nils-herrmann/clean_scirepeval huggingface dataset:
Summaries of 107 Computer Vision-Based Human Motion Capture Papers

This technical report contains summaries of 107 papers concerned with computer vision-based human motion capture. The report can be seen as an appendix to a survey, Computer Vision-Based Human Motion Capture A Survey, which I wrote in parallel to this report. The survey gives a taxonomy for the papers described in this report. The summaries are presented in alphabetic order, with respect to the surname of the rst Author. I have tried to give objective summaries of the di erent papers and only give subjective critique in an item, comments, reserved for this purpose. I have also tried to use the same amount of energy and space on the di erent summaries. But large variations between the length of the di erent summaries can be observed. This is mainly due to the length of the papers, but also due to my interest in the individual papers. Preface This technical report contains summaries of 107 papers concerned with computer vision-based human motion capture. The report can be seen as an appendix to a survey, Computer Vision-Based Human Motion Capture A Survey, which I wrote in parallel to this report. This report has been written as a separate report due to its size alone. The writing of the two reports was carried out in the winter 98/99 while the reading of papers was done during the summer and fall of 1998. The work presented is the rst step in my Ph.D.-study titled: "Multiple Cues for Model-Based Human Motion Capture". Whenever I use 'he' throughout the report is should be read as he/she. Aalborg, Denmark, March 1999. Thomas B. Moeslund (tbm@vision.auc.dk)
Anomaly Detection Analysis of Intrusion Data Using Supervised & Unsupervised Approach

Anomaly based network intrusion detection (ANID) is an important problem that has been researched within diverse research areas and various application domains. Several anomaly based network intrusion detection systems (ANIDS) can be found in the literature. Most ANIDSs employ supervised algorithms, whose performances highly depend on attack-free training data. However, this kind of training data is difficult to obtain in real world network environment. Moreover, with changing network environment or services, patterns of normal traffic will be changed. This leads to high false positive rate of supervised ANIDSs. Using unsupervised anomaly detection techniques, however, the system can be trained with unlabeled data and is capable of detecting previously unseen attacks. We have categorized the existing ANIDSs based on its type, class, nature of detection/ processing, level of security, etc. We also enlist some proximity measures for intrusion data analysis and detection. We also report some experimental results for detection of attacks over the KDD’99 dataset.
Learning Monocular Depth by Distilling Cross-Domain Stereo Networks

Monocular depth estimation aims at estimating a pixelwise depth map for a single image, which has wide applications in scene understanding and autonomous driving. Existing supervised and unsupervised methods face great challenges. Supervised methods require large amounts of depth measurement data, which are generally difficult to obtain, while unsupervised methods are usually limited in estimation accuracy. Synthetic data generated by graphics engines provide a possible solution for collecting large amounts of depth data. However, the large domain gaps between synthetic and realistic data make directly training with them challenging. In this paper, we propose to use the stereo matching network as a proxy to learn depth from synthetic data and use predicted stereo disparity maps for supervising the monocular depth estimation network. Cross-domain synthetic data could be fully utilized in this novel framework. Different strategies are proposed to ensure learned depth perception capability well transferred across different domains. Our extensive experiments show state-of-the-art results of monocular depth estimation on KITTI dataset.
Approaches to uncertainty visualization

Visualized data often have dubious origins and quality. Different forms of uncertainty and errors are also introduced as the data are derived, transformed, interpolated, and finally rendered. This paper surveys uncertainty visualization techniques that present data so that users are made aware of the locations and degree of uncertainties in their data. The techniques include adding glyphs, adding geometry, modifying geometry, modifying attributes, animation, sonification, and psychovisual approaches. We present our results in uncertainty visualization for environmental visualization, surface interpolation, global illumination with radiosity, flow visualization, and figure animation. We also present a classification of the possibilities in uncertainty visualization and locate our contributions within this classification.
Security in Internet of Things: issues, challenges, taxonomy, and architecture

Internet technology is very pervasive today. The number of devices connected to the Internet, thosewith a digital identity, is increasing day by day. With the developments in the technology, Internet of Things (IoT) become important part of human life. However, it is not well defined and secure. Now, various security issues are considered as major problem for a full-fledged IoT environment. There exists a lot of security challenges with the proposed architectures and the technologies which make the backbone of the Internet of Things. Some efficient and promising security mechanisms have been developed to secure the IoT environment, however, there is a lot to do. The challenges are ever increasing and the solutions have to be ever improving. Therefore, aim of this paper is to discuss the history, background, statistics of IoT and security based analysis of IoT architecture. In addition, we will provide taxonomy of security challenges in IoT environment and taxonomy of various defense mechanisms. We conclude our paper discussing various research challenges that still exist in the literature, which provides better understanding of the problem, current solution space, and future research directions to defend IoT against different attacks.
HULL FORM PARAMETERIZATION TECHNIQUE WITH LOCAL AND GLOBAL OPTIMIZATION ALGORITHMS Professor

The paper is presented on the development of a designer friendly hull form parameterization and its coupling with a local and a global optimization algorithm: the well known Sequential Quadratic Programming (SQP) and the more recent evolutionary Particle Swarm Optimization (PSO). These two algorithms are representative of classes with rather opposite characteristics (derivative–based and derivative–free, respectively) and their relative performances in solving some typical ship design optimization problem will be discussed in the paper. Following a well known naval architect’s design practice, a parametric modification tool is developed for modifying the ship’s geometry. The original geometry can be easily deformed by direct selection of some standard design parameters and useful information about the effect of the changing in the parameters are immediately obtained and visualized. At the same time, design parameters are assumed as design variables in the formulation of the optimization problem. In the examples, both potential flow and RANS solvers have been used. Numerical results for both single and multi-objective problems are presented.
DeeperLab: Single-Shot Image Parser

We present a single-shot, bottom-up approach for whole image parsing. Whole image parsing, also known as Panoptic Segmentation, generalizes the tasks of semantic segmentation for ’stuff’ classes and instance segmentation for ’thing’ classes, assigning both semantic and instance labels to every pixel in an image. Recent approaches to whole image parsing typically employ separate standalone modules for the constituent semantic and instance segmentation tasks and require multiple passes of inference. Instead, the proposed DeeperLab image parser performs whole image parsing with a significantly simpler, fully convolutional approach that jointly addresses the semantic and instance segmentation tasks in a single-shot manner, resulting in a streamlined system that better lends itself to fast processing. For quantitative evaluation, we use both the instance-based Panoptic Quality (PQ) metric and the proposed regionbased Parsing Covering (PC) metric, which better captures the image parsing quality on ’stuff’ classes and larger object instances. We report experimental results on the challenging Mapillary Vistas dataset, in which our single model achieves 31.95% (val) / 31.6% PQ (test) and 55.26% PC (val) with 3 frames per second (fps) on GPU or near realtime speed (22.6 fps on GPU) with reduced accuracy.
Serious Games for Language Learning: How Much Game, How Much AI?

Modern computer games show potential not just for engaging and entertaining users, but also in promoting learning. Game designers employ a range of techniques to promote long-term user engagement and motivation. These techniques are increasingly being employed in so-called serious games, games that have nonentertainment purposes such as education or training. Although such games share the goal of AIED of promoting deep learner engagement with subject matter, the techniques employed are very different. Can AIED technologies complement and enhance serious game design techniques, or does good serious game design render AIED techniques superfluous? This paper explores these questions in the context of the Tactical Language Training System (TLTS), a program that supports rapid acquisition of foreign language and cultural skills. The TLTS combines game design principles and game development tools with learner modelling, pedagogical agents, and pedagogical dramas. Learners carry out missions in a simulated game world, interacting with non-player characters. A virtual aide assists the learners if they run into difficulties, and gives performance feedback in the context of preparatory exercises. Artificial intelligence plays a key role in controlling the behaviour of the non-player characters in the game; intelligent tutoring provides supplementary scaffolding.
Dual-band 8-element MIMO antenna with short neutral line for 5G mobile handset

A design of compact dual band 8-element MIMO antenna using folded monopole structures is proposed. The MIMO antenna consists of 8 folded monopole antenna elements, each two of them are symmetrically placed on the orthogonal frame corners of the substrate, and closely located to each other with no clearance on the ground. The folded monopole arms are used as radiation elements to resonate at two different bands of 3.4–3.6 GHz and 4.55–4.75 GHz. Each closely placed two antenna elements are connected by a 3 mm short neutral line which can be used to reduce the mutual coupling at both bands. By optimizing the antenna structure and neutral line, dual band decoupling can be achieved. The isolation and efficiency of the MIMO antenna is improved and the calculated ergodic channel capacities with a 20 dB SNR at both bands reach to 41.2 bps/Hz and 40.3 bps/Hz, respectively, very close to the ideal case of 8×8 MIMO system.
Breaking the habit: Measuring and predicting departures from routine in individual human mobility

Researchers studying daily life mobility patterns have recently shown that humans are typically highly predictable in their movements. However, no existing work has examined the boundaries of this predictability, where human behaviour transitions temporarily from routine patterns to highly unpredictable states. To address this shortcoming, we tackle two interrelated challenges. First, we develop a novel information-theoretic metric, called instantaneous entropy, to analyse an individual’s mobility patterns and identify temporary departures from routine. Second, to predict such departures in the future, we propose the first Bayesian framework that explicitly models breaks from routine, showing that it outperforms current state-of-the-art predictors.
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations

Training of discrete latent variable models remains challenging because passing gradient information through discrete units is difficult. We propose a new class of smoothing transformations based on a mixture of two overlapping distributions, and show that the proposed transformation can be used for training binary latent models with either directed or undirected priors. We derive a new variational bound to efficiently train with Boltzmann machine priors. Using this bound, we develop DVAE++, a generative model with a global discrete prior and a hierarchy of convolutional continuous variables. Experiments on several benchmarks show that overlapping transformations outperform other recent continuous relaxations of discrete latent variables including Gumbel-Softmax (Maddison et al., 2016; Jang et al., 2016), and discrete variational autoencoders (Rolfe, 2016).
List decoding of polar codes

We describe a successive-cancellation list decoder for polar codes, which is a generalization of the classic successive-cancellation decoder of Arikan. In the proposed list decoder, up to L decoding paths are considered concurrently at each decoding stage. Simulation results show that the resulting performance is very close to that of a maximum-likelihood decoder, even for moderate values of L. Thus it appears that the proposed list decoder bridges the gap between successive-cancellation and maximum-likelihood decoding of polar codes. The specific list-decoding algorithm that achieves this performance doubles the number of decoding paths at each decoding step, and then uses a pruning procedure to discard all but the L “best” paths. In order to implement this algorithm, we introduce a natural pruning criterion that can be easily evaluated. Nevertheless, straightforward implementation still requires O(L · n2) time, which is in stark contrast with the O(n log n) complexity of the original successive-cancellation decoder. We utilize the structure of polar codes to overcome this problem. Specifically, we devise an efficient, numerically stable, implementation taking only O(L · n log n) time and O(L · n) space.
FBI-Pose: Towards Bridging the Gap between 2D Images and 3D Human Poses using Forward-or-Backward Information

Although significant advances have been made in the area of human poses estimation from images using deep Convolutional Neural Network (ConvNet), it remains a big challenge to perform 3D pose inference in-the-wild. This is due to the difficulty to obtain 3D pose groundtruth for outdoor environments. In this paper, we propose a novel framework to tackle this problem by exploiting the information of each bone indicating if it is forward or backward with respect to the view of the camera(we term it Forwardor-Backward Information abbreviated as FBI). Our method firstly trains a ConvNet with two branches which maps an image of a human to both the 2D joint locations and the FBI of bones. These information is further fed into a deep regression network to predict the 3D positions of joints. To support the training, we also develop an annotation user interface and labeled such FBI for around 12K in-the-wild images which are randomly selected from MPII (a public dataset of 2D pose annotation). Our experimental results on the standard benchmarks demonstrate that our approach outperforms state-of-the-art methods both qualitatively and quantitatively.
Speech Recognition using Hidden Markov Model

Speech technology and systems in human computer interaction have witnessed a steady and important advancement over last two decades. Today, speech technologies are commercially available for boundless but interesting range of tasks. These technologies permit machines to respond correctly and consistently to human voices, and provide useful and valuable services. In the present era, mainly Hidden Markov Model (HMMs) based speech recognizers are used. This paper aims to present a speech recognition system using Hidden Markov Model. Hidden Markov Model Toolkit (HTK) is used to develop the system. It is used to recognize the isolated words using acoustic word model.
Histogram of oriented gradients for human detection in video

Currently, Computer Vision (CV) is one of the most popular research topics in the world. This is because it can support the human daily life. Moreover, CV can also apply to various theories and researches. Human Detection is one of the most popular research topics in Computer Vision. In this paper, we present a study of technique for human detection from video, which is the Histograms of Oriented Gradients or HOG by developing a piece of application to import and detect the human from the video. We use the HOG Algorithm to analyze every frame from the video to find and count people. After analyzing video from starting to the end, the program generate histogram to show the number of detected people versus playing period of the video. As a result, the expected results are obtained, including the detection of people in the video and the histogram generation to show the appearance of human detected in the video file.
Too much of a good thing? Cardiac vagal tone's nonlinear relationship with well-being.

Parasympathetic regulation of heart rate through the vagus nerve--often measured as resting respiratory sinus arrhythmia or cardiac vagal tone (CVT)--is a key biological correlate of psychological well-being. However, recent theorizing has suggested that many biological and psychological processes can become maladaptive when they reach extreme levels. This raises the possibility that CVT might not have an unmitigated positive relationship with well-being. In line with this reasoning, across 231 adult participants (Mage = 40.02 years; 52% female), we found that CVT was quadratically related to multiple measures of well-being, including life satisfaction and depressive symptoms. Individuals with moderate CVT had higher well-being than those with low or high CVT. These results provide the first direct evidence of a nonlinear relationship between CVT and well-being, adding to a growing body of research that has suggested some biological processes may cease being adaptive when they reach extreme levels.
AMEE Guide no. 34: Teaching in the clinical environment.

Teaching in the clinical environment is a demanding, complex and often frustrating task, a task many clinicians assume without adequate preparation or orientation. Twelve roles have previously been described for medical teachers, grouped into six major tasks: (1) the information provider; (2) the role model; (3) the facilitator; (4) the assessor; (5) the curriculum and course planner; and (6) the resource material creator (Harden & Crosby 2000). It is clear that many of these roles require a teacher to be more than a medical expert. In a pure educational setting, teachers may have limited roles, but the clinical teacher often plays many roles simultaneously, switching from one role to another during the same encounter. The large majority of clinical teachers around the world have received rigorous training in medical knowledge and skills but little to none in teaching. As physicians become ever busier in their own clinical practice, being effective teachers becomes more challenging in the context of expanding clinical responsibilities and shrinking time for teaching (Prideaux et al. 2000). Clinicians on the frontline are often unaware of educational mandates from licensing and accreditation bodies as well as medical schools and postgraduate training programmes and this has major implications for staff training. Institutions need to provide necessary orientation and training for their clinical teachers. This Guide looks at the many challenges for teachers in the clinical environment, application of relevant educational theories to the clinical context and practical teaching tips for clinical teachers. This guide will concentrate on the hospital setting as teaching within the community is the subject of another AMEE guide.
Open Information Extraction

Open information extraction (Open IE) systems aim to obtain relation tuples with highly scalable extraction in portable across domain by identifying a variety of relation phrases and their arguments in arbitrary sentences. The first generation of Open IE learns linear chain models based on unlexicalized features such as Part-of-Speech (POS) or shallow tags to label the intermediate words between pair of potential arguments for identifying extractable relations. Open IE currently is developed in the second generation that is able to extract instances of the most frequently observed relation types such as Verb, Noun and Prep, Verb and Prep, and Infinitive with deep linguistic analysis. They expose simple yet principled ways in which verbs express relationships in linguistics such as verb phrase-based extraction or clause-based extraction. They obtain a significantly higher performance over previous systems in the first generation. In this paper, we describe an overview of two Open IE generations including strengths, weaknesses and application areas.
Internet of Things in Precision Agriculture using Wireless Sensor Networks

The Internet of Things (IoT), the idea of getting real-world objects connected with each other, will change the way users organize, obtain and consume information radically. Internet of Things (IoT) enables various applications (crop growth monitoring and selection, irrigation decision support, etc.) in Digital Agriculture domain. The Wireless Sensors Network (WSN) is widely used to build decision support systems. These systems overcomes many problems in the real-world. One of the most interesting fields having an increasing need of decision support systems is Precision Agriculture (PA). Through sensor networks, agriculture can be connected to the IoT, which allows us to create connections among agronomists, farmers and crops regardless of their geographical differences. With the help of this approach which provides real-time information about the lands and crops that will help farmers make right decisions. The major advantage is implementation of WSN in Precision Agriculture (PA) will optimize the usage of water fertilizers while maximizing the yield of the crops and also will help in analyzing the weather conditions of the field.
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks

With the proliferation of mobile devices and wireless technologies, mobile social network systems are increasingly available. A mobile social network plays an essential role as the spread of information and influence in the form of "word-of-mouth". It is a fundamental issue to find a subset of influential individuals in a mobile social network such that targeting them initially (e.g. to adopt a new product) will maximize the spread of the influence (further adoptions of the new product). The problem of finding the most influential nodes is unfortunately NP-hard. It has been shown that a Greedy algorithm with provable approximation guarantees can give good approximation; However, it is computationally expensive, if not prohibitive, to run the greedy algorithm on a large mobile network.
 In this paper we propose a new algorithm called Community-based Greedy algorithm for mining top-K influential nodes. The proposed algorithm encompasses two components: 1) an algorithm for detecting communities in a social network by taking into account information diffusion; and 2) a dynamic programming algorithm for selecting communities to find influential nodes. We also provide provable approximation guarantees for our algorithm. Empirical studies on a large real-world mobile social network show that our algorithm is more than an order of magnitudes faster than the state-of-the-art Greedy algorithm for finding top-K influential nodes and the error of our approximate algorithm is small.