Reviewerly/exHarmony huggingface dataset:
The Use of Social Media in Intelligence and Its Impact on Police Work

Digital platforms such as Facebook, Twitter, LinkedIn, or Instagram are now among the main tools for personal and professional communications. This has created a new relationship between privacy and visibility, as a large amount of personal information has become publicly accessible. Social media is now an indispensable tool in police work, especially in surveillance and intelligence gathering and when conducting investigations (Fallik et al., International Journal of Police Science & Management, 146135572091194, 2020). This chapter presents a summary of studies that have dealt with the topic of how social media is used in criminal intelligence work, focusing on the impact of social media and the challenges and opportunities associated with it. We look in particular at studies that deal with cases where social media was used to produce criminal intelligence and discuss the implications of the use of SOCMINT (social media intelligence).
RETRACTED: Application of fuzzy clustering of massive scattered point cloud data in English vocabulary analysis

Cloud computing is used to find useful information in large amounts of data. The cloud calculates techniques to enable analysis and English vocabulary analysis to solve the types of research problems. This article describes the analysis of large-scale scattering point cloud data by fuzzy clustering of English vocabulary. English vocabulary analysis is digging into information from a file of English words in the text. It is also called intelligent Knowledge Discovery (KDT). Search for pattern vocabulary information, and extracts from structured and unstructured data, and fuzzy clustering. Starting from the famous fuzzy clustering, which is mainly used for pellets, some methods combine cluster-specific large-scale Massive Scattered in Vocabulary Data Analysis. The cloud-based English vocabulary set is divided into functionally three steps. Preprocessing is based on the first steps of text that give rise to techniques for demising. Before classification, the feature is selected using the Weight-Based Feature Selection (WFS) method. Fuzzy Clustering is used to improve the performance of functional classification by selection based on the proposed classification. It is a classification that is only useful to facilitate an effective classification process for English vocabulary datasets. Of these, Fuzzy Clustering improves classification accuracy and reduces error rates and classification times. The proposed system improves the accuracy of the analysis and reduces the complexity of time.
Demonstration of quantum linear equation solver on the IBM qiskit platform

HHL algorithm is a basic quantum algorithm, which is mainly used to solve linear equations. It has a wide range of potential applications in science, engineering, finance, economics and other fields. Although the current scale of quantum computers is small, we can still demonstrate basic quantum algorithms. In this paper, taking the HHL quantum algorithm as an example, the quantum circuit of the HHL quantum algorithm corresponding to 4 qubits and 7 qubits is given, and the simulation verification is carried out on the IBM qiskit development platform. The results show that when the input matrix is a second-order matrix, the fidelity is very high, but when the input is a fourth-order matrix, the probability that the result is close to the real value is very high only when the sparsity of the matrix is relatively strong. However, when the sparsity of the input matrix is very weak, the result will be very different from the real value.
A Defence Against Input-Agnostic Backdoor Attacks on Deep Neural Networks

Backdoor attacks insert hidden associations or triggers to the deep neural network (DNN) models to override correct inference such as classification. Such attacks perform maliciously according to the attacker-chosen target while behaving normally in the absence of the trigger. These attacks, though new, are rapidly evolving as a realistic attack, and could result in severe consequences, especially considering that backdoor attacks can be inserted in variety of real-world applications. This paper first provides a brief overview of backdoor attacks and then presents a countermeasure, STRong Intentional Perturbation (STRIP). STRIP intentionally perturbs the incoming input, for instance by superimposing various image patterns, and observes the randomness of predicted classes for perturbed inputs from a given deployed model – malicious or benign. STRIP fundamentally relies on the entropy in predicted classes; for example, a low entropy violates the input-dependence property of a benign model and implies the presence of a malicious input. We demonstrate the effectiveness of our method through experiments on two public datasets, MNIST and CIFAR10.
Construction of higher-level MDS matrices in nested SPNs

This paper presents a new construction of large-scale maximum distance separable (MDS) matrices which can be used in the design of block ciphers. We extend Cauchy matrices defined over a finite field and consider these matrices on a finite commutative ring. The resulting generalized Cauchy (GC) matrices retain the advantage of a maximum branch number. We investigate the properties of such matrices in this study. First, we provide a construction of GC matrices. The proposed construction guarantees that most of the (binary) entries in a GC matrix are zero, thereby reducing the search space for cheap GC matrices considerably. Second, we minimize the number of different sub-matrices in a GC matrix to make it more suitable for lookup table implementation, we call this a compact GC matrix. We then elaborate the structure of such compact GC matrices to consider all possible matrices and thus obtain the number of compact GC matrices. Finally, we consider the involutory potential of compact GC matrices. In particular, we prove that any compact GC matrix can be modified into an involutory one through a series of column-exchanging operations and a constant matrix multiplication. Through our experiment, we discover large-scale GC matrices, which can facilitate the future design of involutory component-based ciphers. Our best constructions can reduce the number of XOR gates in the higher-level diffusion layers in Hierocrypt-L1 and Hierocrypt-3 by 35.1% and 50%, respectively, while providing the same number of active S-boxes. Moreover, compared with the existing ones, the involutory nature of the new matrices allows for equally efficient diffusion in both encryption and decryption.
On the Probability of Cross-Radar Assignment Error

If two radar sensors observe the same target their measurements can be combined to produce a fused target-state estimate that is of higher quality than that from one radar alone. If there are multiple targets whose information is shared, a necessary first step to fusion is to “assign” each measurement from the first sensor to that at the other in such a way that both refer to the same underlying object, a task generally accomplished by minimizing a global cost involving distance. An assignment error occurs when the measurement originated by target i at the first radar is wrongly associated to a measurement originated by target j (not i) at the second radar. Naturally, when such an error occurs the result is fusion of information describing disparate objects, resulting in degraded estimation performance and poor self-assessment in terms of posterior uncertainty. Here we address the issue, and derive approximate assignment error probability. Remarkably, performance depends only upon the parameters combined to a single scalar constant.
Project Education and Adams’ Theory of Equity

This work-in-progress paper in research category is concerned with students' perceptions of justice in software engineering group projects. Taking a directed content analysis approach, the aim is to analyze the relevance of Adams' theory of Equity for small-group project education. The analysis was applied to the data collected from a third-year project course in which students encounter an open-ended group assignment. The results indicate that the theory can serve as a useful framework that helps teachers to identify justice-related issues in project courses. On the other hand, it was concluded that special carefulness is advisable considering the simplicity of the theory.
A Novel DIBR 3D Image Hashing Scheme Based on Pixel Grouping and NMF

Most of the traditional 2D image hashing schemes do not take into account the change of viewpoint when constructing the final hash vector. This result in the classification accuracy rate is unsatisfactory when applied for depth-image-based rendering (DBIR) 3D image identification. In this work, pixel grouping based on histogram shape and nonnegative matrix factorization (NMF) are applied to design DIBR 3D image hashing with better robustness resisting to geometric distortions and higher classification accuracy rate for virtual image identification. Experiments show that the proposed hashing is robust against common signal and geometric distortion attacks, such as additive noise, blurring, JPEG compression, scaling, and rotation. Compared with the state-of-art schemes of traditional 2D image hashing, the proposed hashing achieves better performances under above attacks, especially for virtual image identification.
Optimization of software cost estimation model based on biogeography-based optimization algorithm

Estimation of software cost (ESC) is considered a crucial task in the software management life cycle as well as time and quality. Prior to the development of a software project, precise estimations are required in the form of person month and time. In the last few decades, various parametric and non-algorithmic or non-parametric approaches regarding the estimation of software costs have been developed. Among them, the constrictive cost model (COCOMO-II) is a commonly used method for estimating software cost. To further improve the accuracy of this model, researchers and practitioners have applied numerous computational intelligence algorithms to optimize their parameters. However, accuracy is still a big problem in this model to be addressed. In this paper, we proposed a biogeography-based optimization (BBO) method to optimize the current coefficients of COCOMO-II for better estimation of software project cost or effort. The experiments are conducted on two standard data sets: NASA-93 and Turkish Industry software projects. The performance of the proposed algorithm called BBO-COCOMO-II is evaluated by using performance indicators including the manhattan distance (MD) and the mean magnitude of relative error (MMRE). Simulation results reveal that the proposed algorithm obtained high accuracy and significant error minimization compared to original COCOMO-II, particle swarm optimization, genetic algorithm, flower pollination algorithm, and other various baseline cost estimation models.
A Novel Adaptive Directional Interpolation Algorithm for Digital Video Resolution Enhancement

In this paper, a novel digital video resolution enhancement algorithm based on adaptive directional interpolation is proposed, where the directionality of the edge structure and the nonlocal self-similarity prior within the current frame as well as its adjacent frames are both considered. First, we establish the regularization equation that conforms to the prior model of a video frame and then take the classic bicubic interpolation result as the initial estimation to iteratively solve the restoration equation, in which the edge structures and contours in low resolution (LR) input are reconstructed to estimate and refine the desired high resolution (HR) output. Experimental results show that the proposed algorithm can effectively enhance the clarity of a video frame, with satisfying subjective visual quality and PSNR value.
Joint Sparse Principal Component Analysis Based Roust Sparse Fault Detection

In this paper, a novel variant of PCA, joint sparse principal component analysis(JSPCA), is adopted into robust sparse fault detection. By imposing l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> norm jointly on the loss function and the regularization term of traditional sparse PCA, the JSPCA based fault detection method achieves sparse feature selection and robust fault detection simultaneously without high computation cost. The effectiveness of the proposed method is evaluated on the Tennessee Eastman process.
Online Learning Resource Based on One ID Website for All Access (OIAA) as a Student Learning Assistance System

Online learning resources are learning resources that can be accessed online. As the basis for development is the relationship between the vision and mission of the State University of Malang as a reference university and changes to the curriculum at the State University of Malang which refers to life-based learning or a life-based curriculum. Researchers conduct research and development of learning innovation platforms or products in the form of websites as learning resources to become learning companion tools for students of the Special Education Department, Faculty of Education, State University of Malang. The level of feasibility of learning resources to accompany student learning is known through validation by material experts, validation by media experts, and trial use by students. The process of developing website-based online learning resources for students of the Department of Special Education, Faculty of Education, State University of Malang consists of assessment, design, development, implementation, and evaluation. The product result is a websitebased online learning resource. The website offers multimedia facilities with a combination of text, images, and videos as well as reference content in the form of journals and e-books.
An overview of an automated essay grading systems on content and non content based

Abstract Assessment is considered to play an essential function inside the Educational System. The interest of using automatic tools by human beings has been increased. So in the same way student response evaluation in education system with automatic assessment systems has grown exponentially in the last couple of years. Due to the increasing number of students and the use of online MOOC courses and lack of time and lack of consistency, assessment is shifted to automatic assessment. In this regard many of the researchers worked on it to make the assessment process easy. And they succeeded in assessment objective-type questions: i.e. multiple choices. Now here comes an interesting part is to assess the essays with automated tools. In this area, more number of researches worked and invented some tools for grading the essays but not up to the mark. Most of the assessment tools were assigning grades based on the style that is the number of sentences, the number of words, parts of speech, length of an essay, and grammar but not on the content of the essay. But few of the tools grading the essays based on the content by using traditional methods, and very few tools are using natural language processing methods.
Analysis of Cloud Bursting from the Openstack Infrastructure to AWS

Cloud bursting provides enterprises, especially those of small and medium scale, the ability to scale up their own infrastructure using the resource of an external cloud. However, many cloud bursting software are proprietary and strictly dependent on a specific cloud platform. Moreover, the technique remains difficult for its complexity and inconsistency in maintaining its performance especially during the production phase. In this paper, a cloud bursting technique for an Openstack environment to the AWS EC2 platform is conducted using the open-source OMNI module and the results are analyzed. We found that during the migration of many VM instances, the time to synchronize is heavily impacted in contrast to moving a small collection of VMs with larger sizes. It is also found that the utilization of adequate scheduling algorithms can be achieved to enhance the cloud bursting technique, better synchronization time, and lower delay between each separate migration.
Robust tracking of moving objects using thermal camera and speeded up robust features descriptor

Summary Robust methods based on nonlinear influence functions are often used to remove outliers from data. The article describes the design of an algorithm for tracking a moving object in a thermal image using a SURF descriptor and robust Kalman filter. However, given that the main shortfall of robust methods is reduced efficiency, tunable parameters of the robust influence function are used to achieve a balance between robustness and efficiency. The parameters adapt to the observed scene's current conditions, while their estimation is based on the calculated data outlier contamination factor. The outliers that contaminate the data are generally a result of various types of occlusions. The results show that the proposed solution achieves better performance than the standard Kalman filter or fixed‐parameter robust Kalman filter.
Image Categorization Using Agglomerative Clustering Based Smoothed Dirichlet Mixtures

With the rapid growth of multimedia data and the diversity of the available image contents, it becomes necessary to develop advanced machine learning algorithms for the purpose of categorizing and recognizing images. Hierarchical clustering methods have shown promising results in computer vision applications. In this paper, we present a new unsupervised image categorization technique in which we cluster images using an agglomerative hierarchical procedure and a dissimilarity metric is derived based on smoothed Dirichlet (SD) distribution. We propose a mixture of SD distributions and a maximum-likelihood learning framework, from which we derive a Kulback-Leibler divergence between two SD mixture models. Experiments on challenging images dataset that contains different indoor and outdoor places reveal the importance of the hierarchical clustering when categorizing images. The conducted tests prove the robustness of the proposed image categorization approach as compared to the other related-works.
Towards a recommendation system for children's animated movies based on chromatic features

The production of audiovisual content continues to increase, and mainly the content intended for children such as the animated video. However, children are unable to judge the quality, relevance and essentially the adequacy of videos to their ages. In this case, the problem is that many videos can have a negative impact on the psychology and the emotional state of children. Our aim in this work is to identify if an animated video can have a negative impact or not in order to develop a recommendation system. The desired system allows to avoid animated movies causing negative impacts for children. For this, we use a chromatic approach. We propose multi-level descriptors, based on color features, in the judging process. The results of the experimental study of the proposed approach prove its performance and consequently the importance of chromatic features as pertinent features for a recommendation system that aims to reduce the emotional and psychological impacts of children movies and avoid them.
Demonstration-Efficient Inverse Reinforcement Learning in Procedurally Generated Environments

Deep Reinforcement Learning achieves very good results in domains where reward functions can be manually engineered. At the same time, there is growing interest within the community in using games based on Procedurally Content Generation (PCG) as benchmark environments since this type of environment is perfect for studying overfitting and generalization of agents under domain shift. Inverse Reinforcement Learning (IRL) can instead extrapolate reward functions from expert demonstrations, with good results even on high-dimensional problems, however there are no examples of applying these techniques to procedurally-generated environments. This is mostly due to the number of demonstrations needed to find a good reward model. We propose a technique based on Adversarial Inverse Reinforcement Learning which can significantly decrease the need for expert demonstrations in PCG games. Through the use of an environment with a limited set of initial seed levels, plus some modifications to stabilize training, we show that our approach, DE-AIRL, is demonstration-efficient and still able to extrapolate reward functions which generalize to the fully procedural domain. We demonstrate the effectiveness of our technique on two procedural environments, MiniGrid and DeepCrawl, for a variety of tasks.
On revealing shared conceptualization among open datasets

Openness and transparency initiatives are not only milestones of science progress but have also influenced various fields of organization and industry. Under this influence, varieties of government institutions worldwide have published a large number of datasets through open data portals. Government data covers diverse subjects and the scale of available data is growing every year. Published data is expected to be both accessible and discoverable. For these purposes, portals take advantage of metadata accompanying datasets. However, a part of metadata is often missing which decreases users’ ability to obtain the desired information. As the scale of published datasets grows, this problem increases. An approach we describe in this paper is focused towards decreasing this problem by implementing knowledge structures and algorithms capable of proposing the best match for the category where an uncategorized dataset should belong to. By doing so, our aim is twofold: enrich datasets metadata by suggesting an appropriate category and increase its visibility and discoverability. Our approach relies on information regarding open datasets provided by users — dataset description contained within dataset tags. Since dataset tags express low consistency due to their origin, in this paper we will present a method of optimizing their usage through means of semantic similarity measures based on natural language processing mechanisms. Optimization is performed in terms of reducing the number of distinct tag values used for dataset description. Once optimized, dataset tags are used to reveal shared conceptualization originating from their usage by means of Formal Concept Analysis. We will demonstrate the advantage of our proposal by comparing concept lattices generated using Formal Concept Analysis before and after the optimization process and use generated structure as a knowledge base to categorize uncategorized open datasets. Finally, we will present a categorization mechanism based on the generated knowledge base that takes advantage of semantic similarity measures to propose a category suitable for an uncategorized dataset.