Reviewerly/exHarmony huggingface dataset:
Planning of AdHoc and IoT Networks Under Emergency Mode of Operation

Computer networks, and specifically AdHoc and IoT networks, are constantly evolving to become complex systems marked by diverse features such as clusterization, node mobility, a large number of devices/objects and deployment in a wide variety of environments. Within this context, packet routing that is not optimized (statically, i.e. at design time) may lead, at best, to ineffective behavior (power consumption, packet delays, and congestion). In more critical scenarios, it may lead to network malfunction and abnormal behavior. On the other hand, another relevant factor is the lack of redundancy in emergency scenarios, which leaves the network vulnerable to malfunction and under-performing. In this work, using discrete-event simulation of a model that includes an AdHoc network, Monte Carlo simulation, Random WayPoint mobility model, and an IoT network, we introduce the planning steps, in terms of routing optimization and redundancy, considering an emergency scenario. Specifically, we analyze the processor utilization and mean queueing time of the network under the emergency scenario where nodes and links may be severely affected. The results indicate the impact of routing on network performance for the emergency context in consideration. For example, the number of CPUs (cluster heads) was reduced by 30% with optimized routes. We have extended the model to include redundancy both at the availability and connectivity levels, and we have also shown the steps to the calculation and inclusion of such redundancy.Computer networks, and specifically AdHoc and IoT networks, are constantly evolving to become complex systems marked by diverse features such as clusterization, node mobility, a large number of devices/objects and deployment in a wide variety of environments. Within this context, packet routing that is not optimized (statically, i.e. at design time) may lead, at best, to ineffective behavior (power consumption, packet delays, and congestion). In more critical scenarios, it may lead to network malfunction and abnormal behavior. On the other hand, another relevant factor is the lack of redundancy in emergency scenarios, which leaves the network vulnerable to malfunction and underperforming. In this work, using discrete-event simulation of a model that includes an AdHoc network, Monte Carlo simulation, Random WayPoint mobility model, and an IoT network, we introduce the planning steps, in terms of routing optimization and redundancy, considering an emergency scenario. Specifically, we analyze the processor utilization and mean queueing time of the network under the emergency scenario where nodes and links may be severely affected. The results indicate the impact of routing on network performance for the emergency context in consideration. For example, the number of CPUs (cluster heads) was reduced by 30% with optimized routes. We have extended the model to include redundancy both at the availability and connectivity levels, and we have also shown the steps to the calculation and inclusion of such redundancy.
Comparative analysis of energy based optimized dynamic source multipath routing protocol in WSNs

&lt;p&gt;Wireless Sensor Network (WSN) has been considered to be a formula for overcoming various requirements like surveillance, health care, traffic surveillance and defense systems. As an aid to the demands for Wireless sensor network, research has been going on the efficient consumption of energy and secure communication areas for WSN. Here introducing Ant colony optimization based Optimized dynamic Secure multipath routing protocol (ODMRP) have improvised WSN in terms of energy efficiency, performance, strength and scalability. This paper proposed mechanism of Ant Colony Optimization based on the optimized dynamic secure multipath routing protocol (ODMRP-ACO) for data transmission in the WSNs. This eases the data transmission via WSNs. The shortest path between the source node and the destination node is analyzed by the ant colony cooperation which is based on the ACO. There are several routing protocols like Energy-aware and Secure Routing with Trust (ESRT-ACO), Split multipath routing protocol-Ant colony Optimization (SMR-ACO), Light Weight Trust Based routing protocol (TLB-AODV) and AODV routing protocols that are built with the ACO based Routing Protocol. They are concerned over the End to End Delay, Packet Delivery Rate, Routing Overhead, Throughput and Energy Efficiency. The data analytic results have proven to be effective compared to the other routing protocols designed for WSN. This is because; the overall ODMRP-ACO was calculated to be 96.98% better than the other existing. For the newly developed ODMRP-ACO protocol, the simulation platform used is NS2 simulation software.&lt;/p&gt;
Combining Conflict-Driven Clause Learning and Chronological Backtracking for Propositional Model Counting

In propositional model counting, also named #SAT, the search space needs to be explored exhaustively, in contrast to SAT, where the task is to determine whether a propositional formula is satisfiable. While state-of-the-art SAT solvers are based on non- chronological backtracking, it has also been shown that backtracking chronologically does not significantly degrade solver performance. Hence investigating the combination of chronological backtracking with conflict-driven clause learning (CDCL) for #SAT seems evident. We present a calculus for #SAT combining chronological backtracking with CDCL and provide a formal proof of its correctness.
A Novel Image Zero-Watermarking Scheme Based on Non-Uniform Triangular Partition

In this paper, a novel time-domain zero-watermarking algorithm based on Non-Uniform Triangular Partition (NTP) is proposed. NTP is an image representation method in which the bivariate polynomials are calculated and used to represent the pixel values in each triangular region under a set control error. The number of triangles in each 8×8 region is counted and recorded as a feature matrix which will be mapped to a binary watermark scrambling by the Arnold scrambling method to enhance the security of the algorithm. The feature matrix and the scrambled binary watermark are stored as zero watermarks. Experimental results show that the proposed algorithm is robust to various attacks, such as JPEG compression, rotation, Gaussian noise and salt and pepper noise.
A Hybrid Generative Model for Online User Behavior Prediction

With the increase of rich datasets from various online platforms, predicting user behavior has been one of the most active research topics. The user behavior on these online platforms includes listening to music, watching videos, purchasing products, checking-in to places, and joining online sub-communities. Predicting online user behavior is an important challenge for various applications. Personalization, recommendation systems, target advertisements are based on user behavior prediction, where user's next purchases or actions need to be predicted. In this paper, we propose a hybrid generative model that can predict user behavior considering multiple factors. While previous work has been focused on two aspects individually: predicting repeat behavior or predicting new behavior, our model considers both aspects simultaneously during the learning process. The user-specific preference component is used to capture repeat behavior patterns, while the latent group preference component is used to discover new behavior. Besides these two components, we also consider the exogenous effect, which is not captured in the former two. Our experimental results on real-world datasets show how our proposed model outperforms the state-of-the-art model.
Continuous Variable Quantum Secret Sharing with Fairness

The dishonest participants have many advantages to gain others’ shares by cheating in quantum secret sharing (QSS) protocols. However, the traditional methods such as identity authentication and message authentication can not resolve this problem due to the reason that the share has already been released to dishonest participants before realizing the deception. In this paper, a continuous variable QSS (CVQSS) scheme is proposed with fairness which ensures all participants can acquire or can not acquire the secret simultaneously. The quantum channel based on two-mode squeezing states provides secure communications through which it can send shares successfully, as long as setting the squeezing and modulation parameters according to the quantum channel transmission efficiency and the Shannon information of shares. In addition, the Chinese Remainder Theorem (CRT) can provides tunable threshold structures according to demands of the complex quantum network and the strategy for fairness can be incorporated with other sharing schemes, resulting in perfect compatibility for practical implementations.
Residual Neural Processes

A Neural Process (NP) is a map from a set of observed input-output pairs to a predictive distribution over functions, which is designed to mimic other stochastic processes' inference mechanisms. NPs are shown to work effectively in tasks that require complex distributions, where traditional stochastic processes struggle, e.g. image completion tasks. This paper concerns the practical capacity of set function approximators despite their universality. By delving deeper into the relationship between an NP and a Bayesian last layer (BLL), it is possible to see that NPs may struggle in simple examples, which other stochastic processes can easily solve. In this paper, we propose a simple yet effective remedy; the Residual Neural Process (RNP) that leverages traditional BLL for faster training and better prediction. We demonstrate that the RNP shows faster convergence and better performance, both qualitatively and quantitatively.
An Energy and Deadline Aware Scheduling Using Greedy Algorithm for Cloud Computing

Cloud computing has been providing various services to different users by means of an aid of large and scalable virtualized resources on the internet.Owing to all the recent and inventive developments that are found in the field, there are several scheduling algorithms which were developed in a cloud computing environment with the intention of decreasing the services given in cloud computing.For a very enormous gauge, assorted and the multi-user atmosphere in the cloud scheme where maximization of profit for that of the Cloud Service Provider (CSP) has been the primary objective.For the purpose of this work, the inclusive optimization problem in the operation of the cloud system by means of lowering the cost of procedure and by maximizing the efficiency of energy.At the same time, it satisfies the deadlines that are definite in Service Level Agreements (SLA) that has been addressed from a CSP perspective.The work proposes a Greedy algorithm for the environment of the cloud and this is compared to the scheduling of a First Come First Served (FCFS) and the Min-Min scheduling procedure.This system exploits the tasks and their heterogeneity and also the resources using a scheduler unit that schedules and allocates the tasks which are deadline-constrained which is delimited to the nodes that are energy conscious.After this, the CSP capitalizes on the parallelisms of data for every user workload and also effectively manages all collective user requests and also apply the custom optimization that creates a cost of global energy and a cloud platform which is dead-line aware.The results of the experiment prove that this proposed Greedy algorithm which achieves a performance which is better (a guarantee ratio, utilization of resources and energy saving) compared to the FCFS and the Min-Min scheduling algorithm.
Decidability and Complexity of Action-Based Temporal Planning over Dense Time

This paper studies the computational complexity of temporal planning, as represented by PDDL 2.1, interpreted over dense time. When time is considered discrete, the problem is known to be EXPSPACE-complete. However, the official PDDL 2.1 semantics, and many implementations, interpret time as a dense domain. This work provides several results about the complexity of the problem, studying a few interesting cases: whether a minimum amount ϵ of separation between mutually exclusive events is given, in contrast to the separation being simply required to be non-zero, and whether or not actions are allowed to overlap already running instances of themselves. We prove the problem to be PSPACE-complete when self-overlap is forbidden, whereas, when allowed, it becomes EXPSPACE-complete with ϵ-separation and undecidable with non-zero separation. These results clarify the computational consequences of different choices in the definition of the PDDL 2.1 semantics, which were vague until now.
ACTION RECOGNITION IN STILL IMAGES BASED ON R-FCN DETECTOR

Still image action recognition is the task of detecting and classifying a person's action based on a single image.Most of the research on the field has focused on the action classification task, assuming that human bounding-box annotations are provided at both training and testing time.Thus, researchers have pursued different cues to characterize actions, often using multiple image patches.We argue that the action should be considered during the localization stage, considering that the existing state-of-the-art generic object detectors are not adequate for proposing candidate bounding boxes for action classification as they disregard human-object interactions.Our approach outperforms significantly methods based on general person detection by 16.5% mean average precision (mAP) on the PPMI dataset and by 29.7% on PASCAL VOC 2012.Our approach also achieves state-of-the-art results in both datasets, outperforming them by 1.2% and 3.7% respectively.
Comparison of Classification Techniques for Wall Following Robot Navigation and Improvements to the KNN Algorithm

Autonomous navigation is an important feature that allows the robot to move independently from a point to another without a tele-operator.This feature makes mobile robots useful in many tasks that require transportation, exploration, surveillance, guidance, inspection …etc.Furthermore, autonomous robots deal with real time environments that tend to be complex, nonlinear and partially observed.They also operate with limited memory resources and tight time constraints.In this paper, we present an investigation related to mobile robot navigation.We first compare a group of classification algorithms using real traces of wall following robot navigation.Then we focus on the k Nearest Neighbors (KNN) algorithm to improve it and help it be more applicable in autonomous robot navigation.We applied a Nearest Neighbor set reduction technique to help reduce the high running time of KNN.The results indicate that KNN is a competing algorithm especially after decreasing the running time significantly by a factor of 19 and combining that with the KNN's existing features.Results are further improved by applying an attribute selection method.
Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance

Unpaired image-to-image domain translation involves the task of transferring an image in one domain to another domain without having pairs of data for supervision. Several methods have been proposed to address this task using Generative Adversarial Networks (GANs) and cycle consistency constraint enforcing the translated image to be mapped back to the original domain. This way, a Deep Neural Network (DNN) learns mapping such that the input training distribution transferred to the target domain matches the target training distribution. However, not all test images are expected to fall inside the data manifold in the input space where the DNN has learned to perform the mapping very well. Such images can have a poor mapping to the target domain. In this paper, we propose to perform Langevin dynamics, which makes a subtle change in the input space bringing them close to the data manifold, producing benign examples. The effect is significant improvement of the mapped image on the target domain. We also show that the score function estimation by denoising autoencoder (DAE), can practically be replaced with any autoencoding structure, which most image-to-image translation methods contain intrinsically due to the cycle consistency constraint. Thus, no additional training is required. We show advantages of our approach for several state-of-the-art image-to-image domain translation models. Quantitative evaluation shows that our proposed method leads to a substantial increase in the accuracy to the target label on multiple state-of-the-art image classifiers, while qualitative user study proves that our method better represents the target domain, achieving better human preference scores.
RST invariant watermarking technique for vector map based on LCA-transform

The dangers of copyright protection can impact 2D vector maps, having a knock-on effect on the use of vector data. To achieve invariance property, uniform RST (rotation, scaling and translation) and disguising the digital vector map’s information by implementing distortion control, is all done by using watermarking schemes. Convert an original map, then engrain the watermark. An LCA algorithm is used in this study, as a newly proposed way to protect the vector maps under copyright. The procedure is operated in this order: 1) use an original map, altered by the LCA algorithm, 2) use the coefficient of the transformation to engrain the watermark, inserting the resulting frequency into the LSB wave, 3) the watermarked map is acquired by using the inverse LCA map transformation. Further investigations discovered that the necessary standards of fidelity and invisibility can be achieved using this technique. This procedure also gives out numerous frequency domains for digital watermarking; as well as being resilient to signal and geometric invasions.
Innovations in Ukrainian Modern Art: New Technologies

. In Ukrainian art of the XX–XXI centuries the dominant position has taken by contemporary art. In addition to the traditional art, many trends are being developed based on the various media, such as digital-art, internet-art, VR, interactive art, narrower types of net-art, software-art, game-art, as well as scientific progress — science-art, bio-art and others. All of these practices operate in the realm of technical transformation, where image performs a minor function. Updating of artistic practices is associated with updates to expressiveness. Changes in the artistic process caused by technology development are twofold: related to the update of the facilities and resources and digital technologies. The main characteristic of contemporary art is its interactivity, as the opportunity for the viewer to interact with the artist and to participate in the creation of works. And the biggest difference between digital and traditional art is that it exists only at the time of its creation and requires the immediate presence of the author or viewer.The world of art today has undergone several major changes that have affected all its traditional types and industries, disrupting the development of artistic practices related to the use of new technologies (their impact can be compared with the revolutionary invention of photography and cinema). Technological updates in this collaboration are mostly applied, acting as a «mediator» between the author’sidea and its tangible embodiment. As a result of technical innovations, new principles of exhibition space construction have emerged, and the process of transporting and exhibiting works has been modernized, expanding opportunities for exchange and cultural connections. The opportunity to virtually visit art events on the Internet and view museum exhibits from anywhere in the world, anyone can create art objects, freely distributing them on social networks. This all creates a renewed perception of reality, channelling social experience into the realm of the visual. By its nature, art that responds quickly and acutely to transformation and innovation is beginning to use its full range of technical and scientific achievements
Learning Multi-feature Based Spatially Regularized and Scale Adaptive Correlation Filters for Visual Tracking

Visual object tracking is a challenging problem in computer vision. Although the correlation filter-based trackers have achieved competitive results both on accuracy and robustness in recent years, there is still a need to improve the overall tracking performance. In this paper, to tackle the problems caused by Spatially Regularized Discriminative Correlation Filter (SRDCF), we suggest a single-sample-integrated training scheme which utilizes information of the previous frames and the current frame to improve the robustness of training samples. Moreover, manually designed features and deep convolutional features are integrated together to further boost the overall tracking capacity. To optimize the translation filter, we develop an alternating direction method of multipliers (ADMM) algorithm. Besides, we introduce a scale adaptively filter to directly learn the appearance changes induced by variations in the target scale. Extensive empirical evaluations on the TB-50, TB-100 and OTB-2013 datasets demonstrate that the proposed tracker is very promising for various challenging scenarios.
The Link Between Necessity And Randomness In Scientific Discovery

The article is devoted to the problem of the link between the essential and the accidental in the process of scientific discoveries. The author criticises Karl Popper concept who states that each scientific discovery is entirely “accidental”. The author‟ viewpoint is based on the methodology of dialectical materialism as a whole, the concept of dialectical determinism, current works on the theory of truth and the criterion of truth. The article treats randomness as the form of being and the phenomenon of necessity. The randomness is presented in the act of discovery by a variety of phenomena such as the particular time, the author of the discovery and the specific aspect of the scientific problem determining this discovery. The necessity here has to do with a specific measure reflecting the limits of change for the phenomenal side of scientific discovery, and this is true for other objective and subjective processes. The author argues that revealing the link between the essential and the accidental in scientific discovery is a critical foundation for solving all epistemological problems, the problem of the criterion of the truth in the first place.
Multi-agent system for selecting images based on the gender and age

This paper presents a multi-agent system that is able to search people on a database of images recognizing patterns of facial features on each person, based on the main features of the face (eyes, nose and mouth). Using that multi-agent architecture, the system can do the work faster applying Fisherfaces algorithm for the face recognition and classification. This technology can be used for several purposes like specific ads in each user group to suit better their interests or search for the age and gender of people that usually go to different places like malls or shops.