Reviewerly/exHarmony huggingface dataset:
Random Forest Classification and Regression Models for Literacy Data

In this study we analyzed data provided by the Ministry of Human Resources Development (MHRD), India to develop models for estimation of male, female and Overall literacy rates. This study further examines the district-wise primary and secondary school education data. The data originally consisted of 819 input attributes. We employed exploratory data analysis for the removal of redundant features. We also concatenated some features and we were left with 222 features. The output consisted of male and female literacy rates. We employed the Random Forest regression algorithm for the prediction of these two output variables. Further, We employed proper thresholding to convert these data into classification models using two different classification models. The first one classified data into two groups: males who are literates and illiterates. The second model classified females into literates and illiterates. We employed random forest classifiers for the classification tasks also. Further, we employed Gini importance ranking criteria which are embedded in the random forest algorithm itself for selecting the most informative attributes for both classification and regression tasks. Finally, we carried out multi-label literacy classification for multi-label prediction of literacy rates. We used the binary relevance method for the two-label prediction task. Our prediction algorithms performed well in regression and classification tasks. We also identified top-ranked features having a maximum correlation with the output variables. This characterization provides important domain knowledge along with model interpretability.
User-Oriented Data and Model Privacy Protection Technology

Machine learning algorithms based on deep neural networks have achieved remarkable results and have been widely used in different fields. Currently, it is necessary to fully solve privacy issues in machine learning systems, and be able to efficiently encrypt and decrypt data involved in calculations. This article aims to provide the intersection of these two fields, and highlight the technologies used to protect data and models. It introduces the privacy protection capabilities of homomorphic encryption for smart algorithms and efficient data encryption and decryption algorithms, and the benefits of user-oriented privacy protection technologies. The research has certain reference significance.
Exploration Study of Ensembled Object Detection Models and Hyperparameter Optimization

Object identification models are becoming more accurate as processing capabilities improve. It is our goal to improve the accuracy of object recognition by the use of several ensembles of distinct state-of-the-art object detection models. The use of single architectures and models to handle object detection challenges has been demonstrated in prior studies; however, each model was later shown to have its own bias and variation. “Ensemble Learning” is currently being studied in recent research after the success of fundamental ensembled models like XGBoost. Ensemble learning in object detection is proposed to be expanded through this research by grouping different permutations of existing models to reduce individual bias and variance while improving metrics, accuracy, and gathering metrics that will aid in hyperparameter optimization for future research on object detection ensembles. It took us a while to find a top-performing ensemble for PASCAL VOC problems.
Crosstalk-Computing-Based Gate-Level Reconfigurable Circuits

The functionality of polymorphic circuits can be altered using a control variable. Owing to the multifunctional embodiment in polymorphic circuits, they are helpful to reconfigure circuit behavior from the gate level to the system level, either on the fly or off-line. The polymorphic circuit approaches available in the literature are either based on custom nonlinear circuit designs or special emerging devices, such as ambipolar FET and configurable magnetic devices. While some of these approaches are inefficient in performance, the other approaches involve exotic devices. We have proposed a novel polymorphic circuit design approach based on crosstalk (CT) computing, where we leverage deterministic signal interference between nanometal lines for logic computation and reconfiguration. In this article, we elaborate upon the polymorphic circuit design in CT computing through mathematical formulation, which conveys the rationale to generalize and achieve a wide variety of polymorphic circuits, and then demonstrate a comprehensive list of polymorphic circuit designs. In addition, all circuits are characterized and benchmarked against CMOS circuit implementations to gauge the benefits. Finally, we compare the CT polymorphic circuit approach with other approaches in the literature and highlight its unique features and limitations. The ability to design a wide range of polymorphic logic circuits (basic and complex logics) compact in design and minimal in transistor count is unique to CT computing, which leads to benefits in the circuit power, performance, and area (PPA). Our circuit designs, simulation, and PPA characterization results show that the polymorphic CT circuits provide <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> improvement in transistor count, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> improvement in switching energy, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.5\times $ </tex-math></inline-formula> improvement in speed for polymorphic logic circuits. In the best-case, the transistor count reduction is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> .
Laser Induced Breakdown Spectroscopy (Libs) for Whole Rock Geochemistry

To meet the ever-increasing global demand for metals new mineral deposits need to be discovered. However, as the exploration frontier progressively moves towards deeper buried prospective areas, sampling is becoming costlier and deposit discovery rates are in decline. New technologies and analytical procedures able to deliver fast, reliable, and cheaper samples, analysis, and data, compared to current procedures are therefore, crucial in decreasing the risk of exploration targeting and can represent a step change in mineral exploration. In this study we demonstrate how to emulate laboratory whole-rock geochemical data for major elements (Al, Ca, Fe, K, Mg, Na, Si, and Ti) by averaging LIBS spot analyses performed over single, 1-mm spaced transects along ~1-meter length drill core intervals for chemically, texturally, and mineralogically diverse rocks. Data was collected using a benchtop prototype LIBS instrument that could be reconfigured to fit within a ≥ 75 mm diameter drill hole. The prototype comprises a customised 8 ns pulsed 532 nm Nd:YAG laser with maximum pulse energy of 40 mJ at 5 Hz; two high-resolution spectrometers that together cover a spectral range from 190 to 830 nm, hence allowing all elements to be analysed; a motorised stage; and optics. Estimated LIBS geochemistry shows strong correlations with laboratory whole rock geochemistry for the selected major elements, particularly for Si, Al, and Na, and to a lesser extent K, although the LIBS estimation can breakdown for elements found in low concentrations (e.g. 0.06 wt.%). Factors including the variability in mineralogy and element deportment, or roughness of the analysed surface can result in under- or over-estimation in LIBS whole rock geochemistry. In addition, for more efficient data collection processes, optimal LIBS for whole-rock geochemistry was investigated. Within this dataset from ~30 to ~560 spot analyses per meter are required to emulate whole rock geochemistry with LIBS (e.g. 1% error and 95% confidence). The strongest influence on the optimal number of LIBS analysis required for estimation of whole rock geochemistry is the range of element concentration and to a lesser extent the number of minerals that host an element as well as grain size. The results of this study show promise in development of strategies for rapid whole rock geochemical analysis down a drill hole.
Optimized Fuzzy Hypersphere Neural Network with Online Adaptation Capability

The Fuzzy min-max network (FMN), fuzzy hypersphere neural network classifier (FHSNN), and other FMN variants are sensitive to hyperbox/hypersphere (HB/HS) size or expansion parameter. The neural network of these models needs to be repeatedly trained to find the best value of expansion parameter for which network provides near about full classification accuracy with a minimum number of HBs/HSs. The user-defined expansion parameter ranges between 0 to 1. Hence these models scan input patterns multiple times for various values of the expansion parameter. In addition to it, its value needs to be retuned by retraining the existing patterns while adapting the new input patterns or classes. The paper proposes an Optimized Fuzzy Hypersphere Neural Network Classifier with online adaptation capability (OFHSNNwOC). The designed algorithm trains the network in a single pass that means without scanning the input dataset multiple times. The modifications proposed in the model are eviction of the expansion parameter, calculation of centre points and radii of FHSs in an optimized way, membership functions for various kinds of hyperspheres, and online capability for adaptation of new input patterns or classes in the already built model with no need for retraining. The classifier is assessed and examined utilizing the benchmark datasets.
RESAT: A Utility-Aware Incentive Mechanism-Based Distributed Spatial Cloaking

Distributed spatial cloaking (DSC) enables users to enjoy precise location-based service (LBS) with location privacy preserving. An incentive mechanism is necessary to encourage users to cooperate. However, due to the inappropriate design of incentive mechanisms, the existing works cause low user benefits and fail to encourage users, ruining the expected incentive effect. Moreover, introducing a third party to manage users’ information also causes the existing works to disclose users’ privacy and be unpractical. To address these issues, we propose a utility-aware incentive mechanism-based distributed spatial cloaking (RESAT). By the idea of utility theory and optimization theory, RESAT devises basic and extended incentive mechanisms. The two mechanisms for assuming that all users are honest and that malicious users provide unreasonable locations. RESAT proposes an incentive mechanism-based cloaking cooperation without a third party, incorporating the developed mechanisms based on the blind signature. Theoretical analysis indicates that RESAT achieves incentive compatibility and is secure. Extensive experiments on the real data set show that compared with the existing works, RESAT enables 1 time more users to cooperate at best while eliminating the malicious behaviors that provide unreasonable locations. The required DSC construction time delay is limited.
Multi-task Learning for Automatic Event-Centric Temporal Knowledge Graph Construction

An important aspect of understanding written language is recognising and understanding events described in a document. Each event is usually associated with a specific time or time period when it occurred. Humans naturally understand the time of each event based on our common sense and the relations between the events, expressed in the documents. In our work we will explore and implement a system for automated extraction of temporal relations between the events in a document as well as of additional attributes like date, time, duration etc. for placing the events in time. Our system will use the extracted information to build a graph representing the events seen in a document. We will also combine the temporal knowledge over multiple documents to build a global knowledge base that will serve as a collection of common sense about the temporal aspect of common events, allowing the system to use the gathered knowledge about the events to derive information not explicitly expressed in the document.
FIBI (Fluorescence Imitating Brightfield Imaging) for Slide-Free Rapid Histopathology

We present FIBI (fluorescence imitating brightfield imaging) for creating histology images from unsectioned fresh or fixed tissue specimens. It rapidly creates full-color images using low-cost optics and is suitable for low-resource settings.
Deep Multi View Spatio Temporal Spectral Feature Embedding on Skeletal Sign Language Videos for Recognition

To build a competitive global view from multiple views which will represent all the views within a class label is the primary objective of this work.The first phase involves the extraction of spatio temporal features from videos of skeletal sign language using a 3D convolutional neural network.In phase two, the extracted spatio temporal features are ensembled into a latent low dimensional subspace for embedding in the global view.This is achieved by learning the weights of the linear combination of Laplacian eigenmaps of multiple views.Subsequently, the constructed global view is applied as training data for sign language recognition.
Using ML for Facial Mask Detection

The COVID-19 pandemic has severely impacted our daily lives and continues to spread over the globe. Wearing a protective face mask to prevent the spread of infection has become the accepted norm. Since people have become a it careless towards wearing facial masks outdoors, this system detects the human individual through videos not wearing a mask and informs the authorities about it. This paper proposes a simplified approach to accomplishing this goal utilizing TensorFlow, Keras, and OpenCV, as well as some fundamental Machine Learning packages. We have used the Res10 SSD Caffe model for face detection and Image classification is done through MobileNetV2 architecture for the implementation of our system. By attaining high precision, real-time detection, and classification, the suggested methodology established its effectiveness in identifying facial masks.
A Substitution Box for Lightweight Ciphers to Secure Internet of Things

Internet of Things is a resource-constrained device that demands lightweightcryptography solutions to achieve high performance and optimal security. TheSubstitution Box (S-box) is one of the costly and Nonlinear operations in lightweightciphers. The designing and construction of such S-boxes for the security of theInternet of Things (IoT) is critical. This work proposes a 4-bit, highly NonlinearBijective, Balanced S-box called Feather S-box to enable confusion in lightweightciphers. The hardware performance of Feather S-Box is also analysed in termsof Area and Critical Path-Delay cost. While examining the Area Delay-Productand Power-Delay Product, it shows 23% and 19% lower than PRESENT and12% lesser than GIFT and KATAN. The security analysis of the proposed S-Boxis also done for Nonlinearity, Bijective, Balanced, Space complexity, GlobalAvalanche characteristics, resistance to Algebraic attack, Side-channel attack,Differential and Linear Cryptanalysis. The Feather S-box offers good cryptographicproperties in Nonlinearity and immunity against Algebraic attacks.Moreover, it also exhibits good resistance against Side-channel attacks, Differentialand Linear Cryptanalysis. We also observed that the Feather S-box hasthe highest immunity against Differential and Linear Cryptanalysis except forSKINNY.
Sample-wise dynamic precision quantization for neural network acceleration

Quantization is a well-known method for deep neural networks (DNNs) compression and acceleration. In this work, we propose the Sample-Wise Dynamic Precision (SWDP) quantization scheme, which can switch the bit-width of weights and activations in the model according to the task difficulty of input samples at runtime. Using low-precision networks for easy input images brings advantages in terms of computational and energy efficiency. We also propose an adaptive hardware design for the efficient implementation of our SWDP networks. The experimental results on various networks and datasets demonstrate that our SWDP achieves an average of 3.3× speedup and 3.0× energy saving over the bit-level dynamically composable architecture BitFusion.
Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders

This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders. Missing borders is a common real-world situation, for example, when dealing with the reconstruction of broken artifacts or ruined frescoes. In this particular condition, the puzzle's pieces do not align perfectly due to the borders' gaps; in this situation, the patches' direct match is unfeasible due to the lack of color and line continuations. JiGAN, is a two-steps procedure that tackles this issue: first, we repair the eroded borders with a GAN-based image extension model and measure the alignment affinity between pieces; then, we solve the puzzle with the relaxation labeling algorithm to enforce consistency in pieces positioning, hence, reconstructing the puzzle. We test the method on a large dataset of small puzzles and on three commonly used benchmark datasets to demonstrate the feasibility of the proposed approach.
Improving Accuracy in Facial Detection Using Viola-Jones Algorithm AdaBoost Training Method

Facial detection and recognition is a major problem facing in image processing. Face identity is an important for identifying, searching and authentication purposes. Facial detection is quite common for humans; hence, it is very difficult for the computational device. The human face consists of many complicated multi-dimensions, and it is difficult to identify by a machine. The proposed methodology is used to distinguish human faces in the given multiple images using Viola-Jones algorithm framework and also improving accuracy from the false positive information and true negative information in the given data by using AdaBoost training methods and cascading classification.
Fractal Analysis of RGB Color Images

In the study of natural things with a high degree of complexity, randomness, and irregularity in the colour domain, FD is a helpful attribute. This paper dealt with RGB color texture analysis in terms of box-counting (BC) and differential BC approaches. However, the analysis of RGB color images was evaluated in 5D space and images are normally treated as non-uniform metric space; hence it was very difficult to estimate surface roughness. In this study, we’ve reviewed the various color-based box-counting strategies that are presently available. The primary goal of this study is to provide readers with a general understanding of how various algorithms work, as well as the benefits and drawbacks that can arise as a result on FD of colour domain. At the end we have explained the categorized of various algorithms into group and their details work flow in image analysis and finally indicate why FD yields different results in same kinds of images.
Data Security in Collaborative Business Intelligence for Sustainable Super Smart Society

With research and development in full swing for super smart society, collaborative business intelligence (CBI) has emerged as a key component in the whole ecosystem. Super smart society aims to meet the needs of members of society through the provision of the quality goods and services in adequate amounts to address their distinctive needs based on age, gender, religion or language. This requires highly structured and secure collaborative business intelligence that would analyze data from various elements of super smart society like IoT, AI, and Big data. This chapter aims to explore recent developments in the field of CBI. It further explores information security practices and related mechanisms to ensure safeguarding CBI processes. Finally chapter covers a use case and design of data security framework for CBI that can boost sustainability of super smart society.
A Multi-feature Embedding Method for Robust Image Matching

In this paper, a feature point matching method that integrates both spatial structure and multiple descriptors is proposed. To be specific, given a set of detected keypoints on both images, multiple feature descriptors are extracted at each keypoint. Then, a subspace that simultaneously encodes both spatial structure and multi-feature similarity is computed. In this subspace, two points from different images will be close if their similarity measured by multiple features are high, and two points from the same image will be close if their distance in the original spatial domain is small. The above task is formulated as a Laplacian Embedding problem, which can be solved by eigen decomposition. Finally, vectors in the subspace are treated as new descriptors of the keypoints, and correspondences are established by searching mutual nearest neighbors. Extensive experiments show remarkable improvement in matching accuracy and downstream tasks such as homography and relative pose estimation by combining both structure information and multiple descriptors.
Image Tamper Detection and Reconstruction Based on Compressive Sensing and Reversible Data Hiding

Image data is vulnerable to malicious attacks in the process of transmission, especially the region of interest (ROI) of the image. The existing reversible data hiding authentication algorithm is difficult to recover the tamper area after the original image is tampered. To solve this problem, this paper proposes an image tampering detection and reconstruction algorithm based on compressive sensing and reversible data hiding for tampering detection and reconstruction of regions of interest. Firstly, the image is divided into regions of interest and non-interest, and the regions of interest are divided into blocks. The DCT coefficients of the image blocks in the region of interest are sampled by the compressive sensing algorithm, and the watermark is generated according to the sampled data. At the same time, the non-interested region is adaptively partitioned, and the reversible data hiding algorithm is used to embed the watermark information on different positions of the non-interested region. After the watermark is extracted and decoded by the receiver, the measured value of the carrier image is obtained by block compressive sensing measurement, which can be compared with the extracted watermark to locate tampered area, and the decoded watermark is used for compressive sensing to reconstruct the damaged area. By using this method to experiment with images with different texture features, the results show that the algorithm can not only achieve accurate tampering location, but also can recover tamper images of high quality.
Cryptocurrency Venues: Segmentation, Fees, and Tax Policies

We study interactions between cryptocurrency trading venues, traders, and taxation in which the venues differ in technology (fast vs. slow). The property distinguishing this market from other markets like equities is the fact that each venue clears trades separately from one another. We show that trading fees rise when the venues are technologically differentiated. Improvements in the slow venue’s technology is associated with greater trading volumes, whereas improvements in the fast venue’s speed has an ambiguous impact. When the two venues have similar technologies, differentiation has a positive effect on trading volumes. Our welfare analysis suggests that in equilibrium, the tax rate to optimize tax revenue depends only on trader preferences, and it is thus independent of the venue properties and competition. When the government assigns different weights to its revenue, trader welfare, and venue welfare, the aggregate welfare can have a maximum for nontrivial tax rates.