Reviewerly/exHarmony huggingface dataset:
On metrics for measuring scanpath similarity

Saliency and visual attention have been studied in a computational context for decades, mostly in the capacity of predicting spatial topographical saliency maps or simulated heatmaps. Spatial selection by an attentive mechanism is, however, inherently a sequential sampling process in humans. There have been recent efforts in analyzing and modeling scanpaths, however, there is as of yet no universal agreement on what metrics should be applied to measure scanpath similarity or the quality of a predicted scanpath from a computational model. Many similarity measures have been suggested in different contexts and little is known about their behavior or properties. This paper presents in one place a review of these metrics, axiomatic analysis of gaze metrics for scanpaths, and careful analysis of the discriminative power of different metrics in order to provide a roadmap for further future analysis. This is accompanied by experimentation based on classic modeling strategies for simulating sequential selection from traditional representations of saliency, and deep neural networks that produce sequences by construction. Experiments provide strong support for the necessity of sequential analysis of attention and support for certain metrics including a family of metrics introduced in this paper motivated by the notion of scanpath plausibility.
Packages delivery based on marker detection for UAVs

Intelligent UAVs have great application prospects in the express delivery industry. However, efficient environmental awareness and self-control capabilities are must-have for UAVs. In order to solve this, this paper design an efficient delivery system combined with marker detection, which improve the delivery and landing accuracy. In this system, the embedded platform as upper-computer carries out the task of delivery marker detection and trajectory planning, the underlying flight controller completes the UAV attitude control, motion control and data interaction through communication protocol. Besides, the embedded platform simultaneously accomplishes the control commands to UAVs based on Robot Operating System (ROS) distributed architecture, which can be quickly transplanted and deployed. Aiming at the system's visual perception problem, this paper proposes a color detection algorithm based on color channel ratio to detect the markers' color quickly. The experiment used different colors of "mailbox" as the receiving device on the ground to guide UAV to package delivery. The UAVs carry the packages and search for the mailbox independently, and finally control the delivery servo to release package. After the delivery completed, the UAVs return to the take-off location and start the visual landing in conjunction with the detection of the landmark. Experimental results show that the proposed package delivery system can perform tasks quickly and efficiently.
Design of the footprints of uncertainty for a class of typical interval type-2 fuzzy PI and PD controllers

The Footprint of Uncertainty (FOU) for an Interval type-2 (IT2) input fuzzy set is a key parameter of IT2 fuzzy PI and PD (FPI and FPD) controllers because it provides additional options to handle uncertainties. Because the effects of FOUs on IT2 FPI and FPD controller are still unclear, it is necessary to provide a guideline for designing the FOUs. In this study, from the input–output relationship of IT2 FPI and FPD controllers using typical and popular configuration, the variation trend of variable gains, in relation to the increase of FOUs, was analyzed. It is mathematically proven that variable gains of typical controllers decrease or not increase as the FOUs increase. These theoretical results can be extended to provide a guideline for designing FOUs of typical IT2 FPI and FPD controllers. Real-time response of linear voice coil motor is used to show the effectiveness of our results.
An Interactive Narrative to Improve Cultural Heritage Experience in Elementary School Children

Cultural Heritage can use ICT and game design to provide compelling experiences of visiting ancient ruins. In fact, archaeological sites benefit from being enriched with additional meanings that help visitors to contextualize what they see. For example, young tourists, when accessing cities of the past, could miss the chance of really appreciating the history and the culture of extinguished civilizations. We propose an interactive story for them through which they can explore an archaeological site. We carefully planned the structure of this game according to four main dimensions: magic circle, narrative, immersion, and agency. Afterward, the prototype was presented as a scenario to five tourist guides, who positively evaluated the game and also gave us some valuable feedback.
Vision-Based Autonomous Navigation Approach for a Tracked Robot Using Deep Reinforcement Learning

Tracked robots need to achieve safe autonomous steering in various changing environments. In this article, a novel end-to-end network architecture is proposed for tracked robots to learn collision-free autonomous navigation through deep reinforcement learning. Specifically, this research improved the learning time and exploratory nature of the robot by normalizing the input data and injecting parametric noise into the network parameters. Features were extracted from the four consecutive depth images by deep convolutional neural networks, which were used to derive the tracked robot. In addition, a comparison was made between three Q-variant models in terms of average reward, variance, and dispersion across episodes. Also, a detailed statistical analysis was performed to measure the reliability of all the models. The proposed model was superior in all the environments. It is worth noting that our proposed model, layer normalisation dueling double deep Q-network (LND3QN), could be directly transferred to a real robot without any fine-tuning after being trained in a simulation environment. The proposed model also demonstrated outstanding performance in several cluttered real-world environments considering both static and dynamic obstacles.
Multifunctionality in a reservoir computer

Multifunctionality is a well observed phenomenological feature of biological neural networks and considered to be of fundamental importance to the survival of certain species over time. These multifunctional neural networks are capable of performing more than one task without changing any network connections. In this paper we investigate how this neurological idiosyncrasy can be achieved in an artificial setting with a modern machine learning paradigm known as `Reservoir Computing'. A training technique is designed to enable a Reservoir Computer to perform tasks of a multifunctional nature. We explore the critical effects that changes in certain parameters can have on the Reservoir Computers' ability to express multifunctionality. We also expose the existence of several `untrained attractors'; attractors which dwell within the prediction state space of the Reservoir Computer that were not part of the training. We conduct a bifurcation analysis of these untrained attractors and discuss the implications of our results.
Consistency Guided Network for Degraded Image Classification

Although clear image classification and degraded image restoration have been studied a lot, regrettably, the degraded image classification problem has been largely overlooked. Degraded images usually have low classification performance due to the presence of blur, noise and other imperfections. Existing methods improve classification performance of degraded images by simple restoration, fine-tuning or data augment techniques. However, they ignore the useful information guided by clear images, which leads to the performance gap between degraded images and clear images. In this article, we find that the category distribution, feature distribution and visual attention of degraded images are usually inconsistent with that of clear images. Motivated by this observation, we propose an end-to-end Consistency Guided Network, named CG-Net, for degraded image classification. More specifically, we first propose Category Consistency Loss (CCL) to guide the model to learn category distribution that is more consistent with clear images. Second, we propose Semantic Consistency Loss (SCL) to enforce the model to learn more robust feature representation that is guided by clear images. Third, we propose Visual Attention Alignment Loss (VAAL), which can align the semantically informative regions between clear images and degraded images to improve the performance of degraded images. Besides, our method is more general and suitable for various kinds of degraded images. We conduct extensive experiments on various degraded images. The experimental results show that the proposed method significantly outperforms baselines, which demonstrate the effectiveness of our proposed method.
Player Identification in Hockey Broadcast Videos

We present a deep recurrent convolutional neural network (CNN) approach to solve the problem of hockey player identification in NHL broadcast videos. Player identification is a difficult computer vision problem mainly because of the players' similar appearance, occlusion, and blurry facial and physical features. However, we can observe players' jersey numbers over time by processing variable length image sequences of players (aka 'tracklets'). We propose an end-to-end trainable ResNet+LSTM network, with a residual network (ResNet) base and a long short-term memory (LSTM) layer, to discover spatio-temporal features of jersey numbers over time and learn long-term dependencies. For this work, we created a new hockey player tracklet dataset that contains sequences of hockey player bounding boxes. Additionally, we employ a secondary 1-dimensional convolutional neural network classifier as a late score-level fusion method to classify the output of the ResNet+LSTM network. This achieves an overall player identification accuracy score over 87% on the test split of our new dataset.
Blind image deblurring based on the sparsity of patch minimum information

Blind image deblurring is a very challenging inverse problem due to the severe ill-posedness caused by the unknown kernel and the latent clear image. To tackle this problem, appropriate smoothing regularizations and image priors are usually employed and incorporated into the associated variational models to alleviate the inherent ill-posedness. In this paper, we first propose a strongly imposed zero patch minimum constraint for the latent image, which helps alleviate the ill-posedness of the inverse problem for blind image deblurring. Then, we retrieve important fine details by assigning the patch minimum information obtained from the blurred image back to the latent image to further enhance its structure. Finally, we introduce an adaptive regularizer which was shown to have significantly better edge-preserving property than the total variation regularizer for the image restoration of degraded images. Operator splitting techniques are used to accomplish an efficient numerical implementation of the proposed variational model. A number of numerical experiments and comparisons with some state-of-the-art methods are conducted to demonstrate the effective performance of the newly proposed method.
Use of Virtual Reality in Product Development by Distributed Teams

This paper shows how future VR software improvements can support work in distributed teams. The comprehensive summary to the topics "VR-technology" and "working in distributed teams" lead to the following recommendations. In order to improve distributed teamwork in product development in the future the VR-technology can be a suitable tool. Therefor the data transfer between CAD and VR must be smooth. Moreover, existing or new VR-Software must be adapted in a way that allows the creation of 3D objects directly in the virtual environment.
Algebra-chaos amalgam and DNA transform based multiple digital image encryption

This study suggests a novel color multiple image encryption technique using “Algebra-Chaos Amalgamated 256-length-12-bit random sequence” and DNA Transform. The proposed random sequence (S-box) has the property of occupying small computer memory space as regards to the existing S-boxes. Moreover, for simplicity and novelty, 1D sin chaotic map and 1D logistic map are mixed to generate a 1D mixed Chaotic map. Initially, a 12 × 12 (256-length-12-bit) S-box is designed by the unit elements of a chain ring, and then it is mixed with the 1-D mixed chaotic map that results triplets T1,T2 and T3 of high random behavior. A multiple digital image encryption is performed by using the triplet for substitution-permutation along with DNA transform. The proposed image encryption technique enhances key-space size. Moreover, the experimental results show that the proposed technique of encryption for digital medium has extra ordinary resistance against the well-known attacks.
Improved dual-scale residual network for image super-resolution

In recent years, convolutional neural networks have been successfully applied to single image super-resolution (SISR) tasks, making breakthrough progress both in accuracy and speed. In this work, an improved dual-scale residual network (IDSRN), achieving promising reconstruction performance without sacrificing too much calculations, is proposed for SISR. The proposed network extracts features through two independent parallel branches: dual-scale feature extraction branch and texture attention branch. The improved dual-scale residual block (IDSRB) combined with active weighted mapping strategy constitutes the dual-scale feature extraction branch, which aims to capture dual-scale features of the image. As regards the texture attention branch, an encoder–decoder network employing symmetric full convolutional-deconvolution structure acts as a feature selector to enhance the high-frequency details. The integration of two branches reaches the goal of capturing dual-scale features with high-frequency information. Comparative experiments and extensive studies indicate that the proposed IDSRN can catch up with the state-of-the-art approaches in terms of accuracy and efficiency.
Shallow and deep learning for event relatedness classification

In the two recent decades various security authorities around the world acknowledged the importance of exploiting the ever-growing amount of information published on the web on various types of events for early detection of certain threats, situation monitoring and risk analysis. Since the information related to a particular real-world event might be scattered across various sources and mentioned on different dates, an important task is to link together all event mentions that are interrelated. This article studies the application of various statistical and machine learning techniques to solve a new application-oriented variation of the task of event pair relatedness classification, which merges different fine-grained event relation types reported elsewhere into one concept. The task focuses on linking event templates automatically extracted from online news by an existing event extraction system, which contain only short text snippets, and potentially erroneous and incomplete information. Results of exploring the performance of shallow learning methods such as decision tree-based random forest and gradient boosted tree ensembles (XGBoost) along with kernel-based support vector machines (SVM) are presented in comparison to both simpler shallow learners as well as a deep learning approach based on long short-term memory (LSTM) recurrent neural network. Our experiments focus on using linguistically lightweight features (some of which not reported elsewhere) which are easily portable across languages. We obtained F1 scores ranging from 92% (simplest shallow learner) to 96.4% (LSTM-based recurrent neural network) evaluated on a newly created event linking corpus.
Determining Optimal Shard Size in a Hierarchical Blockchain Architecture

Monolithic blockchain architectures employed in Bitcoin and other major alt-coins are inherently non-scalable. In recent past, some hierarchical approaches have been explored to shard the decentralized blockchain to improve scalability. However, there is no discussion in the literature about how to determine an optimal shard size to maximize performance and how the presence of malicious or faulty nodes can impact on choosing an optimal shard size. To address these issues, this paper presents a sharding scheme and validation protocols for a hierarchical blockchain architecture named OptiShard. The hierarchy divides the network nodes into multiple disjoint shards and the majority of transactions are distributed among these shards in non-overlapped fashion. Optimal shard size is determined based on two parameters: performance and correctness of transaction validation in the presence of malicious or faulty nodes. OptiShard provides guaranteed majority of good shards, subject to a maximum allowable threshold of faulty nodes, by choosing the right shard size. It also provides a mechanism for identifying faulty shards and discarding all their transactions through the overlapping of a small fraction of transactions across all the shards. Experimental results performed on up to 800 Amazon EC2 nodes conform to the theoretical performance analyses and exhibit the impact of sharding the network on performance.
Multi-source domain adaptation with graph embedding and adaptive label prediction

Multi-source domain adaptation addresses the problem of transferring knowledge from multiple source domains to a target domain. Compared with conventional domain adaptation with single source and target domain, multi-source domain adaptation is more challenging because there are discrepancies not only across the source domains and the target domain, but also in source domains themselves. Hence, previous single-source domain adaptation methods may be suboptimal in handling multi-source scenarios. In this paper, we propose a multi-source domain adaptation method with graph embedding and adaptive label prediction to tackle the challenge. Specifically, our method leverages subspace learning and label prediction to mitigate both the inter-domain and intra-domain discrepancies. From the view of probability distribution, we match the first-order moment maximum mean discrepancy (MMD) to reduce the distribution divergence. From the view of sample relationship, we deploy graph embedding to preserve the similarity and discriminability of samples from different domains. Moment matching and geometry alignment are optimized in a unified objective to learn a subspace where both inter-domain and intra-domain discrepancies are reduced. For the label prediction, with the new representations learned in the subspace, we utilize K-means clustering and structural risk minimization (SRM) to predict pseudo labels in both low-dimensional subspace and high-dimensional RKHS. Specifically, we report two different strategies (denoted as ALP-u and ALP-s, respectively) for pseudo-labeling in this paper. The proposed ALP-u and ALP-s are effective in different kinds of domain adaptation scenarios, including multi-source and single-source domain adaptation. Experiments on three benchmark datasets verify that our ALP-u and ALP-s can outperform state-of-the-arts by a large margin.
A Data Science Approach for Detecting Honeypots in Ethereum

Ethereum smart contracts have recently drawn a considerable amount of attention from the media, the financial industry and academia. With the increase in popularity, malicious users found new opportunities to profit by deceiving newcomers. Consequently, attackers started luring other attackers into contracts that seem to have exploitable flaws, but that actually contain a complex hidden trap that in the end benefits the contract creator. In the blockchain community, these contracts are known as honeypots. A recent study presented a tool called HONEYBADGER that uses symbolic execution to detect honeypots by analyzing contract bytecode. In this paper, we present a data science detection approach based foremost on the contract transaction behavior. We create a partition of all the possible cases of fund movements between the contract creator, the contract, the transaction sender and other participants. To this end, we add transaction aggregated features, such as the number of transactions and the corresponding mean value and other contract features, for example compilation information and source code length. We find that all aforementioned categories of features contain useful information for the detection of honeypots. Moreover, our approach allows us to detect new, previously undetected honeypots of already known techniques. We furthermore employ our method to test the detection of unknown honeypot techniques by sequentially removing one technique from the training set. We show that our method is capable of discovering the removed honeypot techniques. Finally, we discovered two new techniques that were previously not known.
Requirements capture and comparative analysis of open source versus proprietary service oriented architecture

Service Oriented Architecture (SOA) integrates information systems towards an agile and reusable service-based connectivity. It is an approach amalgamating large scale private/public computer systems and other resources with continuous phenomenal advent evolution and leveraging of the World Wide Web (WWW, commonly referred to as the Web) social media, mobile communications, Big Data (BD), data analytics, Machine Learning (ML) based optimisation, Cloud Computing (CC) and Internet of Things (IoT), commonly known as Advanced Technologies (AT). Implementing SOA, whether Open Source Software (OSS) or proprietary or absolute freeware is a choice to be made which depends on the organisation's requirements in light of AT as well as a host of delivery and security concerns. In this paper, a comparative analysis of an open source vs. proprietary SOA for large scale computer systems servicing AT is presented by examining their main efficacies, features, advantages and disadvantages and capturing their generic technical functional and non-functional requirements in a unified manner. Furthermore, the SOA evaluation criteria, recommendations and conclusions are also presented.
Generic Decoding in the Sum-Rank Metric

We propose the first non-trivial generic decoding algorithm for codes in the sum-rank metric.The new method combines ideas of well-known generic decoders in the Hamming and rank metric.For the same code parameters and number of errors, the new generic decoder has a larger expected complexity than the known generic decoders for the Hamming metric and smaller than the known rank-metric decoders.
A Nonintrusive Elderly Home Monitoring System

Home anomaly monitoring is crucial for the elderly who live alone. A number of IoT-based home monitoring systems have been available, but most rely on privacy-intrusive cameras. With more and more concerns on privacy and security of human data, anomaly detection based on nonintrusive IoT devices becomes more desirable. Considering the elderly consumers, a low-cost system with good detection accuracy is further critical for the system's acceptability by elderly users. We propose a smart home monitoring system for living-alone senior citizens, relying on carefully designed, low-cost infrared sensor devices, as well as a cloud-based data processing and anomaly detection platform. Our PIR sensor device is effective in continuous monitoring of motion data in a user's apartment, and an open-hardware software platform is devised to support sensors manufactured by various vendors in the IoT system, all for cost reduction purpose. For privacy preservation, we encrypt collected data and store data indices in a blockchain system, to achieve efficient data access control and auditing. For motion anomaly detection, we propose a simple but effective environment adaptation method to work with the one-class support vector machine (OCSVM) method. Experiments driven by real-world traces show good reliability, accuracy, and efficiency of our system.