nils-herrmann/clean_scirepeval huggingface dataset:
Good Features to Correlate for Visual Tracking

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in this family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize the robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks, which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.
Estimating 3D Egomotion from Perspective Image Sequence

This paper deals with the computation of sensor motion from sets of displacement vectors obtained from consecutive pairs of images. The problem is investigated with emphasis on its application to autonomous robots and land vehicles. First, the effects of 3-D camera rotation and translation upon the observed image are discussed and in particular the concept of the focus of expansion (FOE). It is shown that locating the FOE precisely is difficult when displacement vectors are corrupted by noise and errors. A more robust performance can be achieved by computing a 2-D region of possible FOE-locations (termed the fuzzy FOE) instead of looking for a single-point FOE. The shape of this FOE-region is an explicit indicator for the accuracy of the result. It has been shown elsewhere that given the fuzzy FOE, a number of powerful inferences about the 3-D scene structure and motion become possible. This paper concentrates on the aspects of computing the fuzzy FOE and shows the performance of a particular algorithm on real motion sequences taken from a moving autonomous land vehicle. Zndex Tenns-Autonomous mobile robot, dynamic scene analysis, fuzzy focus of expansion, motion analysis, passive navigation, sensor motion estimation.
Real-time randomized path planning for robot navigation

Mobile robots often find themselves in a situation where they must find a trajectory to another position in their environment, subject to constraints posed by obstacles and the capabilities of the robot itself. This is the problem of planning a path through a continuous domain, for which several approaches have been developed. Each has some limitations however, including requiring state discretizations, steep efficiency vs. accuracy tradeoffs, or the difficulty of adding interleaved execution. Rapidly-Exploring Random Trees (RRTs) are a recently developed representation on which fast continuous domain path planners can be based. In this work, we build a path planning system based on RRTs that interleaves planning and execution, first evaluating it in simulation and then applying it to physical robots. Our planning algorithm, ERRT (execution extended RRT), introduces two novel extensions of previous RRT work, the waypoint cache and adaptive cost penalty search, which improve replanning efficiency and the quality of generated paths. ERRT is successfully applied to a real-time multi-robot system. Results demonstrate that ERRT is significantly more efficient for replanning than a basic RRT planner, performing competitively with or better than existing heuristic and reactive real-time path planning approaches. ERRT is a significant step forward with the potential for making path planning common on real robots, even in challenging continuous, highly dynamic domains.
Lightweight Risk Management in Agile Projects

Risk management in software engineering has become a recognized project management practice but it seems that not all companies are systematically applying it. At the same time, agile methods have become popular, partly because proponents claim that agile methods implicitly reduce risks due to for example, more frequent and earlier feedback, shorter periods of development time and easier prediction of cost. Therefore, there is a need to investigate how risk management can be usable in iterative and evolutionary software development processes. This paper investigates the gathering of empirical data on risk management from the project environment and presents a novel approach to manage risk in agile projects. Our approach is based on a prototype tool, Agile Risk Tool (ART). This tool reduces human effort in risk management by using software agents to identify, assess and monitor risk, based on input and data collected from the project environment and by applying some designated rules. As validation, groups of student project data were used to provide evidence of the efficacy of this approach. We demonstrate the approach and the feasibility of using a lightweight risk management tool to alert, assess and monitor risk with reduced human effort. Keywordssoftware risk, risk management, agile projects.
UCM-2: a Rule-Based Approach to Infer the Scope of Negation via Dependency Parsing

UCM-2 infers the words that are affected by negations by browsing dependency syntactic structures. It first makes use of an algorithm that detects negation cues, like no, not or nothing, and the words affected by them by traversing Minipar dependency structures. Second, the scope of these negation cues is computed by using a post-processing rulebased approach that takes into account the information provided by the first algorithm and simple linguistic clause boundaries.
Towards the Limit of Network Quantization

Network quantization is one of network compression techniq ues employed to reduce the redundancy of deep neural networks. It compresses t he size of the storage for a large number of network parameters in a neural network b y quantizing them and encoding the quantized values into binary codewords of s maller sizes. In this paper, we aim to design network quantization schemes that mi nimize the expected loss due to quantization while maximizing the compression r atio. To this end, we analyze the quantitative relation of quantization errors t the loss function of a neural network and identify that the Hessian-weighted dist ortion measure is locally the right objective function that we need to optimize f or minimizing the loss due to quantization. As a result, Hessian-weighted k-means clustering is proposed for clustering network parameters to quantize when fixed-le ngth binary encoding follows. Hessian-weighting properly handles the differen t impact of quantization errors not only within layers but also across layers and thus it can be employed for quantizing all layers of a network together at once; it is beneficial since one can avoid layer-by-layer compression rate optimization. W hen optimal variablelength binary codes, e.g., Huffman codes, are employed for f urther compression of quantized values after clustering, we derive that the net work quantization problem can be related to the entropy-constrained scalar quanti zation (ECSQ) problem in information theory and consequently propose two solutio ns f ECSQ for network quantization, i.e., uniform quantization and an itera ive algorithm similar to Lloyd’s algorithm for k-means clustering. Finally, usin g the simple uniform quantization followed by Huffman coding, our experiment re sults show that the compression ratios of 51.25, 22.17 and 40.65 are achievable (i.e., the sizes of the compressed models are 1.95%, 4.51% and 2.46% of the original model sizes) for LeNet, ResNet and AlexNet, respectively, at no or marginal p erformance loss.
From Skimming to Reading: A Two-stage Examination Model for Web Search

User's examination of search results is a key concept involved in all the click models. However, most studies assumed that eye fixation means examination and no further study has been carried out to better understand user's examination behavior. In this study, we design an experimental search engine to collect both the user's feedback on their examinations and the eye-tracking/click-through data. To our surprise, a large proportion (45.8%) of the results fixated by users are not recognized as being "read". Looking into the tracking data, we found that before the user actually "reads" the result, there is often a "skimming" step in which the user quickly looks at the result without reading it. We thus propose a two-stage examination model which composes of a first "from skimming to reading" stage (Stage 1) and a second "from reading to clicking" stage (Stage 2). We found that the biases (e.g. position bias, domain bias, attractiveness bias) considered in many studies impact in different ways in Stage 1 and Stage 2, which suggests that users make judgments according to different signals in different stages. We also show that the two-stage examination behaviors can be predicted with mouse movement behavior, which can be collected at large scale. Relevance estimation with the two-stage examination model also outperforms that with a single-stage examination model. This study shows that the user's examination of search results is a complex cognitive process that needs to be investigated in greater depth and this may have a significant impact on Web search.
Universals and cultural differences in the judgments of facial expressions of emotion.

We present here new evidence of cross-cultural agreement in the judgement of facial expression. Subjects in 10 cultures performed a more complex judgment task than has been used in previous cross-cultural studies. Instead of limiting the subjects to selecting only one emotion term for each expression, this task allowed them to indicate that multiple emotions were evident and the intensity of each emotion. Agreement was very high across cultures about which emotion was the most intense. The 10 cultures also agreed about the second most intense emotion signaled by an expression and about the relative intensity among expressions of the same emotion. However, cultural differences were found in judgments of the absolute level of emotional intensity.
60 GHz system-on-package antenna array with parasitic microstrip antenna single element

In this paper microstrip antenna for 60 GHz on LTCC is presented. Special techniques are used to satisfy the antenna specification in wide bandwidth (7 GHz, from 57 GHz to 64 GHz) and high gain (15 dBi). An increase in bandwidth is achieved by using aperture the coupling and the multilayer structure. Gain is increased by using parasitic patches which increase the total antenna aperture. In paper was presented 60 GHz parasitic microstrip antenna with two layers of parasitic patches. High gain is obtained as the result of the optimization. However, presented return loss results can not be treated as wide bandwidth.
Tracking football player movement from a single moving camera using particle filters

This paper deals with the problem of tracking football players in a football match using data from a single moving camera. Tracking footballers from a single video source is di cult: not only do the football players occlude each other, but they frequently enter and leave the camera's eld of view, making initialisation and destruction of a player's tracking a di cult task. The system presented here uses particle lters to track players. The multiple state estimates used by a particle lter provide an elegant method for maintaining tracking of players following an occlusion. Automated tracking can be achieved by creating and stopping particle lters depending on the input player data.
Quaddirectional 2D-Recurrent Neural Networks For Image Labeling

We adopt Convolutional Neural Networks (CNN) to learn discriminative features for local patch classification. We further introduce quaddirectional 2D Recurrent Neural Networks to model the long range dependencies among pixels. Our quaddirectional 2D-RNN is able to embed the global image context into the compact local representation, which significantly enhance their discriminative power. Our experiments demonstrate that the integration of CNN and quaddirectional 2D-RNN achieves very promising results which are comparable to state-of-the-art on real-world image labeling benchmarks.
Phishing Detection: A Literature Survey

This article surveys the literature on the detection of phishing attacks. Phishing attacks target vulnerabilities that exist in systems due to the human factor. Many cyber attacks are spread via mechanisms that exploit weaknesses found in end-users, which makes users the weakest element in the security chain. The phishing problem is broad and no single silver-bullet solution exists to mitigate all the vulnerabilities effectively, thus multiple techniques are often implemented to mitigate specific attacks. This paper aims at surveying many of the recently proposed phishing mitigation techniques. A high-level overview of various categories of phishing mitigation techniques is also presented, such as: detection, offensive defense, correction, and prevention, which we belief is critical to present where the phishing detection techniques fit in the overall mitigation process.
DroidPro: An AOTC-Based Bytecode-Hiding Scheme for Packing the Android Applications

Android is an open source mobile operating system represented by the Open Handset Alliance (OHA), developed by Google and other organizations since 2007, which has taken up most of the market share of smart devices. However, the applications on the platform are facing the increasingly serious security threat. Although the Android system itself provides a set of security mechanism to protect the safety of the system and applications, there are still many security risks. In order to hide the vulnerability of the applications and prevent the malicious users from tampering the apps, multiple anti-analysis methods have been applied by many Android packers to consolidate the apps. Bytecode-hiding is one of the most effective anti-analysis method, which can extract some bytecode from the Dex files and hide them from the vision of malicious analysts. Mostly, the hidden bytecode was encrypted, which can be recovered in runtime. But the conventional bytecode-hiding methods are always low-efficient and unsafe on some occasions, where the hidden bytecode can be recovered by the malicious analysts in some way. In this paper, we propose a bytecode-hiding scheme based on Ahead-Of-Time (AOT) compilation, called DroidPro, which can compile some chosen bytecode of Dex files of apps to native code in ahead-of-time that will be much harder to reverse. In our experiments, the apps packed by the packer associated with our bytecode-hiding scheme are more efficient and safer than other packers that use other bytecode-hiding schemes.
Seeing Stars When There Aren’t Many Stars: Graph-Based Semi-Supervised Learning For Sentiment Categorization

We present a graph-based semi-supervised learning algorithm to address the sentiment analysis task of rating inference. Given a set of documents (e.g., movie reviews) and accompanying ratings (e.g., “4 stars”), the task calls for inferring numerical ratings for unlabeled documents based on the perceived sentiment expressed by their text. In particular, we are interested in the situation where labeled data is scarce. We place this task in the semi-supervised setting and demonstrate that considering unlabeled reviews in the learning process can improve ratinginference performance. We do so by creating a graph on both labeled and unlabeled data to encode certain assumptions for this task. We then solve an optimization problem to obtain a smooth rating function over the whole graph. When only limited labeled data is available, this method achieves significantly better predictive accuracy over other methods that ignore the unlabeled examples during training.
Edina: Building an Open Domain Socialbot with Self-dialogues

We present Edina, the University of Edinburgh’s social bot for the Amazon Alexa Prize competition. Edina is a conversational agent whose responses utilize data harvested from Amazon Mechanical Turk (AMT) through an innovative new technique we call self-dialogues. These are conversations in which a single AMT Worker plays both participants in a dialogue. Such dialogues are surprisingly natural, efficient to collect and reflective of relevant and/or trending topics. These self-dialogues provide training data for a generative neural network as well as a basis for soft rules used by a matching score component. Each match of a soft rule against a user utterance is associated with a confidence score which we show is strongly indicative of reply quality, allowing this component to self-censor and be effectively integrated with other components. Edina’s full architecture features a rule-based system backing off to a matching score, backing off to a generative neural network. Our hybrid data-driven methodology thus addresses both coverage limitations of a strictly rule-based approach and the lack of guarantees of a strictly machine-learning approach.
A broadband dual-polarized magneto-electric dipole antenna

A novel wideband dual-polarized magneto-electric dipole unidirectional antenna is proposed in this paper. The antenna is composed of dual-layer magneto-electric (ME)-dipole and two Γ-shaped probes. Its two ports both achieve a common impedance bandwidth of 91.6%(SWR<2). The antenna has excellent performance in isolation, which is more than 15dB, and a high front-to-back ration more than 14.8dB over the operating band.
A survey of Ambient Assisted Living systems: Challenges and opportunities

As the research in Ambient Assisted Living (AAL) matures, we expect that data generated from AAL IoT devices will benefit from analysis by well established machine learning techniques. There is also potential that new research in ML and Artificial Intelligence (AI) can be used on data generated from the sensors used in AAL. In this paper we present a survey of the research in the related topics, identify its shortcomings and propose future work that will integrate these fields by collecting ambient sensor data and process the data by ML framework which can detect and classify activities.
Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning

Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position data when GPS errors occur. To mitigate GPS errors, numerous Bayesian filters based on sensor fusion algorithms have been studied. The estimation performance of Bayesian filters primarily relies on the choice of process model. For this reason, the change in vehicle dynamics with driving conditions should be addressed in the process model of the Bayesian filters. This paper presents a positioning algorithm based on an interacting multiple model (IMM) filter that integrates low-cost GPS and in-vehicle sensors to adapt the vehicle model to various driving conditions. The model set of the IMM filter is composed of a kinematic vehicle model and a dynamic vehicle model. The algorithm developed in this paper is verified via intensive simulation and evaluated through experimentation with a real-time embedded system. Experimental results show that the performance of the positioning system is accurate and reliable under a wide range of driving conditions.
E-Readers and Visual Fatigue

The mass digitization of books is changing the way information is created, disseminated and displayed. Electronic book readers (e-readers) generally refer to two main display technologies: the electronic ink (E-ink) and the liquid crystal display (LCD). Both technologies have advantages and disadvantages, but the question whether one or the other triggers less visual fatigue is still open. The aim of the present research was to study the effects of the display technology on visual fatigue. To this end, participants performed a longitudinal study in which two last generation e-readers (LCD, E-ink) and paper book were tested in three different prolonged reading sessions separated by--on average--ten days. Results from both objective (Blinks per second) and subjective (Visual Fatigue Scale) measures suggested that reading on the LCD (Kindle Fire HD) triggers higher visual fatigue with respect to both the E-ink (Kindle Paperwhite) and the paper book. The absence of differences between E-ink and paper suggests that, concerning visual fatigue, the E-ink is indeed very similar to the paper.
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal

Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data. The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of travel, through buildings and outdoor regions that collectively exhibit large variations in visual appearance, self-similarity, and connectivity. We compare pretrained visual encoders that enable precomputation of visual embeddings to achieve a throughput of tens of thousands of transitions per second at training time on a commodity desktop computer, allowing agents to learn from millions of trajectories of experience in a matter of hours. We propose multiple forms of computationally efficient stochastic augmentation to enable the learned policy to generalise beyond these precomputed embeddings, and demonstrate successful deployment of the learned policy on the real robot without fine tuning, despite environmental appearance differences at test time. The dataset and code required to reproduce these results and apply the technique to other datasets and robots is made publicly available at rl-navigation.github.io/deployable.