Reviewerly/exHarmony huggingface dataset:
TokenX: A Digital Sales Platform for B2C Payment and Finance Solutions

Nowadays, the importance of digital payment and sales force automation services is increasing, especially in Business to Consumer (B2C) domain. While consumers are mainly interested in digital shopping, user-friendly payment, and related streaming services; It is necessary to provide an efficient, secure, and streamlined payment and sales solution to both ensure operational efficiency and achieve enriched user experiences and satisfaction with high sales conversions. Contemporary B2C businesses are dealing with challenges such as seamless, secure data integration of various hardware and software. Even newly established companies, despite having simple operations, can face significant costs depending on their scale and complex process commitments. Factors such as channel heterogeneity, organizational size and sales strategies increase the software demands of businesses, requiring adaptable solutions. This work presents an innovative, scalable, cloud-based digital sales platform, the TokenX framework, that addresses these complexities. With the special API application to be developed within the scope of the TokenX platform, business data from various sources will be integrated with sales automation processes and an ecosystem will be created where applications work together in harmony. Accordingly, in this study, the scope of the TokenX digital sales platform with functionalities and system development issues are presented.
Resolving controversial situations using a hybrid decision-making method based on neuro-fuzzy mathematical apparatus

In this paper considers the mechanism of decision-making in the event of controversial situations. The definition of a situation is the presence of several dictionary concepts claiming to be a marker of a recognizable object. The parameters and algorithm for the functioning of the hybrid system are also experimental factors that give satisfactory results in terms of the quality of decisions made, and will be improved with the development of the system. With the help of the decision-making method used, with equal chances of choosing 1 object of information from 3 options of dictionary concepts, it improves the quality of the decision made.
Graph structure and homophily for label propagation in Graph Neural Networks

Graph Neural Networks work under the assumption of homophily, where similar nodes are more likely to be connected together. We evaluate the relation between graph homophily and various aspects of a graph: node scores derived from the graph structure (notably PageRank and SALSA), node labels, and local neighborhood. These aspects are then used to build label propagation mechanisms, and we evaluate their relative efficiency in the context of node classification for graph data. We find that the local topology of graph datasets has a much greater impact than such score-based propagation mechanisms. We highlight this by proposing two simple mechanisms, label homophily and unanimity, that show interesting properties and a relation with homophily. In particular, we argue that label homophily could be further used in more elaborate Graph Neural Network algorithms.
Software in the Loop (SIL) Simulation for an Autonomous Multirotor Flight Planning and Landing with ROS and Gazebo

Unmanned Aerial Vehicles (UAVs) have emerged as a transformative asset in surveillance, mapping, and delivery tasks since they have sophisticated autonomous capabilities. This paper focuses on enhancing the UAV autonomy by addressing the autonomous detection of suitable landing sites and safe landing flight planning, which are both critical in emergency scenarios. To mitigate the possible risks during the flight tests, vision-based sensors and image processing techniques, along with the developed guidance and control algorithms are evaluated in Software in the Loop (SIL) simulation using the Robot Operating System (ROS) and Gazebo simulation environments. The key novelty of this paper is proposing a vision-based hybrid LQR-integrator flight planning strategy for safe landing and optimal control of the UAVs. Therefore, this paper incorporates the image processing techniques for the landing site detection and UAV kinematics for safe and autonomous landing on the selected site. Further contributions of this paper are the selection of a compatible image processing technique, translation of the selected site position data into a control reference frame, development of a novel controller strategy, and the utilization of an observer for destination verification. Through the comprehensive simulations using Gazebo and ROS, the paper evaluates the effectiveness of the proposed framework, paving the way for further real-world testing and implementation.
Reentrancy Vulnerability Detection Based on Graph Convolutional Networks and Expert Patterns

Smart contracts as one of the most successful applications of blockchain. It holds digital currency with huge economic value. During the rapid development of smart contracts, vulnerabilities in the contracts have caused huge financial losses to the blockchain. This has strengthened researchers’ focus on smart contract security vulnerability detection. In this paper, we explore a vulnerability detection deep neural network-based method on combining features in both contract source code and bytecode forms. We conduct extensive experiments on the Ethereum smart contract datasets for reentrancy vulnerability. The experiment demonstrates that our method achieves 87% accuracy and 78% f1-score. Another experiment shows that our method maintains a good detection performance even when the feature part is missing.
The Demographic Effect on Information Practices of Pakistan’s Urban Youth

This study aims to investigate the effect of demographics such as number of siblings, province, gender, and age on youth’s everyday information using, managing, and sharing practices. It also exposes the demographic differences regarding information-seeking practices. A good number of studies have been carried out on youth and their information behavior but to observe their everyday information behavior and the impact of demographics very little literature is available. This study is an effort to understand the demographic effects and differences that may lead to designing systems to fulfill their everyday information needs. A quantitative approach was applied with a sample size of 1000 Pakistani urban teen students of private and public sector schools all over Pakistan. Survey methodology was applied; cluster sampling was done with a 5-point Likert scale. The data was collected through Google Forms and all the provinces were part of this survey including ICT and Gilgit Baltistan. Data Analysis was done through SPSS-21. The results revealed demographic variables were found to have a great effect on EIP, these variables were also found to polish up the proficiency of urban teen's everyday information practices. The findings would assist in developing helpful guidelines for urban teens to improve their EIP. This study will also respond to a gap in research by explaining the everyday information practices of urban teens.
Within-Project Defect Prediction

In order to improve the quality of a software system, software defect prediction aims to automatically identify defective software modules for efficient software test. To predict software defect, those classification methods with static code attributes have attracted a great deal of attention. In recent years, machine learning techniques have been applied to defect prediction. Due to the fact that there exists the similarity among different software modules, one software module can be approximately represented by a small proportion of other modules. And the representation coefficients over the pre-defined dictionary, which consists of historical software module data, are generally sparse. We propose a cost-sensitive discriminative dictionary learning (CDDL) approach for software defect classification and prediction. The widely used datasets from NASA projects are employed as test data to evaluate the performance of all compared methods. Experimental results show that CDDL outperforms several representative state-of-the-art defect prediction methods.
Other Research Questions of SDP

Sorting software modules in order of defect count can help testers to focus on software modules with more defects. One of the most popular methods for sorting modules is generalized linear regression. However, our previous study showed the poor performance of these regression models, which might be caused by severe multicollinearity. Ridge regression (RR) can improve the prediction performance for multicollinearity problems. Lasso regression (LAR) is a worthy competitor to RR. Therefore, we investigate both RR and LAR models for cross-version defect prediction. Cross-version defect prediction is an approximate to real applications. It constructs prediction models from a previous version of projects and predicts defects in the next version. We propose a two-layer ensemble learning approach TLEL which leverages decision tree and ensemble learning to improve the performance of just-in-time defect prediction. To evaluate the performance of TLEL, we use two metrics, that is, cost effectiveness and F1-score.We perform experiments on the datasets from six large open source projects, that is, Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL, containing a total of 137,417 changes. Unsupervised models do not require the defect data to build the prediction models and hence incur a low building cost and gain a wide application range. Consequently, it would be more desirable for practitioners to apply unsupervised models in effort-aware just-in-time (JIT) defect prediction if they can predict defect-inducing changes well. However, little is currently known on their prediction effectiveness in this context.
Heterogeneous Defect Prediction

Cross-company defect prediction (CCDP) learns a prediction model by using training data from one or multiple projects of a source company and then applies the model to the target company data. Existing CCDP methods are based on the assumption that the data of source and target companies should have the same software metrics. However, for CCDP, the source and target company data is usually heterogeneous, namely the metrics used and the size of metric set are different in the data of two companies. We call CCDP in this scenario as heterogeneous CCDP (HCCDP) task. We aim to provide an effective solution for HCCDP. Cross-project defect prediction (CPDP) refers to predicting defects in a target project using prediction models trained from historical data of other source projects. And CPDP in the scenario where source and target projects have different metric sets is called heterogeneous defect prediction (HDP). Existing HDP methods only consider the linear correlation relationship among the features (metrics) of the source and target projects, and such models are insufficient to evaluate nonlinear correlation relationship among the features. We propose a new cost-sensitive transfer kernel canonical correlation analysis (CTKCCA) approach for HDP. CTKCCA can not only make the data distributions of source and target projects much more similar in the nonlinear feature space, where the learned features have favorable separability, but also utilize the different misclassification costs for defective and defect-free classes to alleviate the class imbalance problem. To facilitate data sharing, it is essential to study how to protect the privacy of data owners before they release their data.
Machine Learning Techniques for Intelligent SDP

In this chapter, several common learning algorithms and their applications in software defect prediction are briefly introduced, including deep learning, transfer learning, dictionary learning, semi-supervised learning, and multi-view learning.
Implementation of HOG based feature extraction method

Human detection on emerging intelligent transportation systems is a challenging task in hardware implementation. The Histogram of Oriented Gradients (HOG)-based human detection is the most successful algorithm due to its superior performance. Unfortunately, more intensive computations and poor performance at a multi-scale and low-contrast make human detection more difficult and unreliable. To address the aforementioned problems, an efficient histogram of edge-oriented gradients-based human detection is proposed to preserve the edge gradients at low-contrast and support multi-scale detection. The proposed algorithm uses approximation methods and adopts a pipelined structure that utilizes low-cost and high-speed, respectively. Experiments conducted on various challenging human datasets show that the proposed method provides efficient detection. This algorithm has been synthesized on Xilinx Spartan 3 FPGA software and board, achieving better hardware utilization compared to other state-of-the-art approaches.
Decision Making in Team-Adversary Games with Combinatorial Action Space

The team-adversary game simulates many real-world scenarios in which a team of agents competes cooperatively against an adversary. However, decision-making in this type of game is a big challenge since the joint action space of the team is combinatorial and exponentially related to the number of team members. It also hampers the existing equilibrium finding algorithms from solving team-adversary games efficiently. To solve this issue caused by the combinatorial action space, we propose a novel framework based on Counterfactual Regret Minimization (CFR) framework: CFR-MIX. Firstly, we propose a new strategy representation to replace the traditional joint action strategy by using the individual action strategies of all the team members, which can significantly reduce the strategy space. To maintain the cooperation between team members, a strategy consistency relationship is proposed. Then, we transform the consistency relationship of the strategy to the regret consistency for computing the equilibrium strategy with the new strategy representation under the CFR framework. To guarantee the regret consistency relationship, a product-form decomposition method over cumulative regret values is proposed. To implement this decomposition method, our CFR-MIX framework employs a mixing layer under the CFR framework to get the final decision strategy for the team, i.e., the Nash equilibrium strategy. Finally, we conduct experiments on games in different domains. Extensive results show that CFR-MIX significantly outperforms state-of-the-art algorithms. We hope it can help the team make decisions in large-scale team-adversary games.
Privacy-stealing Approach in Distributed IoMT Systems

Model or parameter sharing has been commonly used in many distributed learning architectures, e.g., federated learning. However, such a sharing mechanism may result in the risk of privacy leakage to the third party. In this paper, we represent an approach to medical image privacy stealing in the distributed Internet of Medical Things (IoMT) systems. Experiment results based on the real-world dataset show that, based on our approach, key characteristics of original medical images could be recovered in a third party, i.e., aggregation server.
A Constraint Partition Method for Combinatorial Optimization Problems

Ising machines have attracted much attention in recent years because of their potential in solving combinatorial optimization problems that are difficult to be solved using conventional computers. Ising machines solve the optimization problem that can be transformed into a quadratic unconstrained binary optimization (QUBO) problem. In the case of a problem with constraints, named a constraint problem, an objective function and constraint function with penalty coefficients are combined into a single Hamiltonian of a QUBO problem. When solving a constraint problem, the accuracy of the solution decreases compared to a QUBO problem with an objective function only. This is because penalty coefficients are set to excessively large values to prevent constraint violations. As a result, it becomes difficult to minimize the objective function. To solve this problem, this paper proposes a method that partitions constraint functions and reduces the value of the penalty coefficients. The performance evaluation using the traveling salesperson problem (TSP) with one-hot constraints demonstrates that the proposed method can improve the solution accuracy compared to the conventional method.
A Lightweight Action Recognition Method for Deployable Embedded Devices for Human-Computer Interaction

In recent years, numerous researchers have proposed various solutions to address the challenges in action recognition. However, most existing approaches suffer from high computational requirements and significant memory usage, making them impractical for real-time deployment on embedded devices. This paper introduces a lightweight action recognition method suitable for deploying on embedded devices for human-computer interaction, denoted as LARMDED-HCI (Lightweight Action Recognition for Mobile and Deployable Human-Computer Interaction). Our method employs MobileNetV3 as the backbone network, significantly reducing computational load and parameter count. We incorporate the Temporal Shift Module (TSM) to model the temporal aspect of video frames, enabling the model to capture temporal features effectively. Additionally, a 1D temporal convolutional layer is introduced to enhance feature extraction in the temporal dimension, improving the model’s ability to model temporal differences between adjacent frames. Experiments conducted on the Jester and Something-Something-V2 datasets demonstrate that our approach exhibits a noticeable competitive advantage in recognition accuracy compared to other methods. Furthermore, our method achieves satisfactory real-time performance when deployed on embedded devices, facilitating human-computer interaction on such platforms.
Autonomous Driving Robot Using FPGA and BNN with Random Forest

In this paper, we propose an autonomous driving robot using binarized neural network (BNN) with Random Forest (RF). The proposed robot implements all functions from camera shooting to motor control on a small scale Field Programmable Gate Array (FPGA). For taken pictures, BNN with RF decides the direction of the proposed robot to left, right, and straight. In the experiments, we evaluate the circuit area, execution time, power consumption, and energy consumption of the proposed robot. Due to the use of BNN with RF, we can implement all functions into a small scale FPGA. Also, the proposed robot consumes about 61% less energy compared to an autonomous driving robot based on Raspberry Pi.
Overcoming Limited Data Challenges: Training Large-Scale Deduplication Models through Distributed and Non-Distributed Methods

Identity and authorisation systems are increasingly using biometric data to uniquely identify an individual. Facial image is one such biometric which is being readily used, however the integrity of such systems relies on the capability of the system to discriminate one face from another. Training state of the art face recognition models on the representative local diaspora would ensure that the model understands the facial intricacies of the population and successfully distinguish the individuals. Such high quality representative data is already readily available to the existing enrollment systems. However, the data captured is limited to only a few images per individual, and getting a good training accuracy is debatable. We process the WebFace dataset to make it representative of a large scale enrollment system, and train a Resnet-50 model with Additive Angular Margin Loss (ArcFace) with it. Given the large size of the model we train models in a distributed and a non-distributed setup and compare the two. We discuss points to consider when deciding which setup to use. We also highlight that training this model on a quality controlled dataset of a few images per identity is not only possible but also surpasses the results of the various state of the art models which are typically trained on hundreds on images per identity. Further, we use this model to build a scalable deduplication system, achieving a low crossover error rate of 0.12%, and a maximum accuracy of 99.79%.
Iterative Refinement Quantum Amplitude Estimation

Quantum Amplitude Estimation (QAE) is an important quantum algorithm that has the potential to quadratically speed up Monte Carlo based calculations.In this paper, we present a variant of the QAE without Phase Estimation Algorithm called Iterative Refinement QAE (IRQAE). IRQAE can refine the current estimation to a more accurate estimation iteratively, hence it can provide an estimation with arbitrary required accuracy ∊. The key idea of IRQAE is to use a rotation gate to create a quantum state for samplings with the current estimation. Using this idea, we show that IRQAE can provide a highly accurate estimation with lower classical computational complexity and with the same quantum computational complexity compared to state-of-the-art QAEs without phase estimation using numerical experiments. We prove that the computational complexity of IRQAE of the quantum part is O(1/∊) and the classical one is O(1/∊). The quantum cost gives a quadratic advantage over that of the classical Monte Carlo simulation.
Context-Aware Layer Scheduling for Seamless Neural Network Inference in Cloud-Edge Systems

With deep neural networks (DNNs) gaining popularity for tasks like object detection and image segmentation in domains like autonomous driving and smart agriculture, DNN inference on edge devices is increasingly necessary. While inference on edge devices can provide real time capabilities, these devices might be time and energy constraint. Offloading execution to the cloud can provide a possibility to save energy and execution time on the edge device, when a stable network connection is present. Contrary to that, failing to communicate with cloud servers during operation is detrimental to offloaded inference.In this work, we introduce a scheduling algorithm capable of leveraging the strength of cloud offloading in reducing execution time and power consumption on the edge device, while simultaneously being able to recover in the event of a network failure and continue inference seamlessly. Only raising the worst case execution time by the length of one branching interval, our method provides the ability to greatly reduce energy consumption and execution time. For FCN-ResNet50 inference on an edge accelerator, we were able to lower the overall execution time from 142.25 ms to 12.23 ms and the energy consumption on the edge device from 105.02 mJ to 5.46 mJ, while recovering from connectivity failures twice with continued inference. Comparable improvements can be achieved for other use cases using GoogleNet or SqueezeNet.
Automatic Text Classification as Relevance Measure for Russian School Physics Texts

The research tests two text classification methods applied to lexical features of Russian physics schoolbook texts. The research findings indicate that text classification methods can be implemented effectively to identify if a text relevant for a specific topic or a year of studying. We use and describe results via classical methods to validate the classification models.