computational workload of CNNs could be reduced. In this work we analyze the linear algebraic properties of CNNs and propose an algorithmic modification to reduce their computational workload. An up to a 47% reduction can be achieved without any change in the image recognition results or the addition of any hardware accelerators.
Graph Capsule Convolutional Neural Networks

Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision. In this paper, we expose and tackle some of the basic weaknesses of a GCNN model with a capsule idea presented in (Hinton et al., 2011) and propose our Graph Capsule Network (GCAPS-CNN) model. In addition, we design our GCAPS-CNN model to solve especially graph classification problem which current GCNN models find challenging. Through extensive experiments, we show that our proposed Graph Capsule Network can significantly outperforms both the existing state-of-art deep learning methods and graph kernels on graph classification benchmark datasets.
Design and control of a micro ball-balancing robot (MBBR) with orthogonal midlatitude omniwheel placement

Ball-balancing robots (BBRs) are endowed with rich dynamics. When properly designed and stabilized via feedback to eliminate jitter, and intuitively coordinated with a well-designed smartphone interface, BBRs exhibit a uniquely fluid and organic motion. Unlike mobile inverted pendulums (MIPs, akin to unmanned Segways), BBRs stabilize both fore/aft and left/right motions with feedback, and bank when turning. Previous research on BBRs focused on vehicles from 50cm to 2m in height; the present work is the first to build significantly smaller BBRs, with heights under 25cm. We consider the unique issues arising when miniaturizing a BBR to such a scale, which are characterized by faster time scales and reduced weight (and, thus, reduced normal force and stiction between the omniwheels and the ball). Two key patent-pending aspects of our design are (a) moving the omniwheels to contact the ball down to around 20 to 30 deg N latitude, which increases the normal force between the omniwheels and the ball, and (b) orienting the omniwheels into mutually-orthogonal planes, which improves efficiency. Design iterations were facilitated by rapid prototyping and leveraged low-cost manufacturing principles and inexpensive components. Classical successive loop closure control strategies are implemented, which prove to be remarkably effective when the BBR isn't spinning quickly, and thus the left/right and fore/aft stabilization problems decompose into two decoupled MIP problems.
Analysis of graphene based triangular nano patch antenna using photonic crystal as substrate for wireless applications

The goal of this paper is to explore the effects of two dimensional photonic band gap (PBG) crystals substrate on the performance of a graphene based triangular nano patch antenna in terahertz region (1-3 THz). Graphene as patch conductor on photonic crystal has been used as substrate material for high bandwidth, radiation efficiency and highly directional resonant antenna. Good impedance matching is achieved at the resonating frequencies with 5.6% bandwidth. The radiation characteristics including radiation efficiency and absorption cross section show significant improve on account of using PBG crystals substrate for graphene based patch antenna. The simulation has been performed using High Frequency Simulator Software (HFSS), which is a commercially available electromagnetic simulator based on finite element method technique.
Outpatient Therapeutic Feeding Program Outcomes and Determinants in Treatment of Severe Acute Malnutrition in Tigray, Northern Ethiopia: A Retrospective Cohort Study

Outpatient Therapeutic feeding Program (OTP) brings the services for management of Severe Acute Malnutrition (SAM) closer to the community by making services available at decentralized treatment points within the primary health care settings, through the use of ready-to-use therapeutic foods, community outreach and mobilization. Little is known about the program outcomes. This study revealed the levels of program outcome indictors and determinant factors to recovery rate.



A retrospective cohort study was conducted on 628 children who had been managed for SAM under OTP from April/2008 to January/2012. The children were selected using systematic random sampling from 12 health posts and 4 health centers. The study relied on information of demographic characteristics, anthropometries, Plumpy'Nut, medical problems and routine medications intakes. The results were estimated using Kaplan-Meier survival curves, log-rank test and Cox-regression.



The recovery, defaulter, mortality and weight gain rates were 61.78%, 13.85%, 3.02% and 5.23 gm/kg/day, respectively. Routine medications were administered partially and children with medical problems were managed inappropriately under the program. As a child consumed one more sachet of Plumpy'Nut, the recovery rate from SAM increased by 4% (HR = 1.04, 95%-CI = 1.03, 1.05, P<0.001). The adjusted hazard ratios to recovery of children with diarrhea, appetite loss with Plumpy'Nut and failure to gain weight were 2.20 (HR = 2.20, 95%-CI = 1.31, 3.41, P = 0.001), 4.49 (HR = 1.74, 95%-CI = 1.07, 2.83, P = 0.046) and 3.88 (HR = 1.95, 95%-CI = 1.17, 3.23, P<0.001), respectively. Children who took amoxicillin and de-worming had 95% (HR = 1.95, 95%-CI = 1.17, 3.23) and 74% (HR = 1.74, 95%-CI = 1.07, 2.83) more probability to recover from SAM as compared to those who didn't take them.



The OTP was partially successful. Management of children with comorbidities under the program and partial administration of routine drugs were major threats for the program effectiveness. The stakeholders should focus on creating the capacity of the OTP providers on proper management of SAM to achieve fully effective program.
Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system

This paper provides an analysis of human mobility data in an urban area using the amount of available bikes in the stations of the community bicycle program Bicing in Barcelona. Based on data sampled from the operator’s website, it is possible to detect temporal and geographic mobility patterns within the city. These patterns are applied to predict the number of available bikes for any station someminutes/hours ahead. The predictions could be used to improve the bicycle programand the information given to the users via the Bicing website.
An efficient FPGA-based database processor for fast database analytics

Recent years have witnessed a massive growth of global data due to the ubiquitous internet-of-thing products, social networking services, and mobile devices. Fast database analytics, therefore, has been increasingly attractive to numerous research. In this paper, a low-latency FPGA-based Database Processor (DBP) using bitmap index is proposed. By exploiting available embedded memory blocks and logic elements, a 50-MHz DBP is capable of performing 1,024 queries for entire 32,768 4-KB records within around 3.31 ms. In other words, the DBP can analyze a capacity data of nearly 37.76 GB per second.
Faster Neural Network Training with Approximate Tensor Operations

We propose a novel technique for faster Neural Network (NN) training by systematically approximating all the constituent matrix multiplications and convolutions. This approach is complementary to other approximation techniques, requires no changes to the dimensions of the network layers, hence compatible with existing training frameworks. We first analyze the applicability of the existing methods for approximating matrix multiplication to NN training, and extend the most suitable column-row sampling algorithm to approximating multi-channel convolutions. We apply approximate tensor operations to training MLP, CNN and LSTM network architectures on MNIST, CIFAR-100 and Penn Tree Bank datasets and demonstrate 30%-80% reduction in the amount of computations while maintaining little or no impact on the test accuracy. Our promising results encourage further study of general methods for approximating tensor operations and their application to NN training.
The Stixel world: A medium-level representation of traffic scenes

Recent progress in advanced driver assistance systems and the race towards autonomous vehicles is mainly driven by two factors: (1) increasingly sophisticated algorithms that interpret the environment around the vehicle and react accordingly, and (2) the continuous improvements of sensor technology itself. In terms of cameras, these improvements typically include higher spatial resolution, which as a consequence requires more data to be processed. The trend to add multiple cameras to cover the entire surrounding of the vehicle is not conducive in that matter. At the same time, an increasing number of special purpose algorithms need access to the sensor input data to correctly interpret the various complex situations that can occur, particularly in urban traffic. By observing those trends, it becomes clear that a key challenge for vision architectures in intelligent vehicles is to share computational resources. We believe this challenge should be faced by introducing a representation of the sensory data that provides compressed and structured access to all relevant visual content of the scene. The Stixel World discussed in this paper is such a representation. It is a medium-level model of the environment that is specifically designed to compress information about obstacles by leveraging the typical layout of outdoor traffic scenes. It has proven useful for a multi∗Corresponding author: marius.cordts@daimler.com Authors contributed equally and are listed in alphabetical order Preprint submitted to Image and Vision Computing February 14, 2017 tude of automotive vision applications, including object detection, tracking, segmentation, and mapping. In this paper, we summarize the ideas behind the model and generalize it to take into account multiple dense input streams: the image itself, stereo depth maps, and semantic class probability maps that can be generated, e.g ., by deep convolutional neural networks. Our generalization is embedded into a novel mathematical formulation for the Stixel model. We further sketch how the free parameters of the model can be learned using structured SVMs.
Organizational Sustainability : A New Project Portfolio Management Approach that Integrates Financial and Non-Financial Performance Measures

This paper presents preliminary research on a new decision making tool that integrates financial and non-financial performance measures in project portfolio management via the Triple Bottom Line (TBL) and uses the Analytic Hierarchy Process (AHP) as a decision support model. This new tool evaluates and prioritizes a set of projects and creates a balanced project portfolio based upon the perspectives and priorities of decision makers. It can assist decision makers with developing and making proactive decisions which support the strategy of their organization with respect to financial, environmental and social issues, ensuring the sustainability of their organization in the future.
Emotions evoked by the sound of music: characterization, classification, and measurement.

One reason for the universal appeal of music lies in the emotional rewards that music offers to its listeners. But what makes these rewards so special? The authors addressed this question by progressively characterizing music-induced emotions in 4 interrelated studies. Studies 1 and 2 (n=354) were conducted to compile a list of music-relevant emotion terms and to study the frequency of both felt and perceived emotions across 5 groups of listeners with distinct music preferences. Emotional responses varied greatly according to musical genre and type of response (felt vs. perceived). Study 3 (n=801)--a field study carried out during a music festival--examined the structure of music-induced emotions via confirmatory factor analysis of emotion ratings, resulting in a 9-factorial model of music-induced emotions. Study 4 (n=238) replicated this model and found that it accounted for music-elicited emotions better than the basic emotion and dimensional emotion models. A domain-specific device to measure musically induced emotions is introduced--the Geneva Emotional Music Scale.
Phrase Dependency Parsing for Opinion Mining

In this paper, we present a novel approach for mining opinions from product reviews, where it converts opinion mining task to identify product features, expressions of opinions and relations between them. By taking advantage of the observation that a lot of product features are phrases, a concept of phrase dependency parsing is introduced, which extends traditional dependency parsing to phrase level. This concept is then implemented for extracting relations between product features and expressions of opinions. Experimental evaluations show that the mining task can benefit from phrase dependency parsing.
Neuronal 5-HT Receptors and SERT

5-hydroxytryptamine (5-HT, serotonin) is an ancient biochemical manipulated through evolution to be utilized extensively throughout the animal and plant kingdoms. Mammals employ 5-HT as a neurotransmitter within the central and peripheral nervous systems, and also as a local hormone in numerous other tissues, including the gastrointestinal tract, the cardiovascular system and immune cells. This multiplicity of function implicates 5-HT in a vast array of physiological and pathological processes. This plethora of roles has consequently encouraged the development of many compounds of therapeutic value, including various antidepressant, antipsychotic and antiemetic drugs.