Reviewerly/exHarmony huggingface dataset:
Geochemistry of Rare Earth Elements in Bedrock and Till, Applied in the Context of Mineral Potential in Sweden

The Rare Earth Element (REE) mineralizations are not so “rare” in Sweden. They normally occur associated and hosted within granitic crystalline bedrock, and in mineral deposits together with other base and trace metals. Major REE-bearing mineral deposit types are the apatite-iron oxide mineralizations in Norrbotten (e.g., Kiruna) and Bergslagen (e.g., Grängesberg) ore regions, the various skarn deposits in Bergslagen (e.g., Riddarhyttan-Norberg belt), hydrothermal deposits (e.g., Olserum, Bastnäs) and alkaline-carbonatite intrusions such as the Norra Kärr complex and Alnö. In this study, analytical data of samples collected from REE mineralizations during the EURARE project are compared with bedrock and till REE geochemistry, both sourced from databases available at the Geological Survey of Sweden. The positive correlation between REE composition in the three geochemical data groups allows better understanding of REE distribution in Sweden, their regional discrimination, and genetic classification. Data provides complementary information about correlation of LREE and HREE in till with REE content in bedrock and mineralization. Application of principal component analysis enables classification of REE mineralizations in relation to their host. These results are useful in the assessment of REE mineral potential in areas where REE mineralizations are poorly explored or even undiscovered.
Designing of Face Recognition System

Face Recognition is the most popular and trending technology in the present era. It is an effective way to provide vision to a machine for better interaction with humans. The way of living will be reflected if machines can read our faces. The face recognition system will move the world in a new dimension. It will be beneficial in many ways to find the identity and security. In this paper, a face recognition system is proposed for advanced applications such as access and security, payments, criminal identifications etc. The process of identification will be based on face recognition which is further divided into three steps: detection of face, extractions of the features and classification, and real time recognition. Detection of face is recognized as the essential step of our system. It is used to extract a face in a frame, which is based on the Viola-Jones object detection algorithm that uses AdaBoost classifier with Haar and LBP features. Local Binary Patterns (LBP) is utilized to extract the unique features of the face like eyes, nose, and mouth in the feature extraction phase. The facial image is correlated with the images available in the database for the classification. The system is implemented in Python using OpenCV library. Kivy is used to create a user interface and also to build executables for different platforms.
An Efficient Hybrid Approach of Attribute Based Encryption For Privacy Preserving Through Horizontally Partitioned Data

In the current scenario, the majority of the applications are having the database and it is increasing day by day. The major problem is how to use those databases by sharing it for the societal benefit. So information security is the real concern while sharing the data with the third party. This article aims to implement cryptography on the horizontally partitioned database. There are number of PPDM techniques. Cryptography techniques [9] are one of the solutions for information securities. But each technique is having its own disadvantages. Means if we use individual encryption technique then hackers may misuse the data by decrypting it. So in this research article hybrid approach is used to secure the data. With the help of horizontally partitioned database table. symmetric and asymmetric techniques are used for the partitioned table. In the experimental result symmetric, an asymmetric and hybrid approach of encryption is discussed. This is an approach of implementing encryption on different attribute and different rows in one data set. The tabular and graphical analysis shown that this approach is comparatively good in PPDM.
TEANS: A Target Enhancement and Attenuated Nonmaximum Suppression Object Detector for Remote Sensing Images

In this letter, we propose an effective approach to learn a convolutional neural network (CNN) model with target enhancement and attenuated nonmaximum suppression (NMS) technique (TEANS) for object detection in optical remote sensing images. TEANS mainly consists of two steps. First, the target enhancement architecture, including target upsampling and reconvolution, is designed into a given deep ResNet-101 model for accurate object detection, especially for small ones. Second, the attenuated NMS technique is used for overcoming wrong eliminations of serried object proposals. For verifying the effectiveness of the TEANS method, evaluations are implemented on a publicly available 15-class optical remote sensing object detection data set. Experimental results show that TEANS can achieve 5.55%, 18.77%, 26.81%, 55.07%, 28.48%, 6.01%, and 5.51% improvements in mean Average Precision (mAP), respectively, compared with standard Faster R-CNN, R-FCN, YOLOv2, SSD, USB-BBR, YOLOv3, and MS-VANs frameworks.
Content-based image retrieval through semantic image segmentation

The term Content-based image retrieval (CBIR) is widely used to describe the process of retrieving desired images from a large database on the basis of features that can be automatically extracted from the images themselves. Earlier CBIR methods were based on extracting the low-level features of the image like shape, color, texture etc. But such systems lacked efficiency because the image concepts were not correctly identified. So there is need for an intelligent system to identify the concepts correctly. Semantic segmentation methods help in this regard by analyzing an image region wise. The introduction of deep learning techniques have brought about a significant performance improvement in semantic image segmentation methods. In this paper DeepLab v3 is used for semantic segmentation and ResNet-34 is used for further classification and image retrieval. Segmentation is performed on PASCAL VOC 2012 dataset, the regions extracted from this is further used for classification. This method provides superior results to other baseline methods.
Quantum Key Distillation Using Binary Frames

We introduce a new integral method for Quantum Key Distribution to perform sifting, reconciliation and amplification processes to establish a cryptographic key through the use of binary matrices called frames which are capable to increase quadratically the secret key rate. Since the eavesdropper has no control on Bob’s double matching detection events, our protocol is not vulnerable to the Intercept and Resend (IR) attack nor the Photon Number Splitting (PNS) attack. The method can be implemented with the usual optical Bennett–Brassard ( B B 84 ) equipment allowing strong pulses in the quantum regime.
Exploring Machine Learning Models to Predict Harmonized System Code

The Harmonized System (HS) Code is widely used across all customs administrations because of the several benefits including a more convenient and easier approach for calculating duties as well as preventing the potential loss of revenue. This paper aims to explore various machine learning models to predict the HS Code based on the customers’ input commodity descriptions. This prediction model helps in reducing the complexity, gaps and many other challenges in using HS Code in any Customs administration. This study follows the Cross-Industry Process for Data Mining methodology which comprises six phases, namely business understanding, data understanding, data preparation, building prediction model, performance evaluation and model deployment. The results of the study indicate that machine learning models are effective tools in predicting HS Code based on user’s inputs. The linear support vector machine model was able to achieve the highest accuracy of 76.3%.
Efficient and secure flash-based gaming CAPTCHA

With the growth of connectivity to smart grids, new applications, and the changing interaction between customer and energy clouds, clouds are more vulnerable to denial-of-service attacks. Efficient detection methods are required to authenticate, detect and control attackers. Completely Automated Public Turing test to tell Computers and Humans Apart, CAPTCHA, is one efficient tool to thwart denial of service attacks. The server presents the user with a client puzzle to solve in order to gain access to the service or website. The puzzle should be hard enough for computers, but easy for humans to solve. Several methods have been suggested including the popular image-based, as well as video-based, and text-based CAPTCHAs. In this paper, we present a new Flash-based gaming CAPTCHA to differentiate bots from humans. We propose a drag and drop client puzzle where the user will play a simple game to answer a visual question. Our method turns out to be convenient, easy for users and challenging for bots. Additionally, it has gaming aspect, which makes it interesting to users of all age groups.
A Research on the Perception and use of Electronic Books Among it Students in Management &amp; Science University

This paper describes a case study based on the usage of e-books amongst Information Technology (IT) undergraduates at an academic library in Malaysia. The study of the utilization of e-books among IT students and identifies all the possible elements that would possibly lead to their e-books usability. It focuses on figuring out the locations and conditions where students access eBooks based on their desire of the use of an eBook or printed book.
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The performance of face recognition (FR) system essentially depends on the availability of training data. With limited training data available, it creates a major challenge in real world applications. This motivates researchers to investigate for such technique which generates optimum results using limited training samples or even single sample per person (SSPP). In this paper, a novel FR method is developed as Constrained L1-Optimal Sparse Representation Technique (L1-CSRT) for SSPP. An optimal sparse representation technique is formulated using the constrained Cuckoo search algorithm (CSA) for estimation of λ coefficients. Further, a novel fitness function is developed based on the L1-norm for better classification accuracy yielding better FR. The motivation behind the optimization of λ coefficients using CSA is to increase the sparsity by a better exploration of search space. The efficiency and accuracy of proposed L1-CSRT is shown based on the extensive simulations carried out on standard databases. The experimental results are compared with existing methods in terms of mean classification error. The performance of L1-CSRT is analysed with an improvement of 2--6% in terms of classification accuracy.
Mamdani fuzzy rule-based models for psychological research

The biasness of the participants in psychological research cannot be ignored during answering various psychological questioners or inventory. Hence, the prediction of psychological parameters can be deemed an ambiguous endeavor and fuzzy modeling provides a mean to account for this ambiguity and uncertainty. In the present study, two fuzzy rule-based models that use single input and generate single output are developed to convert the raw scores of neuroticism and extraversion to standard scores. Maudsley personality inventory (MPI) and Sinha's comprehensive anxiety test (SCAT) were used to collect raw data of neuroticism, extraversion and anxiety from participants. Using the standard scores for neuroticism and extroversion, third fuzzy rule-based model is also developed to predict the anxiety level of the participants. Each model is a collection of fuzzy rules that express the relationship of each input to the output. The performance of all developed models is tested by estimating mean absolute percentage error (MAPE) and paired two-tailed t test.
CREATION: Computational ConstRained Travel Aid for Object Detection in Outdoor eNvironment

In the era of technological advancements, where humans are leveraging it in every sector, it will be highly unfair for the visually impaired to be left off. Hence, this research of developing a compact computational constrained travel aid to help them easily tackle the daily practice is a necessity. As there are approximately 1.3 billion visually impaired people, having a low-cost solution for them is an important requirement. To match this condition, we have used raspberry pi 3 and pi 4 tagged with the pi camera and sonar sensor, all of which are budget-friendly and serves our purpose. This system is attached to a cane and notifies the user about the nature and range of object with the help of sonar sensor and images captured from the pi camera. We have used the lighter versions of You Only Look Once (YOLO) and Single Shot Multibox Detector (SSD) model that has been deployed on both raspberry pi 3 and pi 4. The performance of both versions of raspberry with different models has been tested in an outdoor scenario. It has been observed that raspberry pi 4 is twice faster than pi 3 and the overall performance of Tiny YOLOv3 (Model2) is best in comparison to other models on both pi 3 and pi 4.
Virtual Markers based Facial Emotion Recognition using ELM and PNN Classifiers

Detecting different types of emotional expressions from the subject's face is important for developing intelligent systems for a variety of applications. This present work proposed virtual markers based on Facial emotion expression recognition using the Extreme Learning Machine (ELM) and Probabilistic Neural Network (PNN). A facial emotional expression database is developed with 55 undergraduate university students (male: 35, female: 20) of age range between 20 - 25 years with a mean age of 23.9 years. A HD webcam is used to capture the facial image and Haar Like features and Ada Boost classifier is used to detect the face and eyes through Open CV. A mathematical model based is used to place ten virtual markers called Action Units (AUs) on subjects face at a defined location. Later, Lucas-kanade optical flow algorithm is used to track the marker movement while the subject expressing different emotions and the distance between the center of the face to each marker is used as a feature for classifying emotions. One way Analysis of Variance (ANOVA) is used to test the statistical significance of the features and five fold cross-validation method is used to input the feature for classifiers. In this work, two non-linear classifiers namely, ELM and PNN are used for emotional expression classification. The experimental results give a maximum mean emotion classification rate of 88% and 92% in ELM and PNN classifiers, respectively. Maximum individual class accuracy of happiness - 96%, surprise - 94%, anger- 92%, sadness - 88%, disgust - 90% and fear 89% is achieved using PNN. The experimental results confirm that the proposed system is able to distinguish six different emotional expressions and could be used as a potential tool for a variety of applications which include, e-learning, pain assessment, psychological counseling, human-machine interaction-based applications, etc.
An Efficient Image Encryption Using Chaos Theory

This paper presents the encryption of advanced pictures dependent on turmoil hypothesis. Two principal forms are incorporated into this method those are pixel rearranging and pixel substitution. Disorder hypothesis is a part of science concentrating on the conduct of dynamical frameworks that are profoundly touchy to beginning conditions. A little change influences the framework to carry on totally unique, little changes in the beginning position of a disorganized framework have a major effect inevitably. A key of 128-piece length is created utilizing mayhem hypothesis, and decoding should be possible by utilizing a similar key. The bit-XOR activity is executed between the unique picture and disorder succession x is known as pixel substitution. Pixel rearranging contains push savvy rearranging and section astute rearranging gives extra security to pictures. The proposed strategy for encryption gives greater security to pictures.
A Survey on Various Face detecting and Tracking Techniques in Video Sequences

Face is consider as vital fragment in the human body and human uses the face for identifying people. In video, Face detection is a procedure to detect person's face from a video sequences and Face tracking is the process where we track the person face throughout the video. Face detection and tracking has become a demanding topic in research because of the applications such as video surveillance systems, criminal identification but working in videos is a perplexing task due to the factors like pose variation, illumination, low resolution etc. To achieve the purpose of tracking and detection in videos, it is essential to develop a fair analysis of various techniques used in tracking and detection. As there are various methods along with its advantages, disadvantages, performance and accuracy etc. In this paper, we have done exhaustive survey on the various techniques used till now in face detection and tracking in videos.
Benchmarking Image Retrieval Diversification Techniques for Social Media

Image retrieval has been an active research domain for over 30 years and historically it has focused primarily on precision as an evaluation criterion. Similar to text retrieval, where the number of indexed documents became large and many relevant documents exist, it is of high importance to highlight diversity in the search results to provide better results for the user. The Retrieving Diverse Social Images Task of the MediaEval benchmarking campaign has addressed exactly this challenge of retrieving diverse and relevant results for the past years, specifically in the social media context. Multimodal data (e.g., images, text) was made available to the participants including metadata assigned to the images, user IDs, and precomputed visual and text descriptors. Many teams have participated in the task over the years. The large number of publications employing the data and also citations of the overview articles underline the importance of this topic. In this paper, we introduce these publicly available data resources as well as the evaluation framework, and provide an in-depth analysis of the crucial aspects of social image search diversification, such as the capabilities and the evolution of existing systems. These evaluation resources will help researchers for the coming years in analyzing aspects of multimodal image retrieval and diversity of the search results.
Unsupervised urban scene segmentation via domain adaptation

Image semantic segmentation is a basic and challenging computer vision task, where each pixel in an image is classified into a semantic class. In recent years, deep neural networks have settled many computer vision problems such as semantic segmentation, image classification, object detection. A lot of approaches based on deep neural networks have achieved outstanding performance on different benchmarks. However, most of them are restricted by the scale of elaborate labeled data to train a deep neural network, especially on image semantic segmentation. Collecting pixel-level annotated images is an extremely time-consuming process. Thus, utilization of synthetic data is becoming prevalent. Nevertheless, simply applying the models trained on synthetic data leads to a dramatic performance drop on real images due to the domain shift. In this paper, we propose an adversarial learning method for domain adaptation from the perspectives of both image-level and feature-level. The former adapts images from two domains to appear as if drawn from one domain and the latter attempts to learn similarities between the source and target domains in feature-level common space. To further enhance the adapted model, we introduce a self-training strategy using pseudo-labels. Extensive experiments and ablation studies are conducted under various settings and experiment results indicate our unsupervised method outperforms other state-of-the-art methods.
Development of Predictive Models using Machine Learning Algorithms for Food Adulterants Bacteria Detection

One of the necessities of human to survive is food and meat is one of mainly consumed food by humans. Thus, a level of quality of food is a must to be safely consumed. There have been some cases of adulteration of meats, which can cause harm to consumers. Adulteration can lead to bacteria contamination which are difficult to determine the presence of bacteria without an instrument or food laboratory tests. Nowadays, the idea of applying machine learning in the field of food microbiology is becoming a trend. And one of these applications is on detection and classification of bacteria in food products. Hence, this study aims to apply machine learning algorithms to construct predictive models to detect the presence of bacteria such as Escherichia Coli and Staphylococcus Aureus in raw meat and determine which model is best through accuracy and cross-validation. In this study, five machine learning algorithms are used which are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Naïve-Bayes Classifier (NB), and Artificial Neural Network (ANN). All models are implemented effectively each having an accuracy of 94.97%, 91.84%, 97.57%, 61.46%, and 66.84% respectively. A web application is created using the shiny package in R to attain a standalone application used to show the detected bacteria.
Transition to eBook Provision: A Commentary on the Preferences and Adoption of eBooks by Chemistry Undergraduates

When the word textbook is used, the common image in our minds is that of a physical book, but textbook provision has been impacted by the revolution in digital technology. eBooks are now commonplace in higher education with many university libraries moving to an “e-first” policy. The attitudes of chemistry undergraduates to printed and digital texts were explored using survey data and library analytics. Results suggest that chemistry students have not fully embraced the use of ebooks in their reading, and departments and institutions moving to an e-first policy may benefit from designing class activities which expose students to the ebook service early in their undergraduate programmes.
Occlusion-aware Dynamic Human Emotion Recognition Using Landmark Detection

Human emotion analysis depends upon the accurate identification of key frames and balanced representation of facial features. Accurate identification and tracking of facial features while including variations in pose, face shape, illumination, and image resolution is very much essential for serving the purpose. The complexity improves when the task has to be performed from videos since tracking human emotions frame-wise is cumbersome. Face alignment issues and occlusions need to be taken into account in a robust manner. This paper proposes novel methods based on ACNN for dynamic emotion recognition from human videos.