Reviewerly/exHarmony huggingface dataset:
Gas Estimation and Optimization for Smart Contracts on Ethereum

When users deploy or invoke smart contracts on Ethereum, a fee is charged for avoiding resource abuse. Metered in gas, the fee is the product of the amount of gas used and the gas price. The more gas used indicates a higher transaction fee. In my doctoral research, we aim to investigate two widely studied issues regarding gas, i.e., gas estimation and gas optimization. The former is to predict gas costs for executing a transactions to avoid out-of-gas exceptions, and the latter is to modify existing contracts to save transaction fee. We target some problems that previous work did not solve: gas estimation for loop functions, and gas optimization for storage usage and arrays. We expect that my research can help Ethereum users avoid economical loss for out-of-gas exceptions and pay less transaction fee.
Blockchain-Based Reliable Traceability System for Telecom Big Data Transactions

Telecom big data generated by telecom networks have a high economic value. Thus, telecom operators actively explore telecom big data transactions methods to minimize the possibility of leaking users’ privacy. The existing solutions do not allow the data sets to leave the database, instead only allow the buyers to send data mining algorithms to the telecom operator’s platform for training. However, this centralized platform has a high risk of tampering. In addition, the currently existing solutions cannot be used to accurately and quickly trace the information of telecom big data transactions. To address these limitations, we propose a blockchain-based reliable traceability system for telecom big data transactions using smart contracts and the InterPlanetary File System. Two types of smart contracts are developed to store transaction information for tracing. Access control strategies and a reapproval prevention strategy are designed for ensuring the safe operation of the system and avoiding the problem of favoritism and fraud. We use Ethereum as a verification platform to develop and evaluate this system. The implementation of functions, such as purchasing data sets, sending algorithms, obtaining results, and tracing transactions in the smart contract and the implementation of the proposed strategies are verified. The results demonstrate that the performance of the proposed system is better than the existing solutions, and the traceability response time is improved to the order of seconds, so as to realize the safe and efficient traceability of telecom big data transactions. In addition, Ethereum and Hyperledger Fabric v0.6 were discussed to provide insights for future development.
Generation of Novelty Ground Truth Image Using Image Classification and Semantic Segmentation for Copy-Move Forgery Detection

Since the ground truth (GT) generated by CNN has pieces of patch information of the learned class, the accurate detection of Copy-Move is ambiguous. With various CNNs for image classification and semantic segmentation, the generated GT images are different yet similar to patch patterns for detecting forgery regions. It is difficult to determine which network model-generated GT image is suitable. Therefore, an optimal GT image is essential in image forensics. The proposed scheme in this paper generates a novelty GT image to solve this problem for the correct detection of Copy-Move forgery. The novelty GT image was configured using image classification and semantic segmentation. The variety of GT images is generated by adopting the state-of-the-art four image classifications and one semantic segmentation in the deep neural network. The proposed scheme implements mainly three tasks: 1) each network model generates the GT images (GT <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">net</i> ), 2) which are convergence synthesized into one (GT <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">conv</i> ), and 3) it decomposed again into GT images (GT <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decomp</i> ) with a threshold value of the ‘Threshold Filter.’ Here, the GTnet images involve two pieces of information about the image classification and semantic segmentation of the forgery image. The GTconv has two pieces of information as one GT image. The GTdecomp is decomposed GTconv into various GT images by the threshold value, which is a permeated degree of the information about ‘Image classification’ and ‘Semantic segmentation.’ The proposed novelty GT image is accomplished with this operational flow for Copy-Move forgery detection. The results confirmed in the experiment for comparing the performance of the existing GT <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">net</i> image, and the GT <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decomp</i> image of the proposed scheme showed that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accuracy</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1 Score</i> of the proposed scheme had the maximum improvement rate of 0.4% and 0.2%, respectively. Also, by estimating the proposed CMFD scheme, Area Under the Curve (AUC) is graded as ‘ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Excellent</i> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> )’ with a value of 0.9 higher.
Traffic Scene Semantic Segmentation by Using Several Deep Convolutional Neural Networks

Nowadays, Intelligent Transportation System (ITS) has become one of the most popular subjects of scientific research. ITS provides innovative services to transport and traffic monitoring like vehicle segmentation and classification. Image understanding and object semantic segmentation in traffic scenes is a very important topic in the trend of autonomous driving issues, like autonomous vehicles and robotics. Actually, Deep Convolution Neural Network (CNN) architectures have given very good results and good performance in computer vision tasks such as classification, object detection, and segmentation. In this scope, we can distinguish two modes of CNN training, the pre-trained model and the training from scratch. In fact, deep neural network training without pre-trained weights and little data requires more training iterations. Besides, deeper models are more efficient than their shallow counterparts for the task of semantic segmentation. In this paper, we will make a comparative study between many deep CNNs models that can train the CNN model from scratch on small datasets, in addition, we will use data augmentation techniques. Extensive experiments were performed on the MiniCity urban scene dataset to prove the effectiveness of the used neural networks while obtaining promising results.
DensE: An enhanced non-commutative representation for knowledge graph embedding with adaptive semantic hierarchy

Capturing the composition patterns of relations is a vital task in knowledge graph completion. It also serves as a fundamental step towards multi-hop reasoning over learned knowledge. Previously, several rotation-based translational methods have been developed to model composite relations using the product of a series of complex-valued diagonal matrices. However, these methods tend to make several oversimplified assumptions on the composite relations, e.g., forcing them to be commutative, independent from entities and lacking semantic hierarchy. To systematically tackle these problems, we have developed a novel knowledge graph embedding method, named DensE, to provide an improved modeling scheme for the complex composition patterns of relations. In particular, our method decomposes each relation into an SO(3) group-based rotation operator and a scaling operator in the three dimensional (3-D) Euclidean space. This design principle leads to several advantages of our method: (1) For composite relations, the corresponding diagonal relation matrices can be non-commutative, reflecting a predominant scenario in real world applications; (2) Our model preserves the natural interaction between relational operations and entity embeddings; (3) The scaling operation provides the modeling power for the intrinsic semantic hierarchical structure of entities; (4) The enhanced expressiveness of DensE is achieved with high computational efficiency in terms of both parameter size and training time; and (5) Modeling entities in Euclidean space instead of quaternion space keeps the direct geometrical interpretations of relational patterns. Experimental results on multiple benchmark knowledge graphs show that DensE is comparable to the current state-of-the-art models for missing link prediction, especially on composite relations. In addition, the interpretations generated by DensE also reveal how relations with distinct patterns (i.e., symmetry/anti-symmetry, inversion and composition) are modeled, which suggests several important directions of future studies.
The effect of economic growth on banking system performance: An interregional and comparative study of Sub-Saharan Africa and developed economies

As an important part of the financial sector, banking systems play a critical role in economic development as well as in improving the quality of life of the people of sub-Saharan Africa (SSA). However, little evidence exists in the literature about the performance of the banking systems in SSA compared with developed economies. This paper investigates the effects of economic development on banking performance across 23 SSA countries and 14 developed countries between 1981 and 2018. Using estimation models such as feasible general least squares, fixed-effects estimation with Driscoll-Kraay standard errors, and system and difference generalized method of moments, our findings show that in SSA as a whole, economic development has a positive impact on commercial bank lending, raises lending rates, increases the amount of domestic credit to the private sector (DCPS), and reduces the number of nonperforming loans (NPLs). In developed countries, economic development has a positive impact on lending rates, DCPS, and the number of NPLs. However, although economic development in developed economies has mostly positive effects on banking performance, SSA countries have more performance issues in banking as a whole and in regional pockets. A careful reassessment of SSA's interregional banking system is therefore highly recommended. An increase in the number of NPLs in East and Southern Africa, a decline in deposit rates offered in Southern, West, and Central Africa, and increasing real interest rates in SSA despite economic growth are some of the concerns that require careful reassessment and policy adjustments.
An efficient approach for detection and classification of cancer regions in cervical images using optimization based CNN classification approach

Cervical cancer can be cured if it is initially screened and giving timely treatment to the patients. This paper proposes an optimization technique for exposing and segmenting the cancer portion in cervical images using transform and windowing technique. The image processing steps are preprocessing, transformation, feature extraction, feature optimization, classification, and segmentation involved in the proposed work. Initially, Gabor transform is enforced on the cervical test image to modify the pixels associated with the spatial domain into multi-resolution domain. Subsequently, the parameters of the multi-level features are extracted from the Gabor transformed cervical image. Then, the extracted features are optimized using the Genetic Algorithm (GA), and the optimistic prominent part is classified by the Convolutional Neural Networks (CNN). Finally, the Finite Segmentation Algorithm (FSA) is used to detect and segment the cancer region in cervical images. The proposed GA based CNN classification method describes the effectual detection and classification of cervical cancer by the parameters such as sensitivity, specificity and accuracy. The experimental results are shown 99.37% of average sensitivity, 98.9% of average specificity and 99.21% of average accuracy, 97.8% of PPV, 91.8% of NPV, 96.8% of FPR and 90.4% of FNR.
Theory of Communication Efficient Quantum Secret Sharing

A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$((k,n))$ </tex-math></inline-formula> quantum threshold secret sharing (QTS) scheme is a quantum cryptographic protocol for sharing a quantum secret among <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> parties such that the secret can be recovered by any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> or more parties while <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k-1$ </tex-math></inline-formula> or fewer parties have no information about the secret. Despite extensive research on these schemes, there has been very little study on optimizing the quantum communication cost during recovery. Recently, we initiated the study of communication efficient quantum threshold secret sharing (CE-QTS) schemes. These schemes reduce the communication complexity in QTS schemes by accessing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d&gt;k$ </tex-math></inline-formula> parties for recovery; here <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> is fixed ahead of encoding the secret. In contrast to the standard QTS schemes which require <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> qudits for recovering each qudit in the secret, these schemes have a lower communication cost of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {d}{d-k+1}$ </tex-math></inline-formula> . In this paper, we further develop the theory of communication efficient quantum threshold schemes. Here, we propose universal CE-QTS schemes which reduce the communication cost for all <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d&gt;k$ </tex-math></inline-formula> simultaneously. We provide a framework based on ramp quantum secret sharing to construct CE-QTS and universal CE-QTS schemes. We give another construction for universal CE-QTS schemes based on Staircase codes. We derived a lower bound on communication complexity and show that our constructions are optimal. Finally, an information theoretic model is developed to analyse CE-QTS schemes and the lower bound on communication complexity is proved again using this model.
CERTbchain: A Step by Step Approach Towards Building A Blockchain based Distributed Appliaction for Certificate Verification System

A certificate is a document that is received once the person completed a particular course. This certificate can be used to get a job or to get admitted to an institute/university of higher studies. Most of the institutes/universities do not have a mechanism in the admission system that can catch forged certificates. Hence, a system that can store certificates on the blockchain and stop the admission forgery through fake certificates is required. The proposed framework is based on blockchain technology that improves security, transparency, etc., and reduces the cost. To overcome the problem of fake certification, a chain of blocks is created for certificates that are immutable as well as transparent amongst all the nodes in the system. This paper presents a step-by-step development process for a certificate verification system using blockchain technology. The key component of this system is a smart contract. A Smart contract Certi has been developed for storing certificates by using a text editor Sublime Text3 and an IDE Remix Ethereum. The validity of the stored certificates can be checked by the employer or admission team at the time of recruitment or admission using Certi smart contract. Blockchain technology possesses its transparency feature hence at the same time the same data on the certificate can be seen by the employer, admission team as well as student itself.
Another Use of the Five-Card Trick: Card-Minimal Secure Three-Input Majority Function Evaluation

Starting from the five-card trick proposed by Den Boer (EUROCRYPT' 89), many card-based protocols performing secure multiparty computations with a deck of physical cards have been devised. However, the five-card trick is considered to be still the most elegant, easy-to-understand and practical protocol, which enables two players to securely evaluate the AND value of their private inputs using five cards. In other words, for more than thirty years, in the research area of card-based cryptography, we have not discovered any protocols that are as simple and beautiful as the five-card trick.
Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias

A targeted and timely treatment can be a beneficial tool for patients with hematological emergencies (particularly acute leukemias). The key challenges in the early diagnosis of leukemias and related hematological disorders are their symptom-sharing nature and prolonged turnaround time as well as the expertise needed in reporting confirmatory tests. The present study made use of the potential morphological and immature fraction-related parameters (research items or cell population data) generated during complete blood cell count (CBC), through artificial intelligence (AI)/machine learning (ML) predictive modeling for early (at the pre-microscopic level) differentiation of various types of leukemias: acute from chronic as well as myeloid from lymphoid. The routine CBC parameters along with research CBC items from a hematology analyzer in the diagnosis of 1577 study subjects with hematological neoplasms were collected. The statistical and data visualization tools, including heat-map and principal component analysis (PCA,) helped in the evaluation of the predictive capacity of research CBC items. Next, research CBC parameter-driven artificial neural network (ANN) predictive modeling was developed to use the hidden trend (disease's signature) by increasing the auguring accuracy of these potential morphometric parameters in differentiation of leukemias. The classical statistics for routine and research CBC parameters showed that as a whole, all study items are significantly deviated among various types of leukemias (study groups). The CPD parameter-driven heat-map gave clustering (separation) of myeloid from lymphoid leukemias, followed by the segregation (nodding) of the acute from the chronic class of that particular lineage. Furthermore, acute promyelocytic leukemia (APML) was also well individuated from other types of acute myeloid leukemia (AML). The PCA plot guided by research CBC items at notable variance vindicated the aforementioned findings of the CPD-driven heat-map. Through training of ANN predictive modeling, the CPD parameters successfully differentiate the chronic myeloid leukemia (CML), AML, APML, acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL), and other related hematological neoplasms with AUC values of 0.937, 0.905, 0.805, 0.829, 0.870, and 0.789, respectively, at an agreeably significant (10.6%) false prediction rate. Overall practical results of using our ANN model were found quite satisfactory with values of 83.1% and 89.4.7% for training and testing datasets, respectively. We proposed that research CBC parameters could potentially be used for early differentiation of leukemias in the hematology-oncology unit. The CPD-driven ANN modeling is a novel practice that substantially strengthens the predictive potential of CPD items, allowing the clinicians to be confident about the typical trend of the "disease fingerprint" shown by these automated potential morphometric items.
Hyperspherical class prototypes for adversarial robustness

This work addresses the problem of adversarial robustness in deep neural network classification from an optimal class boundary estimation perspective. It is argued that increased model robustness to adversarial attacks can be achieved when the feature learning process is monitored by geometrically-inspired optimization criteria. To this end, we propose to learn hyperspherical class prototypes in the neural feature embedding space, along with training the network parameters. Three concurrent optimization functions for the intermediate hidden layer training data activations are devised, requiring items of the same class to be enclosed by the corresponding class prototype boundaries, to have minimum distance from their class prototype vector (i.e., hypersphere center) and to have maximum distance from the remainder hypersphere centers. Our experiments show that training standard classification model architectures with the proposed objectives, significantly increases their robustness to white-box adversarial attacks, without adverse (if not beneficial) effects to their classification accuracy.
Encrypted Information Transmission by Enhanced Steganography and Image Transformation

A deep neural network is used to develop a covert communication and textual data extraction strategy based on steganography and picture compression in such work. The original input textual image and cover image are both pre-processed before the covert text-based pictures are separated and implanted into the least significant bit of the cover object picture element using spatial steganography. Following that, stego-images are compressed and transformed(by using Leh Transformation) to provide a higher-quality image while also saving storage space at the sender's end. After then, the stego-image will be transmitted to the receiver over a communication link. At the receiver's end, steganography and compression are then reversed. This work contains a plethora of issues, making it an intriguing subject to pursue. The most crucial component of this task is choosing the right steganography and picture compression technology. The proposed technology, which combines picture steganography with compression and transformation, delivers higher peak signal-to-noise efficiency.