Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Reduce Video Traffic Based on Understanding User Demand and D2D Communication in 5G Networks

Mobile network customers today prefer video news updates than text. However, the conventional cellular base stations may have challenges in simultaneously managing video transmission for all users, particularly in extensive network deployments. To address the aforementioned challenge discussed in this article, a proposed solution is presented. To lessen the burden of video traffic, we intend to improve the efficiency of D2D multicast network transmission in cellular D2D communications. A scalable video multicast strategy that accounts for user demand and makes use of device-to device (D2D) communication is offered as a means to this end. In the first step, we analyze the users' interests by looking at things like their video viewing habits and criteria like video popularity. The purpose of this research is to ascertain whether or not users are inclined to share movies, which would increase the number of viewers who receive multicast clusters. In the next stage, an algorithm for selecting cluster heads will be developed, taking into account criteria including channel quality, social metrics, and the requirements for video quality. In comparison to similar systems without multiplexing capacity, the proposed method exhibits superiority in three distinct aspects. The initial modification exhibits a surge ranging from 70% to 375 %. Furthermore, the overall growth rate of the entire system ranges from 32% to 64%. Furthermore, it is worth noting that there has been a significant rise of 90% in the number of network recipients, indicating a substantial boost in customer satisfaction.
DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models

Text-to-image generation models that generate images based on prompt descriptions have attracted an increasing amount of attention during the past few months. Despite their encouraging performance, these models raise concerns about the misuse of their generated fake images. To tackle this problem, we pioneer a systematic study on the detection and attribution of fake images generated by text-to-image generation models. Concretely, we first build a machine learning classifier to detect the fake images generated by various text-to-image generation models. We then attribute these fake images to their source models, such that model owners can be held responsible for their models' misuse. We further investigate how prompts that generate fake images affect detection and attribution. We conduct extensive experiments on four popular text-to-image generation models, including DALL·E 2, Stable Diffusion, GLIDE, and Latent Diffusion, and two benchmark prompt-image datasets. Empirical results show that (1) fake images generated by various models can be distinguished from real ones, as there exists a common artifact shared by fake images from different models; (2) fake images can be effectively attributed to their source models, as different models leave unique fingerprints in their generated images; (3) prompts with the "person'' topic or a length between 25 and 75 enable models to generate fake images with higher authenticity. All findings contribute to the community's insight into the threats caused by text-to-image generation models. We appeal to the community's consideration of the counterpart solutions, like ours, against the rapidly-evolving fake image generation.
A Survey on Various Image Inpainting T arious Image Inpainting Techniques echniques

Over the decades' researchers have studied the image inpainting problem intensively due to its high significance and effectiveness in various image processing applications such as people and object security, object removal, face editing applications. Image inpainting is defined as the process of completing or removing a missing region in images. It is considered one of the most challenging topics in the image processing field, although, it requires a deep understanding of the image details in terms of texture and structure. In this paper, a survey of most image inpainting techniques is presented and summarized with comparisons including the merits and demerits of each technique which could help researchers in evaluating their proposed techniques against.
Multiple Source Domain Adaptation for Multiple Object Tracking in Satellite Video

Satellite videos capture the dynamic changes in a large observed sense, which provides an opportunity to track the object trajectories. However, existing multiple object tracking methods require massive video annotations, which is time-consuming and fallible. To alleviate this problem, this paper proposes a Cross-Domain multiple object Tracker (CDTrack) to learn knowledge from multiple source domains. First, a cross-domain object detector with multi-level domain alignment is constructed to learn domain-invariant knowledge between remote sensing images and satellite videos. Second, the proposed method adopts a bidirectional teacher-student framework to fuse multiple source domains. Two teacher-student models learn different domain knowledge and teach mutually each other. With mutual learning, the proposed method alleviates the discrepancies between different domains. Finally, a simple weakly supervised re-identification model is proposed for long-term association. Experimental results on the satellite video datasets demonstrate that the proposed method can achieve great performance without satellite video annotations. The code is available at https://github.com/XiangtaoZheng/CDTrack.
Covering Number as a Complexity Measure for POMDP Planning and Learning

Finding a meaningful way of characterizing the difficulty of partially observable Markov decision processes (POMDPs) is a core theoretical problem in POMDP research. State-space size is often used as a proxy for POMDP difficulty, but it is a weak metric at best. Existing work has shown that the covering number for the reachable belief space, which is a set of belief points that are reachable from the initial belief point, has interesting links with the complexity of POMDP planning, theoretically. In this paper, we present empirical evidence that the covering number for the reachable belief space (or just ``covering number", for brevity) is a far better complexity measure than the state-space size for both planning and learning POMDPs on several small-scale benchmark problems. We connect the covering number to the complexity of learning POMDPs by proposing a provably convergent learning algorithm for POMDPs without reset given knowledge of the covering number.
Procedural Game Adaptation: Framing Experience Management as Changing an MDP

In this paper, we present the Procedural Game Adaptation (PGA) framework, a designer-controlled way to change a game's dynamics during end-user play. We formalize a video game as a Markov Decision Process, and frame the problem as maximizing the reward of a given player by modifying the game's transition function. By learning a model of each player to estimate her rewards, PGA managers can change the game's dynamics in a player-informed way. Following a formal definition of the components of the framework, we illustrate its versatility by using it to represent two existing adaptive systems: PaSSAGE, and Left 4 Dead's AI Director.
Estrategias creativas para la educación emocional

Emotional education is an outstanding place of the actual culture and social needs. Technology, emotion and change are three conceptual keys of our society of information. These are the theoretical referents of this report. From here emphasize emotional view as value to educate. The main goal of the paper is the research done in the outstanding of the emotional dimension (feeling), next to the perception, think, applied, persistence and social understanding as inclination of basic needs for both the students and adults interior life. We delimitate the concept and reach of the emotional education establishing some finalities, we suggest contents and describe concrete strategies such as the ORA process, the cinema, the questioning, the music in order to teacher take it to the practice.
Nonnormal Filtering via Estimating Functions

A result of Godambe (1999) on optimal combination of estimating functions for discrete time stochastic processes, extended to non-orthogonal estimating functions, is applied to nonnormal filtering problems, in which the posterior mode of the signal distribution is efficient. The extensions so obtained may be applicable in a wider context than the standard notions based upon the conditional mean. Recursive formulas are implied, and their properties are considered.
Analysis Of Tablet Pc Based Learning Experiences In Freshman To Junior Level Engineering Courses

Virginia Tech announced a Tablet PC initiative for its engineering program in summer 2006.In fall 2006 Tablet based in and out of class activities were implemented in the freshman engineering program.In fall 2007, Tablet based instruction activities were implemented in upper level engineering courses.Typical examples of Tablet based instruction included electronic note taking, use of inking features to review homework solutions, completing skeleton PowerPoint slides, and setting up online collaboration sessions to do group design project and problem solving activities.In addition, the Tablets helped students maintain an electronic log of their individual and group efforts in completing design projects.A major change in 2007 was the incorporation of DyKnow software into instruction.Examples of students' feedback in support of feedback based instruction and assessment data from in-class polling and an end of semester course exit survey of freshmen are presented to discuss the effectiveness of Tablet based instruction.Also, experiences of engineering freshman in fall 2006 and 2007 are compared to show effectiveness of new implementation strategies adopted in fall 2007.
An escalated convergent firefly algorithm

Firefly algorithm is a powerfulalgorithm, however, it may show inferior convergence rate towards global optimum because the optimization process of the firefly algorithm depends upon on a random quantity that facilitates the fireflies to explore the search space at the cost of the exploitation. As a result, to improve the performance of firefly algorithm in terms of convergence rate even as its ability to exploit the solutions is preserved, firefly algorithm is altered using two different approaches in this research work. In the first approach, a local search strategy, i.e., classical unidimensional local search is employed in the firefly algorithm in order to improve its exploitation capability. In the second approach, a new solution search strategy, i.e., stochastic diffusion scout search is integrated with the searching phase of the firefly algorithm in order to increase the probability of inferior solutions to improve themselves. Both the approaches are evaluated on various benchmark problems having different complexities and their performances are compared with artificial bee colony and classical firefly algorithm. Results prove the capability of the proposed approaches in finding the global optimum in the search space and avoid the local optima at the same time.
Region of Interest (ROI) Based Image Encryption with Sine Map and Lorenz System

In this research work, ROI-based grayscale image encryption with chaos is proposed. First, ROI areas are identified using Sobel edge detection operator and categorized into important and unimportant regions based on number of edges present in the particular block. Next, the important regions are encrypted with Lorenz system (both confusion and diffusion process) and unimportant regions are encrypted using Sine map. Finally, the entire image is shuffled by Lorenz system with new initial conditions to get final encrypted image. The significant advantage of this research work is that important and unimportant regions are encrypted separately with different chaos equation and system which increases the security of the image and also the end user can vary the important regions depends upon the requirements. The experimental results show that the proposed encryption approach provides better results for different cryptographic attacks.
Studying the Effects of Contamination on Soil Properties Using Remote Sensing

The problem of soil contamination is increased recently due to increasing the industrial wastes such as petroleum hydrocarbon, organic solvents, and heavy metals as well as maximizing the use of agricultural fertilizers. During this period, wide development of data collection methods, using remote sensing techniques in the field of soil and environment applications appear and state the suitable technique for remediation. This study deals with the application of remote sensing techniques in geoenvironmental engineering through a field spectral reflectance measurements at nine spots of naturally hydrocarbons contaminated soil in Al-Daura Refinery Company site which is located to the south west of Baghdad using radiometer device to get standard curves of wavelengths and analyzing the satellite imagery of the site to get the spectral reflectance curves using GIS technique and EARDAS software package which help in producing thematic maps for the spatial distribution and concentration of contaminants. The comparison of results showed a good correlation between the spectral reflectance from field measurements and the spectral reflectance obtained from analyzing the satellite imagery. The study also improves a method to save cost, time, efforts and staff.
Direct Trajectory Planning Method Based on IEPSO and Fuzzy Rewards and Punishment Theory for Multi-Degree-of Freedom Manipulators

Manipulator is a kind of commonly used multi-degree-of-freedom nonlinear system.There is a strong coupling between the links, and the movement of each link constraints and affects each other, which increases the difficulty of motion analysis of the system and reduces its trajectory planning efficiency under specific task targets.To solve this problem, a direct trajectory planning method based on an improved particle swarm optimization (PSO) algorithm, called IEPSO, and the fuzzy rewards and punishment theory is proposed in this paper.First, on the basis of preserving the local search ability of PSO, the global search ability of the population is improved by increasing a population exchange item.At the same time, in order to avoid the population falling into the local optimal value, the last elimination principle is incorporated into the standard PSO algorithm.Second, the fuzzy rewards and punishment theory is introduced to reduce the redundant decoupling operation, which can not only ensure the accuracy of manipulator trajectory planning but also effectively reduce the calculation amount of the trajectory planning for the multi-degree-of-freedom manipulator, to improve the optimization efficiency.Finally, the direct trajectory planning method of the multi-degree-of-freedom manipulator is compared and tested.It can be seen that the efficiency scalar and accuracy of the proposed direct trajectory planning method are significantly higher than those of other optimization methods.
Research of Data Sequencesof Groundwater Levels with Gaps

Investigating the regime of groundwater levels is one of the main stages of hydrogeological research, which allows quantitative characterization of the process of formation and change of hydrogeological conditions in time.To date, there has been a problem in filling the series of groundwater levels (GWLs) that have gaps.During the formation of time series of data, there are usually gaps in connection with the change in the technique of observing the GWL, the untimely filling of data rows, the lack of the ability to conduct such observations, and others like that.However, it is not possible to completely exclude rows with spaces, as this may lead to inaccurate data while developing forecast and constructing predictive maps of groundwater levels.Therefore, the task is to compare the existing methods of filling the data series with spaces in order to choose the optimum for further work, with their practical verification.
Classification of Mt Etna (Italy) Volcanic Activity by Machine Learning Approaches

Assessment of the ongoing activity of volcanoes is one of the key factors to reduce volcanic risks. In this paper, two Machine Learning (ML) approaches are presented to classify volcanic activity using multivariate geophysical data, namely the Decision Tree (DT) and K-Nearest Neighbours (KNN). The models were implemented using a data set recorded at Mount Etna (Italy), in the period 01 January 2011 – 31 December 2015, encompassing lava fountain events and intense Strombolian activity. Here a data set consisting of five geophysical features, namely the root-mean-square of seismic tremor (RMS) and its source depth, counts of clustered infrasonic events, radar RMS backscattering power and tilt derivative, was considered. Model performances were assessed by using a set of statistical indices commonly considered for classification approaches. Results show that between the investigated approaches the DT model is the most appropriate for classification of volcano activity and is suitable for early warning systems applications. Furthermore, the comparison with a different classifier approach, reported in literature, based on Bayesian Network (BN), is performed.
Discrimination of Non-Local Correlations

In view of the importance of quantum non-locality in cryptography, quantum computation, and communication complexity, it is crucial to decide whether a given correlation exhibits non-locality or not. As proved by Pitowski, this problem is NP-complete, and is thus computationally intractable unless NP is equal to P. In this paper, we first prove that the Euclidean distance of given correlations from the local polytope can be computed in polynomial time with arbitrary fixed error, granted the access to a certain oracle; namely, given a fixed error, we derive two upper bounds on the running time. The first bound is linear in the number of measurements. The second bound scales with the number of measurements to the sixth power. The former holds only for a very high number of measurements, and is never observed in the performed numerical tests. We, then, introduce a simple algorithm for simulating the oracle. In all of the considered numerical tests, the simulation of the oracle contributes with a multiplicative factor to the overall running time and, thus, does not affect the sixth-power law of the oracle-assisted algorithm.
Readiness and Success of Ubiquitous Learning in Indonesia: Perspectives from the Implementation of a Pilot Project

The implementation of ubiquitous learning (u-learning) is a good opportunity to address the affordability, accessibility, and quality issues of the higher education sector that are present in many countries. However, its broad application is still limited within the instructionist paradigm. Improving its implementation performance is a challenge for researchers and practitioners. This study aimed to understand the readiness and success status of u-learning implementation in Indonesia and to assess factors influencing the status based on stakeholder perspectives. A mixed methods inquiry with the sequential nested strategy was carried out by focusing on a quantitative analysis. The findings revealed that the readiness constructs influence u-learning performance. The study may contribute, theoretically and practically, to future studies, especially u-learning implementation studies, from the perspective of a developing country.
Improving the prediction accuracy in blended learning environment using synthetic minority oversampling technique

Purpose This paper aims to deal with the previously unknown prediction accuracy of students’ activity pattern in a blended learning environment. Design/methodology/approach To extract the most relevant activity feature subset, different feature-selection methods were applied. For different cardinality subsets, classification models were used in the comparison. Findings Experimental evaluation oppose the hypothesis that feature vector dimensionality reduction leads to prediction accuracy increasing. Research limitations/implications Improving prediction accuracy in a described learning environment was based on applying synthetic minority oversampling technique, which had affected results on correlation-based feature-selection method. Originality/value The major contribution of the research is the proposed methodology for selecting the optimal low-cardinal subset of students’ activities and significant prediction accuracy improvement in a blended learning environment.
Related Stocks Selection with Data Collaboration Using Text Mining

We propose an extended scheme for selecting related stocks for themed mutual funds. This scheme was designed to support fund managers who are building themed mutual funds. In our preliminary experiments, building a themed mutual fund was found to be quite difficult. Our scheme is a type of natural language processing method and based on words extracted according to their similarity to a theme using word2vec and our unique similarity based on co-occurrence in company information. We used data including investor relations and official websites as company information data. We also conducted several other experiments, including hyperparameter tuning, in our scheme. The scheme achieved a 172% higher F1 score and 21% higher accuracy than a standard method. Our research also showed the possibility that official websites are not necessary for our scheme, contrary to our preliminary experiments for assessing data collaboration.
Serverless Architectures Review, Future Trend and the Solutions to Open Problems

Lately, the popularity and adoption of serverless computing or Function-as-a-Service have been grown substantially, and it emerges as a better way to manage cost, reliability, availability, and scalability.This paper presents a detail of serverless offerings from leading cloud providers such as AWS, Azure, Google Cloud Platform and some open source.It compares them side by side in the relevant category like compute, storage, database, messaging, API management, and tooling.Also provides comparative analysis on available serverless architectures for the most common use cases within cloud provider's environment.It will also emphasize on benefits, open problems, possible solutions, and the future of the technology.