Reviewerly/exHarmony huggingface dataset:
Stacked Autoencoder Based Weak Supervision for Social Image Understanding

Many studies in recent years have focused on social image understanding due to the increasing number of shared images from social networks and online communities.However, previous work in social image understanding fails to learn an effective feature representation because of a large amount of missing and irrelevant tags, though matrix completion techniques are frequently utilized for this purpose.Autoencoder models have been validated to be effective in learning latent low-dimensional representations in unsupervised learning.In this paper, we propose a new social image understanding model based on deep autoencoders, which can learn the shared latent codes of social images and tags as supervision information in the deep autoencoders.First, social images are extracted with multi-modal features, which provide a comprehensive characterization to image semantic understanding.And, the social image understanding problems are transformed into the problem of minimizing an optimization objective.Second, multi-layered autoencoders with weak supervision integration are employed to learn an efficient low-dimensional representation from the multi-view feature sources that can make up the semantic gap between image features and tags through minimizing the problem formulation.Finally, we design a new balanced loss function based on binary cross entropy, in which we address highly sparse inputs for a better optimization performance.The extensive experiments on several real-world social image datasets confirm the effectiveness and robustness of the proposed model compared with the state-of-the-art methods.
MACO-MOTS: Modified Ant Colony Optimization for Multi Objective Task Scheduling in Cloud Environment

Cloud computing is the development of distributed computing, parallel computing, and grid computing, or defined as a commercial implementation of such computer science concepts.One of the main issues in a cloud computing environment is Task scheduling (TS).In Cloud task scheduling, many Non deterministic Polynomial time-hard optimization problem, and many meta-heuristic (MH) algorithms have been proposed to solve it.A task scheduler should adapt its scheduling strategy to changing environment and variable tasks.This paper amends a cloud task scheduling policy based on Modified Ant Colony Optimization (MACO) algorithm.The main contribution of recommended method is to minimize makespan and to perform Multi Objective Task Scheduling (MOTS) process by assigning pheromone amount relative to corresponding virtual machine efficiency.MACO algorithm improves the performance of task scheduling by reducing makespan and degree of imbalance comparatively lower than a basic ACO algorithm by its multi-objective and deliberate nature.
Exploration of genetic network programming with two-stage reinforcement learning for mobile robot

This paper observes the exploration of Genetic Network Programming Two-Stage Reinforcement Learning for mobile robot navigation. The proposed method aims to observe its exploration when inexperienced environments used in the implementation. In order to deal with this situation, individuals are trained firstly in the training phase, that is, they learn the environment with ϵ-greedy policy and learning rate α parameters. Here, two cases are studied, i.e., case A for low exploration and case B for high exploration. In the implementation, the individuals implemented to get experience and learn a new environment on-line. Then, the performance of learning processes are observed due to the environmental changes.
Metrics for Multiset-Theoretic Subgraphs

We show how to define a plethora of metrics for graphs—either full graphs or subgraphs. The method mainly utilizes the minimal matching between any two multisets of positive real numbers by comparing the multiple edges with respect to their corresponding vertices. In the end of this article, we also demonstrate how to implement these defined metrics with the help of adjacency matrices. These metrics are easy to be manipulated in real applications and could be amended according to different situations. By our metrics, one should be able to compare the distances between graphs, trees, and networks, in particular those with fuzzy properties.
Decisional-DNA Based Smart Production Performance Analysis Model

In order to allocate resources effectively according to the production plan and to reduce disturbances, a framework for smart production performance analysis is proposed in this article. Decisional DNA based knowledge models of engineering objects, processes and factory are developed within the proposed framework. These models are the virtual representation of manufacturing resources, and with help of Internet of Things, are capable of capturing the past experience and formal decisions. A case study for the smart tool performance analysis is presented in which information of key tool parameters like tool life, surface integrity, tool forces and chip formation can be sensed in real-time, and predictions can be made according to specific requirements. This framework is capable of creating a cyber-physical conjoining of the bottom-level manufacturing resources and thus can work as a technological basis for smart factories and Industry 4.0.
Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization

Due to the difficulty in deducing the corresponding relationship between results and parameter settings of multiple phases sectionalized modulation (MPSM) jamming, a problem occurs when obtaining the optimal local suppression jamming effect, which limits the practical application of MPSM jamming. The traditional method struggles to meet the requirements by setting fixed parameters or random parameters. Therefore, an optimization algorithm for MPSM jamming based on particle swarm optimization (PSO) is proposed in this study to produce the optimal local suppression jamming effect and determine its corresponding parameter settings. First, we analyzed the relationship between the degree of mismatch and local suppression jamming effect. Then, we set appropriate fitness function and fitness value. Finally, we used PSO to calculate parameter settings of a section situation and phase situation, which minimizes the fitness function and fitness value. The optimization algorithm avoids the tremendous computation of traversing all parameter settings, is stable, the results are repeatable, and the algorithm provides the optimal local suppression jamming effect under different conditions. The simulation experiments demonstrate the feasibility and effectiveness of the optimization algorithm.
A Graph-Based Reinforcement Learning Method with Converged State Exploration and Exploitation

In any classical value-based reinforcement learning method, an agent, despite of its continuous interactions with the environment, is yet unable to quickly generate a complete and independent description of the entire environment, leaving the learning method to struggle with a difficult dilemma of choosing between the two tasks, namely exploration and exploitation. This problem becomes more pronounced when the agent has to deal with a dynamic environment, of which the configuration and/or parameters are constantly changing. In this paper, this problem is approached by first mapping a reinforcement learning scheme to a directed graph, and the set that contains all the states already explored shall continue to be exploited in the context of such a graph. We have proved that the two tasks of exploration and exploitation eventually converge in the decision-making process, and thus, there is no need to face the exploration vs. exploitation tradeoff as all the existing reinforcement learning methods do. Rather this observation indicates that a reinforcement learning scheme is essentially the same as searching for the shortest path in a dynamic environment, which is readily tackled by a modified Floyd-Warshall algorithm as proposed in the paper. The experimental results have confirmed that the proposed graph-based reinforcement learning algorithm has significantly higher performance than both standard Q-learning algorithm and improved Q-learning algorithm in solving mazes, rendering it an algorithm of choice in applications involving dynamic environments.
Joint detection of malicious domains and infected clients

Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains.
Gathering Evidence from OSINT Sources

In today’s digital age, it is rare to meet a person with an Internet connection who doesn’t also own one or more accounts on different social media sites. People tend to post considerable amounts of personal details on their social media profiles: this includes personal photos, social interactions, and any kind of personal information you can imagine.
Enhancing accuracy of membrane fouling prediction using hybrid machine learning models

Membrane fouling significantly affects membrane performance, but cleaning and replacement schedules are often set at regular time intervals, regardless of the extent of deterioration in performance.The aim of this study is to develop an improved prediction model for membrane fouling in the seawater reverse osmosis (SWRO) process using a hybrid machine learning approach.A Kalman filter (KF) was combined with either an artificial neural network (ANN) or a support vector machine (SVM)-a family of machine learning models-in series.The performance of these integrated models was evaluated with training and testing data sets compiled from the Fujairah SWRO plant over a period of 18 months.Our findings showed that the SVM alone provided, on average, slightly better prediction of membrane resistance (an indirect indicator of membrane fouling) than a single ANN during training and testing sets.However, hybrid machine learning methods consistently outperformed any single model, with the combination of the KF and SVM exhibiting better performance than that of the KF and ANN, except for one special case in which the accuracy of a single SVM already exceeded 0.8 for both Nash-Sutcliffe model efficiency and R 2 .Taken together, our results demonstrated that the hybrid machine learning approach not only enhanced the prediction ability of membrane resistance in classical fouling and machine learning models, but could also be used to adjust cleaning and replacement schedules correctly in response to progressive deterioration in membrane performance during operation.
Relaxing Dense Scatter Plots with Pixel-Based Mappings

Scatter plots are the most commonly employed technique for the visualization of bivariate data. Despite their versatility and expressiveness in showing data aspects, such as clusters, correlations, and outliers, scatter plots face a main problem. For large and dense data, the representation suffers from clutter due to overplotting. This is often partially solved with the use of density plots. Yet, data overlap may occur in certain regions of a scatter or density plot, while other regions may be partially, or even completely empty. Adequate pixel-based techniques can be employed for effectively filling the plotting space, giving an additional notion of the numerosity of data motifs or clusters. We propose the Pixel-Relaxed Scatter Plots, a new and simple variant, to improve the display of dense scatter plots, using pixel-based, space-filling mappings. Our Pixel-Relaxed Scatter Plots make better use of the plotting canvas, while avoiding data overplotting, and optimizing space coverage and insight in the presence and size of data motifs. We have employed different methods to map scatter plot points to pixels and to visually present this mapping. We demonstrate our approach on several synthetic and realistic datasets, and we discuss the suitability of our technique for different tasks. Our conducted user evaluation shows that our Pixel-Relaxed Scatter Plots can be a useful enhancement to traditional scatter plots.
Reduced bases for a three-level atom interacting with a two-mode radiation field

Reduced bases are obtained for a single three-level atom interacting dipolarly with a two-mode electromagnetic field in a cavity. A truncation scheme for the infinite dimensional Hilbert space of the system is proposed, which we take it as the exact ground-state solution. This is used to determine the quantum phase diagram of the atomic $\mathrm{\ensuremath{\Lambda}}$ configuration and to judge the goodness of the reduced bases. This provides us with a mathematical technique that can be used to solve systems where the number of atoms and excitations grow, yielding a Hilbert space with enormous dimensions, more effectively than with the currently available methods. Additionally, the sudden changes suffered by the ground-state solution can be observed through the calculation of the purity and the occupation probabilities.
A COMPARATIVE STUDY OF CROWD COUNTING AND PROFILING THROUGH VISUAL AND NON-VISUAL SENSORS

In this paper we present a comparative critical study of visual and non-visual sensors used in crowd behavior analysis.The understanding of crowd has main impact of the analysis how much they support the system is the key factor of the analysis.The surveillance and security prospects are the main feature for crowd that can make system easy and gives better result.The visual sensors that have been widely used are wireless sensor network, computer vision, smart camera, sensor fusion and few more; and the non-visual sensors are regarding the call, IEEE 802, 11 signals measurement, Smart Evactrack, Social Network and Bluetooth etc.This comparative study identified the different analysis of crowd behavior and after analysis we show which technique is better to another one.The smart devices are used now days for surveillance and gives better result in crowd behavior analysis.
Reliability of geochemical analyses: Deja vu all over again

Today the vast majority of commercial laboratories are accredited. Quality control (QC) results are documented and, upon request, available to the customer. That has led many customers of analytical services to neglect the need for their own, laboratory independent, external QC for large geochemical mapping or monitoring projects. Here, based on recent examples from projects of the Geological Survey of Norway, it is demonstrated how such an external QC procedure should look. Simple graphics are used to visualize QC results. R scripts for producing these graphics are provided. Despite of the laboratories' own internal QC procedures, a number of quality issues like time trends, sample mix-ups, concentration breaks between different batches due to the use of several instruments and excessive rounding of analytical results were detected. Thus, even in these times of accreditation, it is still a necessity to install project-level, laboratory independent, QC procedures in order to produce reliable and comparable datasets.
Bitcoin Movement Prediction Using Sentimental Analysis of Twitter Feeds

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Learning Relevant Models using Symbolic Regression for Automatic Text Summarization

Natural Language Processing (NLP) methods allow us to understand and manipulate natural language text or speech to do useful things. There are several specific techniques in this area, and although new approaches to solving the problems arise, its evaluation remains similar. NLP methods are regularly evaluated by a gold standard, which contains the correct results which must be obtained by a method. In this situation, it is desirable that NLP methods can close as possible to the results of the gold standard being evaluated. One of the most outstanding NLP task is the Automatic Text Summarization (ATS). ATS task consists in reducing the size of a text while preserving their information content. In this paper, a method for describing the ideal behavior (gold standard) of an ATS system, is proposed. The proposed method can obtain models that describe the ideal behavior which is described by the topline. In this work, eight models for ATS are obtained. These models generate better results than other models used in the state-of-the-art on ATS task.
$$\mathsf {WAPS}$$ : Weighted and Projected Sampling

Given a set of constraints F and a user-defined weight function W on the assignment space, the problem of constrained sampling is to sample satisfying assignments of F conditioned on W. Constrained sampling is a fundamental problem with applications in probabilistic reasoning, synthesis, software and hardware testing. Consequently, the problem of sampling has been subject to intense theoretical and practical investigations over the years. Despite such intense investigations, there still remains a gap between theory and practice. In particular, there has been significant progress in the development of sampling techniques when W is a uniform distribution, but such techniques fail to handle general weight functions W. Furthermore, we are, often, interested in $$\varSigma _1^1$$ formulas, i.e., $$G(X):=\,\exists Y F(X, Y)$$ for some F; typically the set of variables Y are introduced as auxiliary variables during encoding of constraints to F. In this context, one wonders whether it is possible to design sampling techniques whose runtime performance is agnostic to the underlying weight distribution and can handle $$\varSigma _1^1$$ formulas? The primary contribution of this work is a novel technique, called $$\mathsf {WAPS}$$ , for sampling over $$\varSigma _1^1$$ whose runtime is agnostic to W. $$\mathsf {WAPS}$$ is based on our recently discovered connection between knowledge compilation and uniform sampling. $$\mathsf {WAPS}$$ proceeds by compiling F into a well studied compiled form, d-DNNF, which allows sampling operations to be conducted in linear time in the size of the compiled form. We demonstrate that $$\mathsf {WAPS}$$ can significantly outperform existing state-of-the-art weighted and projected sampler $$\mathsf {WeightGen}$$ , by up to 3 orders of magnitude in runtime while achieving a geometric speedup of $$296{\times }$$ and solving 564 more instances out of 773. The distribution generated by $$\mathsf {WAPS}$$ is statistically indistinguishable from that generated by an ideal weighted and projected sampler. Furthermore, $$\mathsf {WAPS}$$ is almost oblivious to the number of samples requested.
Theory of Experiential Career Exploration Technology (TECET): Increasing cybersecurity career interest through playable case studies

There is a large demand to fill cybersecurity jobs.To alleviate this need, it is important to generate interest in cybersecurity as a career.One way to do this is through job shadowing and internships.Using design science principles, we have built and tested a playable case study (PCS) where participants can act out a virtual internship and learn relevant cybersecurity skills.We ran a study with students in introductory university courses where they played through a simulated internship at a penetration testing company called CyberMatics.In the study we showed that a PCS format helps students 1) better understand what skills and traits are needed for, 2) more firmly decide whether to pursue, and 3) increase their confidence in their ability to succeed in a career in cybersecurity.Through this study we propose the Theory of Experiential Career Exploration Technology (TECET).
A Study of Continuous Maximum Entropy Deep Inverse Reinforcement Learning

The assumption of IRL is that demonstrations are optimally acting in an environment. In the past, most of the work on IRL needed to calculate optimal policies for different reward functions. However, this requirement is difficult to satisfy in large or continuous state space tasks. Let alone continuous action space. We propose a continuous maximum entropy deep inverse reinforcement learning algorithm for continuous state space and continues action space, which realizes the depth cognition of the environment model by the way of reconstructing the reward function based on the demonstrations, and a hot start mechanism based on demonstrations to make the training process faster and better. We compare this new approach to well-known IRL algorithms using Maximum Entropy IRL, DDPG, hot start DDPG, etc. Empirical results on classical control environments on OpenAI Gym: MountainCarContinues-v0 show that our approach is able to learn policies faster and better.
Secure File Storage on Cloud using Hybrid Cryptography

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
An argumentative approach on abduction and on the weighing of explanatory hypotheses

This paper makes use of the concepts and theoretical framework developed within the field of Argumentation Theory to account for the structure and characteristics of abduction and of the comparative processes of weighing explanatory hypothesis. It elaborates an analysis of abduction based on its consideration as a meta-explanatory argumentation scheme while elucidating its relations with abductive reasoning and inference. The conceptualization of comparative processes of weighing explanatory hypothesis as complex and varied argumentative structures is presented as an alternative to the idea of “inference to the best explanation”.