Reviewerly/exHarmony huggingface dataset:
Forensic cyberpsychology and approaches to criminal profiling

Although there are plenty of theories which consider psychological factors of criminal behaviour and almost every criminal act has a digital aspect, criminal profiling is not so popular among forensic psychologists, especially when it comes to cybercrime. Thus, the goal of this paper is to give a brief overview of literature considering psychological factors related to deviant computer and online behaviour. Two basic approaches of criminal profiling, inductive and deductive, will be presented, as well as their advantages, disadvantages, logic they follow and concepts they rely on when analysing digital evidence in order to narrow suspect pools. Concrete models developed under each approach will be briefly portrayed. Moreover, studies from the field of cyberpsychology will be presented and their relationship with profiling will be discussed, particularly with inductive approach, which is original contribution of this paper. At the very end, suggestions for overcoming limitations of previous studies and converging disciplines of criminal profiling and cyberpsychology will be offered.
Southbound Message Delivery With Virtual Network Topology Awareness in Clouds

Southbound message delivery from the control plane to the data plane is one of the essential issues in multi-tenant clouds. A natural method of southbound message delivery is that the control plane directly communicates with compute nodes in the data plane. However, due to the large number of compute nodes, this method may result in massive control overhead. The Message Queue (MQ) model can solve this challenge by aggregating and distributing messages to queues. Existing MQ-based solutions often perform message aggregation based on the physical network topology, which do not align with the fundamental requirements of southbound message delivery, leading to high message redundancy on compute nodes. To address this issue, we design and implement VITA, the first-of-its-kind work on virtual network topology-aware southbound message delivery. However, it is intractable to optimally deliver southbound messages according to the virtual attributes of messages. Thus, we design two algorithms, submodular-based approximation algorithm and simulated annealing-based algorithm, to solve different scenarios of the problem. Both experiment and simulation results show that VITA can reduce the total traffic amount of redundant messages by 45%-75% and reduce the control overhead by 33%-80% compared with state-of-the-art solutions.
Solving the Real-Time Motion Planning Problem for Non-Holonomic Robots With Collision Avoidance in Dynamic Scenes

Collision-free motion planning allows robots to perform tasks in complex environments, but the conservative bounding curves during collision detection will reduce the robot's dexterity. This paper presents a novel algorithm for real-time motion planning of non-holonomic robots in dynamic scenes. To achieve this, the robot and the obstacles are generally decomposed into a series of superquadric objects. The expanded closed-form Minkowski sums are used to construct the velocity obstacle to deal with the updating orientation of the robot. The algorithm is extended to collision avoidance for different numbers and types of obstacles, which share the same workspace with the robot. We implement our algorithm in various simulations and experiments, where the robot has to avoid collisions with obstacles in real-time. The results demonstrate the effectiveness and efficiency of the proposed algorithm in collision-free motion planning at real-time computation rates and the enhanced dexterity control in complex environments.
Performance Analysis of Image Retrieval Method Using Quantized Bins of Color Histogram

Direct histogram to histogram matching in content-based image retrieval is not proficient due to its large number of bins. The total number of bins of an original histogram represents the large dimensional feature descriptor which requires high computational overhead during the retrieval process. To address this issue, in the proposed scheme image histogram is quantized into a different number of bins which represents the low dimensional feature descriptor effectively. Since, the global and local features play an important role in image retrieval, therefore, considering any single feature for image retrieval is not adequate, so in this paper, a quantized histogram-based global and local features have been considered for feature representation. To avoid variations among the feature components, suitable weights are assigned to the local and global features effectively. To check the efficacy of the proposed method, performance analysis using a different number of bins has been evaluated based on two standard similarity distances for corel-1 K image dataset. The presented work has achieved satisfactory retrieval results in terms of precision, recall, and F-score metrics.
Directional Radial Median-Local Binary Pattern (DRM-LBP): A Noise-invariant Descriptor in Pose Variations

This work introduces novel noise-invariant descriptor (in pose variations) so-called directional radial median-local binary pattern (DRM-LBP). The information pertaining to radial pixels (with respect to eight directions) is utilized for median computation, which is further compared with the mean of those for making DRM-LBP code. The twofold advantage of using this novel concept is: First, the deployment of median filter in radial pixels suppresses the effect of noise, and second, by using mean for comparison, discriminancy is improved. In contrast to center pixel comparison, the mean comparison is more robust in noisy conditions. The DRM-LBP forms histogram size of 256 by computing DRM-LBP code in every position. Then, most relevant features are picked after FLDA reduction, which are classified by SVMs for obtaining results. Experiments on ORL (face) and AMI (ear) datasets prove dominance of DRM-LBP against benchmark methods in noisy conditions. To make noisy images, these are contaminated with Gaussian white noise (GWN) and salt and pepper noise (SPN). On these conditions, DRM-LBP attains staggering accuracy of 95.00 and 81.66% on ORL and 92.33 and 84.33% on AMI. The single sample training size (SSTS) proves well superior against benchmark methods.
A Deep Learning Approach to Image Splicing Using Depth Map

Image splicing is the art of combining portions of two or more images as if the final image looks like an original single photograph. With the advent of high-quality photograph-editing tools, image splicing is so easy and it is becoming increasingly difficult to detect tampering with the naked eye. This limits the value of accepting photographs as proof in a judicial system. So, developing accurate tools and techniques for splice detection and its localization has enormous potential, especially in digital forensics. In this research work, we develop a deep learning system that makes use of depth information to improve the accuracy of splice detection and localization. We show that the developed end-to-end system surpasses the accuracy offered by state-of-the-art networks Xavier-CNN and SRM-CNN in detecting and localizing image splicing.
Automated Resume Classification Using Machine Learning

One of the current job recruiter’s biggest challenges is to filter the right candidate’s resume over the pool of resumes. For a single job post, many times more than thousands of applicants send their resumes. However, many of them are not suitable for the offered job. Manually filtering the right candidate’s resume is not feasible from the pool; hence, an automated system may help pick the selective candidate’s resume by applying natural language processing. This research suggested a machine learning-based automated resume classification model which classifies the resume into different categories based on their content. The experiment is done with the dataset consisting of ten categories of resumes. The outcomes of the proposed model achieve satisfactory classification reports in terms of precision, recall and F1-score with bi-gram model.
Asymmetrically Weighted Cosine Similarity Measure for Recommendation Systems

A recommendation system (RS) provides suggestions about a variety of items to the users according to their interests. RS tries to predict the preference or rating that a user is expected to give to an item. Many techniques/algorithms, such as collaborative filtering-based, content-based, matrix factorization, machine learning, and many more, are used to recommend items to the users. One of the widely used and simple techniques/algorithms is collaborative filtering-based in which similarity between the users (or the items) is determined to make the recommendations. Some of the well-known similarity measures are cosine similarity (CS) and Pearson correlation (PC). However, these measures consider that the similarity between the two users/items is the same in either way. Therefore, in this paper, we propose a new similarity measure, called the asymmetrically weighted CS (AWCS) measure for RSs, which is inspired by the CS measure. We compare its performance with the CS and PC using mean absolute error (MAE), root mean square error (RMSE), and F1 score. In addition to that, we examine the effect of the number of users and multiple thresholds chosen to compute predicted ratings on the performance of the RS.
Active VM Placement Approach Based on Energy Efficiency in Cloud Environment

Due to ever increasing demand for cloud computing, the operational cost of data centers has increased many folds and is of major concern for the cloud providers. The reduction in the energy consumption of the servers and communication networks can significantly improve the overall operational cost of datacenters. The efficient virtual machine (VM) placement can optimize the utilization of server and network resources resulting in energy efficient datacenters. In this paper, an algorithm is proposed for addressing the multi-objective VM placement problem aiming to simultaneously reduce the server and network energy consumption. The simulation results confirm the effectiveness of the proposed approach as compared to other traditional VM placement algorithms in terms of energy efficiency. The result demonstrates that the solutions generated by our proposed algorithm are having energy consumption in the range of 8.6–32.4% which is less than FF (First Fit). Our proposed Algorithm outperforms as compared to other well-known algorithm on average, it achieves around 21.2% energy reduction.
Investigating the Interpretability of ML-Guided Radiological Source Searches

The coupling of reinforcement learning (RL) and deep neural networks (DNN) has demonstrated promising results in many task-oriented scenarios, including radiological source localization. However, these black box approaches present an issue from the user&#x2019;s perspective &#x2013; the non-interpretably of results both during and after task completion. In this work, an RL-based convolutional neural network (CNN) for single-detector, radiological source localization is augmented with a system-feedback strategy which provides users with real-time information on estimated search progress, confidence in search termination, and a projection of future actions. In this Q-learning network, the agent is a single, mobile detector; the environment is a 2D simulated space, including background, attenuating obstacles, and source; the actions are (from a top-down view) &#x007B;left, right, up, down, and stop search&#x007D;. At each step, the agent moves and records a gamma-ray measurement, the search maps are updated, and the CNN is activated, yielding the action with the greatest q-value (reward). In addition, at each step, system-feedback is generated by virtually probing the network at all locations for q-values. The system-feedback sets are: 1) the confidence in taking each action at the given location, 2) a map of future movements, and 3) a map of stopping likelihood. The information these three sets provide helps the user better understand why a given action was taken and what to expect going forward. The combination of confidence measures, count rate, and pathing does provide interpretable information for discerning current and future actions.
Semiclassical Gabor Analysis

The purpose of this chapter is to recast in the spirit of semiclassical analysis some aspects of Gabor analysis that have been discussed in the previous ones, in order to take into account the presence of a small parameter (inspired by the Planck constant) and its asymptotic behaviour.
Blockchain-based solution for Demand Forecasting in Supply chain

Demand forecasting is one of the most important activity for any business, marketing and sales. It is used to forecast the demand of the product based on the historical data to make supply chain decisions effectively. The mutability of historical data results in forecasting errors. Blockchain helps in providing immutable data storage. The data stored over blockchain will be authentic and can be used as trusted source of information. Blockchain-based demand forecasting will help in reducing time in making accurate decisions in the supply chain. This paper finally aims to overview numerous blockchain-based demand forecasting solutions to maximize the efficiency of the supply chain. In order to give a practical exhibition and explanation of how blockchain demand forecasting solutions improve the supply chain, a Proof of Concept (PoC) for a food supply chain scenario is presented.
Compression and Extraction of the Region of Interest from Medical Images through Deep Learning Methods

The objective of any medical image compression strategy include reducing the bit rate and increasing compression efficiency while trying to maintain the quality of diagnostic images. A partial region-based compression method is applied in order to achieve high compression rate and at the same time maintaining the image quality. Most of the medical images contain regions of interest which are critical for making the diagnoses. These regions, however, need to be removed and recreated in high quality. The present paper aims at describing and exploring deep learning techniques which can be used to extract regions of interest in MR brain imaging. The restored and the recreated high quality regions are then compressed through both lossless and lossy techniques.
Comparison and analysis of deep learning for Dehazing

Hazy image is caused by environmental factors such as uneven air light, darkness, contrast, saturation, and attenuation. Due to limited visibility, various applications such as ITS, tracking systems, object detection devices, and others may fail. Image dehazing is used to minimise the effects of weather conditions and improve the pictorial impacts of the image which make post-processing easier. Various approaches are utilized to create high-quality images with improved colour, lighting, and boundaries which are free of halo artifacts. To eliminate the haze, most known de-hazing algorithms employ a conventional scattering model, prior&#x2019;s methods. This research compares a current de-hazing method based on deep learning to existing state-of-the-art technologies.
Review on Metamorphic Cryptography: A Combined Approach of Cryptography &amp; Steganography Techniques

In the current modern era the transmission of any confidential information over any communication network is an important concern. We can secure any message using the concept of cryptography or embedding the private message into any multimedia file like text, audio, image &amp; video. If we use both concepts separately then it is not more effective in the context of security. So, the combination of both concepts means combination of cryptography and steganography provides more security to the confidential message. In this review paper we analyse the several combinations of cryptographic and steganographic algorithms over different multimedia messages.
An enhanced hybrid approach for building of Question Answering Prototype

The technical advancement and massive shift in technology with the introduction of Artificial Intelligence (AI) and Computational Linguistics has appeared Question Answering as one of the prime extents for the computer scientists. Question answering is more focused on Answer Driven Search where, instead of big pack of words, the exact answer is provided to the user&#x2019;s query in minimal amount of time. Changing era has evolved numerous approaches enhanced QA system implementation by exploiting natural language processing (NLP) and computational theory. <sup>[1]</sup> Three mandatory modules in the architecture of QAS includes question processing module, information retrieval (IR) module and answer processing module. In this paper, we purpose an enhanced hybrid approach for the building of QA prototype for factoid type questions by adopting best mathematical methods for QA implementation.
Application of Machine Learning Algorithms in the Development and Consumption Trend of Green and Intelligent Vehicles under the Background of Big Data

In the face of global warming, air pollution, and other difficulties, electric vehicles have become an industry strongly supported by various countries due to their good environmental protection characteristics. In the context of big data, people are exposed to more and more information, and the convenience brought by big data is also increasing. Based on this background, the development of green and intelligent vehicles is getting faster and faster. This paper is aimed at studying the application of machine learning algorithms in the development and consumption trends of green and intelligent vehicles in the context of big data. This paper proposes machine learning algorithms based on big data, as well as support vector machine algorithms and so on. Machine learning algorithms specialize in how computers simulate or implement human learning behaviors to acquire new knowledge or skills and to reorganize existing knowledge structures to continuously improve their performance. The test results of this paper show that, starting from 2014, China has begun to vigorously develop green and intelligent vehicles. In 2014, the production volume of green and intelligent vehicles in China was 3,675, and the sales volume was 2,790. The development of green and intelligent vehicles is not very good and has not been fully accepted by the public. However, since 2017, the production and sales of green and intelligent vehicles have been slowly increasing. By 2020, the production of green and intelligent vehicles will be 24,360 and the sales will be 24,090. It can be seen that with the development of time, green and intelligent vehicles are gradually being recognized.
Virtual Blue Noise Lighting

We introduce virtual blue noise lighting, a rendering pipeline for estimating indirect illumination with a blue noise distribution of virtual lights. Our pipeline is designed for virtual lights with non-uniform emission profiles that are more expensive to store, but required for properly and efficiently handling specular transport. Unlike the typical virtual light placement approaches that traverse light paths from the original light sources, we generate them starting from the camera. This avoids two important problems: wasted memory and computation with fully-occluded virtual lights, and excessive virtual light density around high-probability light paths. In addition, we introduce a parallel and adaptive sample elimination strategy to achieve a blue noise distribution of virtual lights with varying density. This addresses the third problem of virtual light placement by ensuring that they are not placed too close to each other, providing better coverage of the (indirectly) visible surfaces and further improving the quality of the final lighting estimation. For computing the virtual light emission profiles, we present a photon splitting technique that allows efficiently using a large number of photons, as it does not require storing them. During lighting estimation, our method allows using both global power-based and local BSDF important sampling techniques, combined via multiple importance sampling. In addition, we present an adaptive path extension method that avoids sampling nearby virtual lights for reducing the estimation error. We show that our method significantly outperforms path tracing and prior work in virtual lights in terms of both performance and image quality, producing a fast but biased estimate of global illumination.
Implementasi Gerakan Literasi Sekolah dalam Menumbuhkan Minat Membaca dan Menulis Pada Pembelajaran PAI

Education is basically to form and create quality human resources, but the problem that exists in the world of education is the low quality of education at every level and unit of education. So in this world of education the importance of literacy as an effort to build a generation of people with broad insight and virtuous character.&#x0D; The purpose of this study was to determine the various kinds of school literacy programs in fostering interest in reading and writing in students. To answer various problems how to implement, and what are the population factors and share barriers to the implementation of the school literacy movement at SDN Baujeng 1. The approach used is descriptive qualitative method.&#x0D; The results showed that implementation was realized through the implementation of reading activities, the supporting factor was the active role of students and the inhibiting factor, namely: the condition of the book that was not feasible. The solution to overcome the implementation problem is to create a reading study group and provide proper facilities, as well as provide a reading corner room, providing motivation to students at SDN Baujeng 1.
PENGARUH STRATEGI PEMBELAJARAN INQUIRY LEARNING TERHADAP HASIL BELAJAR PAI SISWA KELAS VIII DI SMPN 02 PURWOSARI

This thesis discusses the "influence of inquiry learning strategies in PAI lessons on student learning outcomes at SMPN 02 Purwosari. subproblem 1. How is the application of inquiry learning strategy in PAI lessons at SMPN 02 Purwosari? 2.Is there any effect of inquiry learning strategy on PAI learning outcomes for SMPN 02 Purwosari students?. In addition, the purpose of this study was to describe the effect of inquiry learning strategies on PAI learning outcomes for class VIII C students of SMPN 02 Purwosari.&#x0D; The approach used is a quantitative approach using statistical analysis. the type of research used is Ex Post Facto. The population in this study were all students of SMPN 02 Purwosari, totaling 213 students. The sample taken is class VIII C, which consists of 32 students. The technique used is purposive sampling technique. The research instrument used a questionnaire and documentation. The collected data was processed using descriptive statistical analysis and inferential analysis with simple regression techniques.&#x0D; The results of descriptive statistical analysis of the average score of variable X regarding the application of inquiry learning strategies in PAI learning are 79 very high categories because they are in the interval (78-83). This means that the application of inquiry learning strategies in PAI learning is included in very high qualifications.&#x0D; The results of descriptive statistical analysis obtained the average score (mean) of the Y variable regarding PAI learning outcomes for students at SMPN 02 Purwosari is 82 with a very high category because it is in the interval (82-89). This means that the average PAI learning outcomes of SMPN 02 Purwosari students are included in very high qualifications.