Reviewerly/exHarmony huggingface dataset:
Texture Feature Analysis for Inter-Frame Video Tampering Detection

Inter-frame video forgeries can involve insertion, deletion, or duplication of frames with malicious intentions. Most of the available passive methods follow a pixel-correlation based approach that is computationally expensive as it compares each pixel of video frames to identify forgery. In this paper, a histogram-based approach is proposed that is computationally efficient and results in better classification accuracy. It computes histograms of frames having texture characteristics encoded with Local Binary Pattern (LBP). Histogram similarity of adjacent LBP coded frames is measured through the Histogram Intersection comparison metric. The differences of these adjacent metric values provide significant cues for forgery detection, that are further normalized and quantized to obtain a fixed-length feature vector. It makes the proposed approach scalable and hence enhances its applicability for variable-length videos. Training and testing are done using SVM classifier with RBF kernel. The method is capable to detect different kinds of interframe forgeries that include insertion, deletion and duplication. Due to lack of benchmark dataset of interframe video forgeries, a customized dataset is prepared through MoviePy tool that comprises total 1370 videos with interframe forgeries (frame deletion, insertion and duplication). Experimental results demonstrate an overall detection accuracy of 99% that can efficiently detect various kinds of inter-frame video forgeries. A comparative analysis with existing interframe forgery detection approaches is also presented.
Digital Literacy of Students of Teacher Training Colleges in Bosnia and Herzegowina—Literature Review and Analysis

This research aims to express an effort to systematically introduce into education, with an emphasis on higher education, a way of teaching digital literacy and training for students of teacher training colleges within the scope of digital competencies. Also, the goal is to study the level of digital literacy and digital competencies of students of teacher training colleges in Bosnia and Herzegovina, based on the analysis of previous research in the past two decades. The analysis presented here was done based on eight scientific research articles in which the research in the field of digital and information literacy was approached. For this research, we used the electronic database search: Central and Eastern European Online Library (CEEOL) and ScienceDirect (Procedia-Social and Behavioral Sciences). The following databases were found, searched, and analyzed within CEEOL: Journal “Education of Adults”; Romanian Journal of Library and Information Science; Proceedings—Association of IT Experts, Librarians, Archivists, and Museologists; Sarajevo Journal of Social Affairs, Faculty of Political Sciences, University of Sarajevo; Journal of the Faculty of Philosophy, University of Zenica, “saZnanje”; Proceedings of the Department of Pedagogy; Proceedings—Social and Behavioral Sciences. A systematic approach was chosen for the analysis of different research perspectives on this topic, as well as for the synthesis of academic literature. Based on the research on Bosnian and Herzegovinian students, which was available, the following was noted: (1) the data show that most teacher training colleges do not develop digital literacy and digital competencies among students; (2) research is dominated by quantitative techniques and methods; (3) research lacks psychometric characteristics except for one research; (4) the level of digital competencies is mainly based on student responses through different types of tests; (5) samples in surveys are mostly not representative; (6) research results indicate that students of teacher training colleges consider digital literacy and competencies important for life in the modern age; (7) the need for research tools that could determine the level of digital literacy and digital competencies was expressed; (8) there is a strong need to modernize access to different digital platforms; (9) most of the research is based on respondents in Sarajevo Canton.
Simulated Kalman Filter with Modified Measurement, Substitution Mutation and Hamming Distance Calculation for Solving Traveling Salesman Problem

Simulated Kalman filter (SKF) is a population-based optimization algorithm based on the Kalman filter framework. Each agent in SKF is treated as a Kalman filter. To find the global optimum, the SKF employs a Kalman filter mechanism that includes prediction, measurement, and estimate. However, the SKF can only operates in numerical search space. In literature, various techniques and modifications have been applied to numerical meta-heuristic algorithms in order to operate in a discrete search space. This paper presents modifications to measurement and estimation in SKF to fit the discrete search space. The modified algorithm is called Discrete Simulated Kalman Filter Optimizer (DSKFO). In solving the Travelling Salesman Problem (TSP), the DSKFO has been compared to four other SKF algorithms and it outperforms all of them.
Applications of Artificial Intelligence in Small- and Medium-Sized Enterprises (SMEs)

The advancements in deep learning methods have brought several new artificial intelligence (AI) applications making AI important for every enterprise that aims to be competitive. Therefore, not only Tech companies but also small- and medium-sized enterprises (SMEs) require AI. This paper discusses SME AI applications and reveals the challenges, solutions, and advantages of implementing AI in SMEs. Although some SMEs are concerned with building their applications because of the cost and length of implementing AI, resulting in a high risk of failure, nevertheless, SMEs still depend on artificial intelligence for growth and cloud-based solutions.
Sentiment of Primary Features in Aspect Based Sentiment Analysis of Hindi Reviews

Aspect Based Sentiment Analysis (ABSA) is a kind of Sentiment classification which allows us to associate sentiments to primary features (Aspect Category) of the object or entity. First, we derive Sentence Vector and sentiment features for a review sentence. Then, we propose algorithms for extracting n-gram features (NGram) and Category Association Word features (CAW). This paper experiments with these four sets of features for building the Ensemble Model for Category-based polarity determination. The result compared with the state-of-the-art reveals that there is improvement in performance for major aspect categories. We also derive an extended Hindi ABSA Category dataset and compare our models’ results. The results show that the accuracy of this subtask ranges from 50% to 76% among four significant domains.
Effect of Competency internal audit staff on Internal Audit Effectiveness in Rwanda’s Public Organization

The aim of this study is all about the assessment of the effect of competency of internal audit staff on internal audit effectiveness in Rwanda's public organizations. The specific objectives of this study were to assess indicators of competency of internal audit staff, to analyze the determinants of internal audit effectiveness, and to assess the correlation between competency of internal audit staff and internal audit effectiveness in KABAYA District Hospital. Research questions: What are the indicators of competency of internal audit staff in KABAYA District Hospital? What are the determinants of internal audit effectiveness in KABAYA District Hospital? Does the competency of internal audit staff collate with the internal audit effectiveness in Rwanda's public organizations? The study adopted a descriptive case study design using both qualitative and quantitative methods. The purposive sampling technique was used to select 20 respondents as the sample size. Primary and secondary data were used and collected using a questionnaire, interviews, and documentary review. Data were analyzed using descriptive statistics presented in the frequency table and percentage. From the finding, this study aims to identify the competence of internal audit staff to improve the Competence of Internal Auditors, Independence and objectivity of an internal audit, Management support for internal audit, and Internal audit quality as the determinants of internal audit effectiveness in KABAYA District Hospital. Therefore, based on the results, it is concluded competency of internal auditors play an important role in the effectiveness of internal audit in KABAYA District Hospital. The correlation coefficient, which shows the relationship between the study variables, from the findings showed that there was a strong positive relationship between the competency of internal auditors and internal audit effectiveness. The study recommends training of internal auditors in the organization as an important tool for motivating internal auditors toward the effectiveness of internal audit
Improved Efficiency of Semantic Segmentation using Pyramid Scene Parsing Deep Learning Network Method

While semantic segmentation is useful for object detection and scene perception, traditional methods have limitations in terms of the level of accuracy details that can be recovered from a given image or scene. A label category can be assigned to each pixel by a deep learning-based semantic segmentation algorithm, which can be developed using the pyramid scene parsing method, which was proposed in this paper. Training and testing experimental results on public datasets were carried out, resulting in high mean accuracy and good intersection over union (IOU).
Modeling and Analysis of Omnidirectional Wheeled Vehicles Using Velocity-Based Impedance Control

This paper presents the velocity-based impedance control that would account for the inertia forces acting on the omnidirectional wheeled vehicle during cornering motions. As favorable omni-vehicle, omnidirectional mecanum wheeled vehicle (OMWV) was selected as a platform in this study. Concerning the problem statements in the dynamic analyses, the control design has considered the difference in vehicle forces because the vehicle's interaction forces were indirectly controlled by the vehicle's velocities. The axial velocities control of the OMWV, vertical and horizontal axial motions on cornering periods were highlighted in this research. The simulation results show that with velocity inputs, the different forces on the OMWV axial motion of the vehicle could be reduced. Furthermore, the reduction in vehicle velocity influenced the overall kinetic energy of the system, which reduced the inertia effect.
Solar Radiation Time-Series Prediction Using Random Forest Algorithm-Based Feature Selection Approach

With the fast expansion of the photovoltaic market, it is critical to accurately anticipate solar radiation. Since solar radiation is highly dependent on environmental conditions, unplanned fluctuations in solar output would raise operational costs. Moreover, the unpredictable output power will be a substantial impediment to introducing this output energy into the grid. This paper aims to develop a suitable and efficient solar power prediction approach that will help to decrease the errors associated with estimating future production. To exploit the unpredictable characteristics of solar power information, this approach is based on combining the Random Forest methodology with feature selection. The initial model takes input temperature, humidity, pressure, wind speed, and wind direction, and outputs solar radiation. The trustworthiness of the proposed models was proved by comparing statistical performance indicators such as MAPE, RMSE.
OntoFusionCrop: An Ontology Centric Approach for Crop Recommendation Based on Bagging and Semantic Alignment

Agriculture is a crucial source of livelihood. There are many farmers who take up farming as their main source of income. The major difficulty present among farmers is that their crop selection is not based on the soil and weather conditions. Thus, it is essential for farmers to know the necessary information regarding the different facets of crop production, best agricultural practices, suitable crops for a specific soil type to be grown in a particular weather, etc. A crop recommender system, which has accurate and precise information, serves as a means for the farmers to farm appropriate crops and get the best yield. This paper proposes an ontology-based recommender system to provide the appropriate details regarding crops and other information associated with them. The proposed OntoFusionCrop uses ontology cluster that includes soil ontology, crop ontology, geographical ontology and agricultural ontology. This strategy uses the crop recommendation dataset from Kaggle which is classified using bagging. The classified instances along with the ontology cluster are semantically aligned using spider monkey optimization algorithm from which we get facts after suitable verification. The query is asked by the user and the recommender system gives the output. The proposed model achieved 93.87% accuracy and A minimum of 0.05 FDR compared to the existing models.
Robust Frequency-Aware Instance Segmentation for Serial Tissue Sections

Serial tissue sections are widely used in imaging large tissue volumes. Navigating to each section is indispensable in the automatic imaging process. Nowadays, the locations of sections are labeled manually or semi-manually. Sections are similar and the border is indiscernible if they stick together, which makes it difficult to locate the sections automatically. In this paper, we present frequency-aware instance segmentation framework (FANet), which can extract shape and size information of sections very well. Firstly, FANet uses discrete cosine transform (DCT) . Secondly, each channel extracts an specific frequency component of themselves. Frequency components from all channels is taken as the multi-frequency description of feature map and finally used to model the channel attention. Additionally, we propose a dataset about the serial sections as benchmark, which contains 2708 images in training set and 1193 images in validation set. Experimental results on the benchmark demonstrate our FANet achieves superior performance compared with the current methods. Our code and dataset will be made public.
Covid-19 Vaccine Sentiment Analysis During Second Wave in India by Transfer Learning Using XLNet

The Covid-19 pandemic has created a world-wide crisis from the perspectives of health and economy. Vaccination is one of the prime means by which herd immunity could be developed. Social media platforms such as Twitter has played a major role in building public opinion as the vaccination drive got underway in several countries. In this paper, we present a tweet-based sentiment analysis of the two popularly administered vaccines in India Covishield and Covaxin during the second wave of the pandemic in India, from March 2021 to September 2021, which was attributed to the Delta mutant of the coronavirus. We use unlabeled Covid-19 vaccine-related tweets downloaded from a large-scale dataset from March 2021 to September 2021, and employ transfer learning for classifying the unlabeled tweets. The contributions of this paper are: - sentiment analysis of unlabeled vaccine-related tweets by training a transformer model on pre-trained XLNet (transformer) features derived from a labeled non-Covid Twitter dataset, a time-line of public sentiments for the two vaccines administered in India, and word clouds of high-frequency adjective unigrams after sentiment analysis, as evidence.
Image Encryption Using RABIN and Elliptic Curve Crypto Systems

In this generation we know how much the internet matters to us. Every basic work can be done with the help of the internet. People have nowadays become very dependent on internet so it is very much needed to provide the users with enough security so that it becomes a reliable platform. This security can be provided by different cryptographic techniques. This makes it difficult for the hackers and the cryptanalyst from stealing crucial informations. This can be done efficiently with the help of asymmetric key and symmetric key cryptographic techniques. We can make a system more safe if we can combine both. So here we have we have used two layers of each encryption and decryption thus providing two layers of security. Here we have worked with different images so firstly, the image has been encrypted using Rabin Cryptography followed by ECC Cryptography. Similarly while decrypting, the decryption was first done using ECC and then Rabin Cryptography. We have tested this approach using different datasets and have received great accuracy. With this approach it can provide images with a double layer of security efficiently while sharing images.
Copy-Move Forgery Detection Using BEBLID Features and DCT

Images can act as a source of information, with the advancement in editing tools anyone can tamper an image effortlessly. Authenticity of the data we get is something very important and hence becomes an essential procedure. A commonly considered image manipulation is to conceal undesirable objects or replace a region with another set of pixels which is copied from the same image’s another region. Existing system uses block-based or keypoint techniques or a combination of both. In the block-based algorithms, images are divided into regular blocks, and the match between every block of the image is found out. Whereas in the keypoint-based method, the interest points in an image are obtained, and the match between those points is found out. Although the keypoint-based method is computationally efficient, it shows less accuracy. The block-based method is whereas computationally expensive. We propose a method by combining the keypoint and block-based methodology in which we split the image into non-overlapping blocks and find the similarity between these blocks using keypoint-based feature extractor BEBLID, and then the block-based technique discrete cosine transformation (DCT) is applied to suspected blocks to detect forgery in copy-move forged images, and also we have compared the performance of boosted efficient binary local image descriptor (BEBLID) with scale-invariant feature transform (SIFT) which is another popular keypoint-based feature extractor.
Gaming Quality of Experience

The goal of this book is to develop a QoE model in the context of cloud gaming services. In this chapter, the background information that is necessary to understand the decisions that are made in each step of the model development is provided. Thus, this chapter starts with an introduction about the cloud gaming technology. Next, the concepts of quality and Quality of Experience are defined. The chapter continues with a short introduction to a gaming QoE taxonomy and relevant influencing factors on cloud gaming quality. Later, the methodology to conduct subjective experiments to assess gaming QoE is described. Finally, the section ends with a short overview of efforts towards the development of gaming quality models in the literature.
UAVs Path Planning by Particle Swarm Optimization Based on Visual-SLAM Algorithm

Intelligent 3-D path planning is a crucial aspect of an unmanned aerial vehicle's (UAVs) autonomous flight system. In this chapter, we propose a two-step centralized system for developing a 3-D path-planning for a swarm of UAVs. We trace the UAV position while simultaneously constructing an incremental and progressive map of the environment using visual simultaneous localization and mapping (V-SLAM) method. We introduce a corner-edge points matching mechanism for stabilizing the V-SLAM system in the least extracted map points. In this instance, a single UAV performs the function using monocular vision for mapping an area of interest. We use the particle swarm optimization (PSO) algorithm to optimize paths for multi-UAVs. We also propose a path updating mechanism based on region sensitivity (RS) to avoid sensitive areas if any hazardous events are detected during the execution of the final path. Moreover, the dynamic fitness function (DFF) is developed to evaluate path planning performance while considering various optimization parameters such as flight risk estimation, energy consumption, and operation completion time. This system achieves high fitness value and safely arrives at the destination while avoiding collisions and restricted areas, which validates the efficiency of proposed PSO-VSLAM system as demonstrated by simulation results.
Certified Computation of Nondeterministic Limits

The computational content of constructive metric completeness is the operator that computes limits of Cauchy sequences. It can be used to construct certified programs that compute interesting transcendental real numbers from sequences of approximations. The desired nondeterministic version of it would be to nondeterministically compute real numbers from nondeterministic approximations. However, it is not obvious how nondeterministic metric completeness should be formalized. We extend previous work on the formalization of exact real computation by primitive properties of nondeterminism. We show that by these properties, various forms of nondeterministic metric completeness can be derived without extending the axiomatic structure of constructive real numbers. We further implement our theory in the Coq proof assistant and use Coq’s code extraction features to extract efficient exact real computation programs using several forms of nondeterministic computation.
Optical Flow Video Frame Interpolation Based MRI Super-Resolution

Flow-based video frame interpolation (VFI) is the process of synthesizing new frames in between the original frames in a video by estimating the motion of pixels. In this paper, usage of flow-based VFI methods has been proposed on 3D MRI images in order to convert low-resolution (LR) images into their high-resolution (HR) counterparts. This paper demonstrates the process of increasing the spatial resolution of the 3D image successively along all three axes to obtain a super-resolution (SR) 3D image using VFI. Quantitative analysis of the results proposes that this method of 3D spatial enhancement outperforms the previously proposed methods in the majority of the circumstances.
TRANSFORMATION OF PERSONALITY IN THE CONDITIONS OF INFORMATION AND DIGITAL ENVIRONMENT OF PROFESSIONAL EDUCATION

The COVID-19 pandemic has become a catalyst for the process of global digitalization in many countries due to the full transition to online learning. This is both a forced and preventive measure for the education system of many states. At present, it can be stated that the pedagogical community is faced with the fact that the formation of instrumental values and education of young people takes place in a virtual environment, and this is a new challenge in pedagogical practice, which required immediate reflection. The article deals with the phenomenon of personality transformation in the information and digital environment of vocational education, which is an integral element of the process of digital transformation of the modern world. It should be added that digital transformation is not only modern technologies, but also the evolution of the individual's thinking in the changing conditions of the new digital-technological environment. The data of the analysis of the phenomenon of digital transformation of personality in the network space were presented. Theoretically, the characteristic features and features of the process of such a personality transformation are singled out. The positive and negative factors of the global digitalization of education are highlighted. Also, the psychological problems of the personality that arise in the conditions of digital transformation, the common feature of which is fear, are revealed. An analysis of these factors allows us to draw the following conclusions: the DH process is neither strictly positive nor strictly negative. This speaks of its naturalness, conformity to nature, i.e. this process, as we undertake to assert, organically fits into the evolution of man.