Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Financial Smart Contract Services on Blockchain

Blockchain technology is disrupting current business models and practices by making intermediary services obsolete and the term has become a buzzword worldwide. One particular area based on the distributed ledger technology that has grabbed the attention of many financial businesses is "Smart Contracts". A lot of research is going on to harness its full potential in many fronts from small to large financial businesses. Currently, the Collateral Smart Contract Services (CSCS) are manually processed in financial institutions. The main objective of this research is to help financial institutions in choosing the most appropriate financial instrument for CSCS and allow its automated trading using blockchain technology, which we have achieved on one of the prominent blockchain platforms. We have designed a Chaincode for CSCS using Hyperledger Fabric, maintaining a transparent distributed ledger for the CSCS between financial institutions. The clients in the network are permissioned and possess certificates that they utilize to interact with the Hyperledger Fabric network. Go programming language has been used to create contracts on Hyperledger Fabric. One of the main challenges in the implementation has been to identify different elements that are required to get the network and chaincode running on the Hyperledger. We have designed a network consisting of multiple organizations each one connecting with other organizations for the Hyperledger transactions, for achieving collateral services among these institutions. The experimental evaluation criteria designed for this study could be used as formal procedure for designing financial smart contracts on Hyperledger.
Smart Health Model With A Linear Integer Programming Approach

Abstract The concept of intelligent health is part of the realization of a smart city. Most hospitals experience shortages in the availability of resources, such as the lack of truly professional and qualified personnel in the health care field, limitations on hospital facilities and infrastructure. Capacity planning at the hospital is done by ensuring a balance between quality given to health care provided and the costs provided. The measuring instrument used in hospital capacity planning is to calculate the availability of resources at the hospital. In general, the capacity model is intended to calculate the amount of resources needed to be able to serve patients’ needs quickly. The problem that is the main description of providing optimal health services based on the concept of “smart health” is how to provide services as early as possible with limited use of health resources. The main assumptions of modeling are only one type of health service can be met by each patient at a time, the number of medical staff is sufficient to fulfill each request, and the type of disease experienced by patients can be handled by available medical staff. The model produced in this study is expected to minimize travel costs, service costs and other costs arising from inaccuracies in providing services to patients.
The Use of Genetic Algorithms for Distribution of Tasks in Groups of Mobile Robots with Minimization of Energy Consumption

We consider one of the options for implementing the system, which distributes tasks assigned to a group of mobile robots, taking into account the initial charge of the battery, the power consumption of each robot, and the energy costs of performing individual tasks. The optimal distribution algorithm is implemented on the basis of evolutionary computations, while the formation of chromosomes also takes into account the current mode of operation of the robot (sleep, movement, task execution) and the class (complexity) of the task being performed. The proposed algorithm makes it possible to effectively solve a multicriteria optimization problem taking into account timeconsuming costs and an integral criterion for the effectiveness of robots, formed from estimates of battery charge, energy costs for performing individual tasks, maintaining in working condition and functionality of each robot.
EMCM: A Novel Binary Edge-Feature-Based Maximum Clique Framework for Multispectral Image Matching

Seeking reliable correspondence between multispectral images is a fundamental and important task in computer vision. To overcome the nonlinearity problem occurring in multispectral image matching, a novel, edge-feature-based maximum clique-matching frame (EMCM) is proposed, which contains three main parts: (1) a novel strong edge binary feature descriptor, (2) a new correspondence-ranking algorithm based on keypoint distinctiveness analysis algorithms in the feature space of the graph, and (3) a false match removal algorithm based on maximum clique searching in the correspondence space of the graph considering both position and angle consistency. Extensive experiments are conducted on two standard multispectral image datasets with respect to the three parts. The feature-matching experiments suggest that the proposed feature descriptor is of high descriptiveness, robustness, and efficiency. The correspondence-ranking experiments validate the superiority of our correspondences-ranking algorithm over the nearest neighbor algorithm, and the coarse registration experiments show the robustness of EMCM with varied interferences.
Genetic Algorithm and Chaotic Maps based Visually Meaningful Image Encryption

The security of data is the main challenge these days. The more robust and efficient encryption algorithm is a need for the emerging technology. The combination of a genetic algorithm and chaotic maps are used in this work to encrypt the digital image. The best encrypted image among generations in this algorithm is created by the genetic algorithm. It is the one optimized with lowest correlation coefficient. This image is hidden into a reference image using DWT(Discrete Wavelet Transform) transform creating a visually meaningful encrypted image. This is an unique way that genetic algorithm is used in generating an encrypted image and making it a visually meaningful encrypted image. The genetic algorithm has given a better security analysis results to the proposed algorithm. It has given high resistance to the attacks and does not allow the data accessible to the unauthorized people. This makes the proposed method useful to transmit data or store data in a more secure way. The results for statistical analysis and differential analysis are evaluated and excellent performance of these parameters for the algorithm can be seen.
Computing with a camera

Photonic computing devices are a compelling alternative to conventional computing setups for machine learning applications, as they are nonlinear, fast and easy to parallelize. Recent work demonstrates the potential of these optical systems to process and classify human motion from video.
IoU-Related Arbitrary Shape Text Scoring Detector

As a medium of information transmission, text is widely existed in natural scenes, showing diversity in orientation, scale, font, color and shape. Accurate detection of scene text is a prerequisite for subsequent recognition. Though many previous methods have worked well on horizontal and multi-oriented text detection datasets, detecting arbitrary shape scene text still remains as a challenging problem. To solve the problem, this paper proposes an arbitrary shape scene text detection method. Based on Mask R-CNN, our method replaces the original ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -smooth loss with the proposed IoU-related loss and adds a text scoring branch to align the confidence score with the text mask IoU to make the model highly relevant to IoU, achieving the goal of improving detection performance by improving IoU directly in a simple but effective way. The proposed method is evaluated on four public datasets: CTW-1500, Total-Text, ICDAR2015 and ICDAR2017-RCTW. For curved text detection datasets CTW-1500 and Total-Text, we have reached 79.2% and 81.1% H-mean respectively, showing that the proposed method has achieved competitive performance in arbitrary scene text detection.
Using empirical studies to mitigate symbol overload in iStar extensions

Modelling languages are frequently extended to include new constructs to be used together with the original syntax. New constructs may be proposed by adding textual information, such as UML stereotypes, or by creating new graphical representations. Thus, these new symbols need to be expressive and proposed in a careful way to increase the extension’s adoption. A method to create symbols for the original constructs of a modelling language was proposed and has been used to create the symbols when a new modelling language is designed. We argue this method can be used to recommend new symbols for the extension’s constructs. However, it is necessary to make some adjustments since the new symbols will be used with the existing constructs of the modelling language original syntax. In this paper, we analyse the usage of this adapted method to propose symbols to mitigate the occurrence of overloaded symbols in the existing iStar extensions. We analysed the existing iStar extensions in an SLR and identified the occurrence of symbol overload among the existing constructs. We identified a set of fifteen overloaded symbols in existing iStar extensions. We used these concepts with symbol overload in a multi-stage experiment that involved users in the visual notation design process. The study involved 262 participants, and its results revealed that most of the new graphical representations were better than those proposed by the extensions, with regard to semantic transparency. Thus, the new representations can be used to mitigate this kind of conflict in iStar extensions. Our results suggest that next extension efforts should consider user-generated notation design techniques in order to increase the semantic transparency.
Research on partitioning algorithm based on RDF graph

Summary With the increasing size of the RDF dataset, the application of parallel computing frameworks is applying more and more widely in big data. In order to realize the parallel query of data, an edge partitioning algorithm is proposed based on RDF sentence graph. Firstly, the algorithm transforms the RDF graph into an RDF sentence graph using coarsening and partitioning. Secondly, the minimum degree vertex partitioning algorithm is put forward to partition the RDF sentence graph. Finally, according to the equivalence thought between edge segmentation of RDF sentence and vertex segmentation of RDF graph, the intersection between RDF subgraphs is the vertex cut set of RDF graph to achieve parallel query of RDF data. The experimental results show that the algorithm's partitioning time and efficiency are better than the traditional algorithms.
Blockchain‐based access control for enterprise blockchain applications

Summary Access control is one of the fundamental security mechanisms of IT systems. Most existing access control schemes rely on a centralized party to manage and enforce access control policies. As blockchain technologies, especially permissioned networks, find more applicability beyond cryptocurrencies in enterprise solutions, it is expected that the security requirements will increase. Therefore, it is necessary to develop an access control system that works in a decentralized environment without compromising the unique features of a blockchain. A straightforward method to support access control is to deploy a firewall in front of the enterprise blockchain application. However, this approach does not take advantage of the desirable features of blockchain. In order to address these concerns, we propose a novel blockchain‐based access control scheme, which keeps the decentralization feature for access control–related operations. The newly proposed system also provides the capability to protect user's privacy by leveraging ring signature. We implement a prototype of the scheme using Hyperledger Fabric and assess its performance to show that it is practical for real‐world applications.
Improving Response Time Prediction for Stack Overflow Questions

This paper proposes a new machine learning model to predict the response times for questions posted on the Developer Q&A community website known as Stack Overflow. The paper first presents a comparative analysis of similar work done in the literature. Then, the paper presents a new problem formulation that represents the prediction problem accurately. Our proposed problem formulation focuses on whether or not a question will receive an answer within a certain time frame, rather than predicting a specific interval of time within which an answer should be received. The paper also introduces a new set of features that help developers better characterize the properties of posted questions. The paper apply feature engineering techniques on the raw questions posted publicly on Stack Overflow over the period of three months and addresses the problem of imbalanced dataset. The paper then uses the generated dataset to train classification models using different machine learning algorithms and evaluates the accuracy of each algorithm using cross-validation methods. Our proposed model was able to achieve a classification accuracy of 63% using feature sets extracted from the question header only.
Regularizing activations in neural networks via distribution matching with the Wassertein metric

Regularization and normalization have become an indispensable component in deep learning because it enables faster training and improved generalization performance. We propose the projected error function regularization loss (PER) that encourages activations to follow the standard normal distribution. PER randomly projects activations to one dimensional space and computes the regularization in the projected space. PER acts like the Pseudo-Huber loss in the projected space, enabling robust regularization for training deep neural networks. In addition, PER can capture interaction between hidden units by projection vector drawn from unit sphere. By doing so, PER minimizes the upper bound of the Wasserstein distance of order one between an empirical distribution of activations and the standard normal distribution. To the best of the authors' knowledge, this is the first work to regularize activations concerning the target distribution in the probability distribution space. We evaluate the proposed method on image classification task and word-level language modeling task.
Dynamic Motion Planning for Conducting Obstacle Avoidance Maneuver of Fixed Wing Autonomous Aerial Vehicle

Fixed Wing Autonomous Aerial Vehicles (FW-AAV) face several challenges when surveying an area. One of the most crucial challenges that the vehicle face is to plan and adjust its path according to the obstacles that exist in the area. Although FW-AAV has good capability in conducting forward movement, its maneuverability is limited. It can't pause its movement in a location and it doesn't flexible enough to change its direction significantly when facing obstacles. In a real-world environment, the obstacles could also shift according to the unpredictable situation. In this paper, we propose a dynamic motion planning for an FW-AAV using Greedy Principle. This method tries to direct the FW-AAV path planning movement when facing obstacles considering its maneuver limitation. The proposed method introduces a concept of 4 directional rotatable Distance Sensor. The Greedy Principle is applied by choosing the nearest possible vacant path obtained by the directional sensor. The method is tested in a 3-Dimensional environment, which includes several realworld obstacles that the vehicle faces and a moving target which is the vehicle needs to reach. Using the Greedy principle, the FW-AAV could successfully reach the moving target safely.
RRT-Based Path Planning Considering Initial and Final Pose under Curvature Constraints for Nonholonomic Wheeled Robot

Rapidly-exploring random trees(RRT) are popular algorithms in path planning, because they provide efficient solutions to singe-query problems and possess probabilistic completeness. Its modifications, such as RRT* and Informed RRT*, extend RRT to asymptotically find optimal solutions as the number of sampling approaches infinity. These algorithms, however, give no considerations to robot's poses and kinematic constraints, and therefore their results can be unfeasible for a nonholonomic robot with given initial pose and desired final pose. In this paper, we present another modification of RRT* for nonholonomic path-planning with not only kinematic constraints but also initial and final pose constraints. The proposed method constructs the search tree as a directed graph of which each node retains the position (x, y) plus the robot pose θ, and a segment of clothoid curve and line is used for connecting and evaluating the cost between two nodes. Furthermore, by building two trees from both initial and final poses and executing bidirectional search, this method can find a path containing crosscut point. We experimentally show that our approach calculates smoother, more feasible paths than RRT* and satisfy the given constraints on curvature.
Sorting Pixels based Face Recognition Using Discrete Wavelet Transform and Statistical Features

Face recognition is used to identity a person effectively and most effective physiological biometric trait. In this paper, we propose sorting pixels-based face recognition using Discrete Wavelet Transform (DWT) and statistical features. The novel concept of sorting pixel values in ascending order is introduced and segmented into two parts viz., Low Pixel Values (LPV) and High Pixel Values (HPV). The DWT is applied on LPV matrix to generate low and high frequency bands such as LL, LH, HL and HH. The low frequency LL band is considered for features as the coefficient values are enhanced compared to original image pixel values and also reduction in dimensionality. The statistical measure is applied on HPV to compute mean, median, mode, maximum and standard deviation features. The features of LL band and statistical features are concatenated to obtain final features. The Artificial Neural Network (ANN) is used as classifier to recognize human beings. It is perceived that the performance of the proposed method is enhanced compared with the existing methods.
Geological and Geophysical Studies of Sulfide Copper Mineralization in the Dochileh Area: An Example of Manto‐Type Deposit in the Sabzevar Zone, Iran

Abstract The Dochileh stratiform copper deposit in the Sabzevar Zone of northeastern Iran is hosted in the basaltic sequence of the Upper Eocene age. The host rock displays two hydrothermal events: zeolite–carbonate alteration that is a stratigraphic–lithologic feature and chlorite and chlorite/ferruginous alterations in the local mineralized structures. Ore formation is related to both hydrothermal events and occurs in both stratiform and vein mineralization types. Mineralization consists of main chalcocite with variable amounts of bornite, chalcopyrite, native copper, malachite, and cuprite minerals, which occur as hydrothermal breccias, and disseminated, vein, and veinlet forms. Geophysical field studies using resistivity and induction polarization (IP) methods were conducted along nine survey lines in the area. As a result of modeling and interpretation of the acquired geophysical data, high values of IP and resistivity corresponding to mineralization were observed at two depth levels: 0–20 m and more than 40 m. Based on these geological and geophysical investigations, six locations for drilling exploration boreholes were proposed. Drilling data confirmed the mineralization containing high copper values in the two depth levels: the vein‐type mineralization in the surface and shallow depth level, and the stratiform mineralization at the deeper level. Fluid inclusion studies in calcite and quartz from stratiform‐ and vein‐type mineralization show the evidence of mixing, and a linear dilution trend during the ore formation occurred at a wide range of temperatures: 121–308°C and 80–284°C, respectively, and varying salinities of between 3.2–16.8 and 0.8–22 wt% NaCl equivalents. The stable isotope composition of δ 34 S that falls in a range of −2.4 to +25.0‰ could be considered biogenetic sulfur from bacterial sulfate reduction and leaching of sulfur from hosting basalt. The δ 13 C values of calcite vary between −0.6 and −7.6‰, suggesting a major contribution of marine carbonates associated with igneous carbonates, and the δ 18 O SMOW values of calcite are between +15.2 and +19.9‰, suggesting a contribution of δ 18 O‐rich sedimentary rocks and δ 18 O‐poor meteoric water. Copper and sulfide‐rich hydrothermal fluid have flowed upward through the local faults and permeable interbeds within the Eocene volcanic sequence and have formed the mineralized veins and horizons. The geophysical results have detected the local faults as the channel ways for mineralization.
Data Mining to Determine Correlation of Purchasing Cosmetics With A priori Method

Abstract Data mining is the process of analyzing data using software to find patterns and rules in the data set. Data mining can analyze large data to find knowledge to support decision making. In this study the Rule Association will be discussed as one of the data mining functions implemented using the A priori Algorithm. There will also be analyzed two support calculation techniques in candidate generation in the A priori Algorithm, such as: K-way and 2 Group-By in three sample datasets with transaction attributes id and item. In this study the problem of support calculation in candidate generation is the bottleneck of the A priori Algorithm where the improvement of the A priori Algorithm was emphasized on the candidate generation and the effectiveness of the A priori Algorithm. This research was lead on the Oracle RDBMS by utilizing WEKA tools to determine maximum support and confidence and to find out the correlation between products. The results shows the highest confidence value at 93% if you buy DeepClensingMilk and DeepClensingToner then you will buy Whitening Soap with confidence = 93%.
Fakeness: Digital Inauthenticity and Emergent Political Tactics in Armenia

Abstract Fake news and fake social networking profiles have recently come under a great deal of public attention. Much of this attention has focused on the dangers of politics as a space in which inauthenticity is now running rampant. Through an ethnographic exploration of how LGBT activists and their right‐wing nationalist opposition make use of fake information and fake social networking profiles in Armenia, I argue that rather than dismissing fakeness as an aberration of politics, it might be regarded as an emergent political tactic within a new informational logic that has risen through, and in interruption of, digital communication. While actors on the right in Armenia and beyond have used forms of such inauthentic politics, fakeness has the potential to be used in multiple directions and for various other political causes.