Reviewerly/exHarmony huggingface dataset:
Controlling Multimedia Player With Eye Gaze Using Webcam

Gaze-based interaction between human and computers has opened a potential domain of effortless supervision. The eye is one of the most important organs to perceive information from our surroundings and it can be the most conspicuous way to interact with computers. Therefore, in this research, a system model has been introduced to control the multimedia player with eye gaze using an economical webcam, as well as provides higher precision and robustness. The goal is to control the play, pause, forward and backward functions of a multimedia player. The click event was triggered by detecting the user’s eye blink.
Intelligent System for Robotic Navigation Using ANFIS and ACOr

In this article we propose an intelligent system for mobile robot navigation in different environments, using ANFIS and ACOr. This system is capable of ensuring to mobile robot to navigate by reacting to the various situations encountered in different environments. In a first step, we use the ANFIS controller (Adaptive network-based fuzzy inference system) in which the contribution of the fuzzy logic of TAKAJI-SUGENO is added to that of the neural networks in a suitable way. In the second step, the ant colony method in a continuous environment ACOr (Ant colony optimization for continuous domains) is grafted into the second layer of the ANFIS network for hybridization. Simulations of the movements of the robot and the graphic interfaces are realized under the C ++ language.
Efficient Shape Classification using Zernike Moments and Geometrical Features on MPEG-7 Dataset

There is an urgent need and demand for manipulating images to extract useful information from them. In every field, whether it is biotechnology, botany, medical, robotics or machinery, ...
Image Segmentation Using Metaheuristics

The image segmentation is considered one of the most popular pre-processing in image processing and computer vision. It aims to partition the image into different regions which have the same properties such as color, texture, shape, and others. There are several image segmentation methods proposed to achieve this task, which include the traditional and other global methods. In this chapter, we present a review of the most popular three image segmentation methods called clustering, edge, region growing. In addition, the advantage and limitations of each method is discussed with some recommendation to be used these methods in future works are given.
Divisively Normalized Sparse Coding: Toward Perceptual Visual Signal Representation

Sparse representation has been shown to be highly correlated with the visual perception of natural images, which can be characterized by a linear combination of neuronal responses in the visual cortex. Divisive normalization transform (DNT) has been proven to be an effective method in reducing statistical and perceptual dependencies for nonlinear properties in primary visual cortex. In this paper, we develop a divisively normalized sparse coding scheme, aiming to further bridge the gap between sparse representation and human visual perception. We show that such a scheme is perceptually meaningful for representing visual signals, with which the pixel-domain image representation and processing tasks can be feasibly and efficiently achieved in the divisively normalized sparse-domain. Specifically, we develop a sparse-domain similarity (SDS) index for perceptual quality evaluation, where the DNT is employed for transforming image signals into a perceptually uniform space. Furthermore, the proposed SDS index is employed to optimize the sparse coding process when representing natural images. The experimental results indicate that the SDS can provide accurate and consistent predictions of perceived image quality, and the performance of sparse coding can be significantly improved in terms of both objective and subjective quality evaluations.
UAV Reconnaissance using Bio-Inspired Algorithms: Joint PSO and Penguin Search Optimization Algorithm (PeSOA) Attributes

Importance of bio-inspired algorithms (BIAs) is increasing exponentially because of their capability to optimization different problems in various fields like artificial intelligence, and medicine etc. Because of the autonomous nature, BIAs are now being used in unmanned aerial vehicles (UAVs) for applications areas like internet of things (IoT), autonomous area searching and reconnaissance etc. This paper presents a UAV reconnaissance strategy jointly using the particle swarm optimization (PSO) algorithm and a few attributes of the penguin search optimization algorithm (PeSOA). The strategy mainly focuses on finding the rescue targets utilizing UAVs by optimizing their movement using combined PSO and PeSOA attributes. The results are analyzed in terms of grouping and non-grouping scenarios with and without using PeSOA attributes.
The Generation of $(n,n(n-1),n-1)$ Permutation Group Codes for Communication Systems

This paper concerns the construction and hardware execution of (n, n(n-1), n-1) permutation group codes (PGCs) for communication systems. Cyclic shift techniques acting on a permutation, including cyclic shift operators and their compound functions (CFs), are defined and demonstrated, then compared to other tools such as cyclic subgroups and n-dimension cyclic shift registers. A generalized finite state machine is established that can enumerate n! permutations based on complete CFs. This is a hardware-oriented algorithm that is capable of being significantly faster than all existing algorithms which we can find up to now. After this, the ideal structural features for an ordered set of n! permutations are discussed and the multiset features of (n, n(n-1), n-1)-PGCs in this ordered set are pinpointed. A simpler way of generating (n, n(n-1), n-1)-PGCs is then developed using cyclic shift techniques, where the code length n is a prime. This outperforms existing approaches using composite operation and affine transform on module-n operation. The simulation experiments in AWGN channel show that the performance on codeword error rates (WER) and signal-noise ratio (SNR) becomes better as the code length increases and the best SNR performance achieves −0.8 dB at 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> WER for code length n=11.
The Game Among Bribers in a Smart Contract System

Blockchain has been used to build various applications, and the introduction of smart contracts further extends its impacts. Most of existing works consider the positive usage of smart contracts but ignore the other side of it: smart contracts can be used in a destructive way, particularly, they can be utilized to carry out bribery. The hardness of tracing a briber in a blockchain system may even motivate bribers. Furthermore, an adversary can utilize bribery smart contracts to influence the execution results of other smart contracts in the same system. To better understand this threat, we propose a formal framework to analyze bribery in the smart contract system using game theory. We give a full characterization on how the bribery budget of a briber may influence the execution of a smart contract if the briber tries to manipulate its execution result by bribing users in the system.
Deep learning based guidewire segmentation in x-ray images

X-ray fluoroscopy is commonly used during liver embolization procedures to guide intravascular devices (e.g. guidewire and catheter) to the branches of the hepatic arteries feeding tumors. A vascular roadmap can be created to provide a reference for the device position. Recently, techniques have been developed to create dynamic vessel masks to compensate for respiratory motion. In order to superimpose the intravascular guidewire onto the vessel mask, robust segmentation is required. Commonly used techniques often use mask subtraction to isolate the device in x-ray images. However, this is not suitable due to the motion in liver applications. The proposed method uses a deep convolutional neural network to segment the guidewire in native (unsubtracted) x-ray images. The neural network uses an encoder / decoder structure, which is based on the VGG-16 network. To create a large dataset of annotated images, simulated images were created based on 3D digital subtraction angiography acquisitions of hepatic arteries in porcine studies. Random guidewire shapes were generated within the vascular volume and superimposed on the original non-contrast projection images. The network was trained using a set of 56,768 images created from 10 acquisitions. The segmentation results of the trained network were compared to a mask-subtraction-based algorithm for an independent validation data set. The deep learning algorithm (Dice = 58.1%, false negative rate (FNR) = 9.6%) outperformed the subtraction technique (Dice = 23.7%, FNR = 40.8%). This study shows that the deep learning approach is suitable for robust segmentation of curvilinear structures such as guidewires and could be used to superimpose the segmented device on dynamic roadmaps.
2D2N: A Dynamic Degenerative Neural Network for Classification of Images of Live Network Data

The detection of new, novel attacks on organizational networks is a problem of ever-increasing relevance in today’s society. Research in the area is focused on the detection of “Zero-Day” and “Black Swan” events through the use of machine learning technologies. Where previous technologies needed a known example of malicious behavior to detect a similar event, recent advances in anomaly detection on network activity has shown promise of detecting novel attacks. In a real word environment however, novel behavior occurs relatively frequently as users utilize new software applications and new standards in networking. Changes such as these, while of notable importance to network security technicians, may not present themselves as an imminent threat to a network. This paper proposes a novel method for the detection and classification of changes in networking behavior. Through the use of a Dynamic Degenerative Neural Network (2D2N), changes in recognizable user activity are dynamically classified and stored for future reference. Through the use of a time-based entropy function, infrequent activity can be analyzed and given precedence over frequent activity. This aids in the classification of abnormal activity for fast, efficient assessment by the relevant persons in an organization. The proposed method enables the detection, classification and scoring of any and all user activity on a network. Evaluation of the proposed method is based upon live data gathered from a large, multinational organization.
Evaluating Deep Models for Dynamic Brazilian Sign Language Recognition

We propose and investigate the use of deep models for dynamic gesture recognition, focusing on the recognition of dynamic signs of the Brazilian Sign Language (Libras) from depth data. We evaluate variants and combinations of convolutional and recurrent neural networks, including LRCNs and 3D CNNs models. Experiments were performed with a novel depth dataset composed of dynamic signs representing letters of the alphabet and common words in Libras. An evaluation of the proposed models reveals that the best performing deep model achieves over 99% accuracy, and greatly outperforms a baseline method.
Multi-target Tracking Based on Gaussian Mixture Labeled Multi-Bernoulli Filter with Adaptive Gating

In this paper, an adaptive gating technique is applied to labeled multi-Bernoulli filter. In target tracking systems, elliptical gating based on chi-square test is widely used to filter out clutter. However, tracking performance and robustness can be improved by adaptively changing the gate size. To apply adaptive gating to labeled multi-Bernoulli (LMB) filter based on Gaussian mixture, gate size is adjusted by weight of Gaussian mixture. To verify the effect of adaptive gating in LMB filter, simulations for multi-target tracking in cluttered environments are carried out. The simulation results show that the adaptive gating performs similar or better than elliptical gating in terms of OSPA error.
Sequences of selective rotation operators to engineer interactions for quantum annealing on three qutrits

We have done simulating of factorization the number 15 on three qutrits, represented by the spins <i>S</i> = 1, by quantum annealing. We assume that strong one-spin interaction allow selectively operate on different transitions between levels of the each qutrit. We present a sequence of selective rotation operators to engineer from dipole-dipole interaction a timedependent effective Hamiltonian necessary for solving the problem. Also we find dependence of fidelity versus different parameters: magnetic field, total annealing time, and duration of time step, when the continuous variation of the Hamiltonian is replaced by a discrete one.
Otsu’s Between Class Variance and the Tree Seed Algorithm

In this chapter, an alternative image segmentation method is proposed to find the multi-level thresholding values. The proposed method is based on the tree seed algorithm (TSA) which emulates the tree generations. The proposed TSA used the maximum between class variance criterion (Otsu) as a fitness function. In order to evaluate the performance of the proposed method a set of images are used and the results and compared with other methods. The experimental results illustrated that the proposed TSA method has better performance to determine the multi-level thresholding problem for image segmentation than other methods regarding performance measures.
Analysis of gradient-based cuckoo search for the large scale optimal rfid network planning

Nature inspired algorithms provide an efficient method to resolve the problems of RFID Network Planning optimization that are not possible with the conventional methods. The gradient of the RFID Network Planning objective function was recently used to improve the precision of global optimal solutions. Therefore, this work presents a comparative study between the Gradient-Based Cuckoo Search (GBCS) and state-of the-art algorithms in a large, complex, and dynamic RNP network. The outcome of this study analyzed the algorithms performance in terms of tag coverage, required number of readers, and interference between reader propagation areas. The present method specifies the combinatorial performance of the reader's propagation area based on the evaluation of the tag density and location by using the Gradient-Based to manage the input representation of the Cuckoo Search. The results observed high local information for the objective function, which facilitated the choice of complex RFID Network Planning parameters that enabled the algorithm to work with big data in large scale conditions.
Improved Security Evaluation Techniques for Imperfect Randomness from Arbitrary Distributions

Dodis and Yu (TCC 2013) studied how the security of cryptographic primitives that are secure in the "ideal" model in which the distribution of a randomness is the uniform distribution, is degraded when the ideal distribution of a randomness is switched to a "real-world" (possibly biased) distribution that has some lowerbound on its min-entropy or collision-entropy. However, in many constructions, their security is guaranteed only when a randomness is sampled from some non-uniform distribution (such as Gaussian in lattice-based cryptography), in which case we cannot directly apply the results by Dodis and Yu. In this paper, we generalize the results by Dodis and Yu using the Rényi divergence, and show how the security of a cryptographic primitive whose security is guaranteed when the ideal distribution of a randomness is a general (possibly non-uniform) distribution Q, is degraded when the distribution is switched to another (real-world) distribution R. More specifically, we derive two general inequalities regarding the Rényi divergence of R from Q and an adversary's advantage against the security of a cryptographic primitive. As applications of our results, we show (1) an improved reduction for switching the distributions of distinguishing problems with public samplability, which is simpler and much tighter than the reduction by Bai et al. (ASIACRYPT 2015), and (2) how the differential privacy of a mechanism is degraded when its randomness comes from not an ideal distribution Q but a real-world distribution R. Finally, we show methods for approximate-sampling from an arbitrary distribution Q with some guaranteed upperbound on the Rényi divergence (of the distribution R of our sampling methods from Q).
Avoiding “conflicts of interest”: a computational approach to scheduling parallel conference tracks and its human evaluation

Conferences with contributed talks grouped into multiple concurrent sessions pose an interesting scheduling problem. From an attendee’s perspective, choosing which talks to visit when there are many concurrent sessions is challenging since an individual may be interested in topics that are discussed in different sessions simultaneously. The frequency of topically similar talks in different concurrent sessions is, in fact, a common cause for complaint in post-conference surveys. Here, we introduce a practical solution to the conference scheduling problem by heuristic optimization of an objective function that weighs the occurrence of both topically similar talks in one session and topically different talks in concurrent sessions. Rather than clustering talks based on a limited number of preconceived topics, we employ a topic model to allow the topics to naturally emerge from the corpus of contributed talk titles and abstracts. We then measure the topical distance between all pairs of talks. Heuristic optimization of preliminary schedules seeks to balance the topical similarity of talks within a session and the dissimilarity between concurrent sessions. Using an ecology conference as a test case, we find that stochastic optimization dramatically improves the objective function relative to the schedule manually produced by the program committee. Approximate Integer Linear Programming can be used to provide a partially-optimized starting schedule, but the final value of the discrimination ratio (an objective function used to estimate coherence within a session and disparity between concurrent sessions) is surprisingly insensitive to the starting schedule. Furthermore, we show that, in contrast to the manual process, arbitrary scheduling constraints are straightforward to include. We applied our method to a second biology conference with over 1,000 contributed talks plus scheduling constraints. In a randomized experiment, biologists responded similarly to a machine-optimized schedule and a highly modified schedule produced by domain experts on the conference program committee.
Evaluating Scenario-Specific Loading Processes on Mobile Phones

The manuscript presents a study that evaluates satisfaction with loading processes during human interactions with mobile devices. This is an innovative study to investigate human perception in terms of loading time for critical scenarios using a realistic mobile device. The scenarios were retrieved by internet searching. Consequently, high-fidelity models were reconstructed based on the identified scenarios. The measurements of contemporary commercial mobile devices yielded typical loading time values, which were subsequently applied in these models. Subjects operated these models, which were installed in a mobile terminal, and scored the models in terms of the loading time and processes. The results indicated that a shorter loading time was generally associated with higher scores. However, unsatisfactory scores were given to the shortest loading interval for the social App, which may indicate that users have higher expectations for this scenario. Furthermore, animation improved subjective satisfaction. These experimental protocols, the developed tools and the obtained results benefit not only manufacturers but also application developers.
Ontology evolution for personalised and adaptive activity recognition

Ontology-based knowledge-driven activity recognition (AR) models play a vital role in realm of Internet of Things (IoTs). However, these models suffer the shortcomings of static nature, inability of self-evolution, and lack of adaptivity. Also, AR models cannot be made comprehensive enough to cater all the activities and smart home inhabitants may not be restricted to only those activities contained in AR model. So, AR models may not rightly recognise or infer new activities. Here, a framework has been proposed for dynamically capturing the new knowledge from activity patterns to evolve behavioural changes in AR model (i.e. ontology based model). This ontology-based framework adapts by learning the specialised and extended activities from existing user-performed activity patterns. Moreover, it can identify new activity patterns previously unknown in AR model, adapt the new properties in existing activity models and enrich ontology model by capturing change representation to enrich ontology model. The proposed framework has been evaluated comprehensively over the metrics of accuracy, statistical heuristics, and Kappa coefficient. A well-known dataset named DAMSH has been used for having an empirical insight into the effectiveness of proposed framework that shows a significant level of accuracy for AR models.
Typed path polymorphism

Path polymorphism enables the definition of functions uniformly applicable to arbitrary recursively specified data structures. Path polymorphic function declarations rely on patterns of the form x y (i.e. the application of two variables), which decompose a data structure into its parts. We propose a static type system for a calculus that captures this feature, combining constants as types, union types and recursive types. The fundamental properties of Subject Reduction and Progress are addressed to guarantee well-behaved dynamics; they rely crucially on a novel notion of pattern compatibility. We also introduce an efficient type-checking algorithm by formulating a syntax-directed variant of the type system. This involves algorithms for checking type equivalence and subtyping, both based on coinductive characterizations of those relations.
A Risk Management Framework for User-Generated Content on Public Display Systems

Digital public displays can represent a powerful medium for personal expression and situated communication. However, before they can actually serve as an effective communication medium, they need to move towards more open models, in which user-generated content can play a more prominent role in their relevance and value proposition. The key challenge, however, is how to share control with users while being able to guarantee that published content matches the social expectations of a place and the goals of the display owner. In this study, we explore a risk management methodology as a comprehensive approach to this issue. We propose a framework that supports the systematic elicitation of the risks involved, their prioritisation, and the selection of the specific combination of moderation techniques that is able to reduce risk to a level that is deemed acceptable, while minimising the moderation effort and the impact on the willingness of users to publish their content. With this overall framework, we expect to help display owners to reason about the moderation needs of their displays and the best mapping between those needs and various moderation techniques.