Reviewerly/exHarmony huggingface dataset:
Cross X-AI: Explainable Semantic Segmentation of Laparoscopic Images in Relation to Depth Estimation

In this work, two deep learning models, trained to segment the liver and perform depth reconstruction, are compared and analysed with their post-hoc explanation interplay. The first model (a U-Net) is designed to perform liver semantic segmentation over different subjects and scenarios. Particularly, the image pixels representing the liver are classified and separated by the surrounding pixels. Meanwhile, with the second model, a depth estimation is performed to regress the z-position of each pixel (relative depths). In general, these two models apply a sort of classification task which can be explained for each model individually and that can be combined to show additional relations and insights between the most relevant learned features. In detail, this work shows how post-hoc explainable AI systems (X-AI) based on Grad CAM and Grad CAM++ can be compared by introducing Cross X-AI (CX-AI). Typically the post-hoc explanation maps provide different visual explanations of their decisions based on the two proposed approaches. Our results show that the Grad Cam++ segmentation explanation maps present cross-learning strategies similar to disparity explanations (and vice versa).
Unraveling the Effect of COVID-19 on the Selection of Optimal Portfolio Using Hybrid Quantum-Classical Algorithms

Portfolio optimization is one of the important problem classes in finance sector. As the world economy recovered and reaching the pre-COVID levels and is still continuing to grow, it is imperative to understand what kind of impact COVID-19 had on portfolio optimization and how quantum computers can be used in doing so. Hybrid quantum algorithms like variational quantum eigen solver (VQE), quantum approximate optimization algorithm (QAOA) have been tried for identifying optimal portfolios. In the present work, we employ these algorithms to carry out portfolio optimization for different time intervals (which includes pre and during COVID-19 periods) and unravel the impact of COVID-19 on the same, across different industries.Our results show that the companies’ stocks were growing steadily more on a sideways movement from 2016-2017. The stocks start to pick momentum from 2018-2019 and in 2020 early months as dip can be observed, however all the stocks recover to normal levels and even reaching lifetime highs from second half of 2020. Keywords—Quantum Algorithms, Portfolio Optimization, Covid-19, Markowitz model, QAOA, VQE
Medical Image Authentication using Watermarking and Blockchain

In recent years the use of images in communication has significantly increased. It is important to secure images from unauthorized access and manipulation. Copyright protection approaches protect data from attacks that result in modification, illegal copying or removal. Our proposed approach uses watermarking and blockchain to confirm authentication. Watermarking is a data-hiding technique whereby data is hidden inside an image to make it difficult to modify. Blockchain at its core, is a distributed-ledger that stores secured and encrypted information while ensuring that it is not changed by someone. We present an imperceptible and secure watermarking approach for digital images using the discrete wavelet transform (DWT). The encrypted watermark is saved in the blockchain. When an image is received and it is to be authenticated, the watermark is retrieved from the blockchain and compared with the watermark that is extracted from the given image. The visual quality of the proposed watermarking approach is tested by comparing the original and watermarked images using peak signal-to-noise ratio (PSNR). The basic challenge that we face is finding suitable locations for inserting the watermark while satisfying the imperceptibility aspects along with the security of the algorithm to reach its highest effectiveness.
Application of Computer Network System in Personalized English Reading Teaching in Intelligent Classroom

With the rise of information technology and the advent of computer network era, computer network communication technology is slowly changing people's way of life. The progress of information technology also promotes the continuous reform of education, the emergence of intelligent classroom, personalized English reading teaching and the deep integration of computer network system technology, all reflect the traditional classroom is developing to the direction of intelligence and information. This paper firstly introduces the theory of computer network system. Secondly, it also analyzes how to apply it in the personalized English reading teaching of wisdom class. Finally, a personalized English reading teaching method based on wisdom class is designed. The experimental results of this paper show that, based on the computer network system, personalized English reading teaching in intelligent classroom is popular with both teachers and students, with the highest popularity reaching 96%.
Example Mining

Programmers often use examples with concrete values to better understand code. Code by itself is abstract, which empowers it to be used for a variety of uses, but can be difficult to grasp by developers. Babylonian Programming addresses this by allowing programmers to concretize their code by defining and visualizing examples directly in the code itself while editing.
Symbolic Hyperdimensional Vectors with Sparse Graph Convolutional Neural Networks

In this paper, we propose a novel way of representing graphs for processing in Graph Neural Networks. We reduce the dimensionality of the input data by using Random Indexing, a Vector Symbolic Architectural framework; we implement a new trainable neural layer, also inspired by Vector Symbolic Architectures; we leverage the sparseness of the incoming data in a Sparse Neural Network framework. Our experiments on a number of publicly available datasets and standard benchmarks demonstrate that we can reduce the number of parameters by up to two orders of magnitude. We show how this parsimonious approach not only delivers competitive results but even improves performance for node classification and link prediction. We find that this holds in particular for cases where the graph lacks node features.
Path Selection for Packet Transmission in Mobile Devices Communications by Deep Learning Technique

The proposed neural network's objective is to reduce network time delay within the context of this study's optimization of the packet pathways. The routing algorithm's primary concern, which can be resolved with real-time path computations, is said to be finding the shortest route. It is possible to achieve excellent performance by taking advantage of the gaps in earlier research, which are reflected in the system's insufficient training and the erroneous predictions as a result of not accounting for the feedback from the hidden layers. In order to enhance the current approaches employing weights obtained from packet ID and to simultaneously alter neural network iteration, this study attempts to propose an effective technique. In this paper, the creation of an effective neural network with the right output is covered in detail, along with the network's underlying concepts. The study's conclusions showed how efficient it is to use computational systems to showcase computer simulation. The study's conclusions showed that it is highly effective to use the computational system's energy to show off numerical simulations. It is also demonstrated that the system performed well when trained to obtain a 3 percent time delay with 5 nodes in mobile device communications. In order to determine whether to permit each D2D pair to transmit data, a CNN is constructed. We gather data samples from a suboptimal algorithm in order to train the CNN and verify the trained CNN. Due to the limited computer capacity, our numerical findings demonstrate that the accuracy of the proposed deep learning-based transmission technique reaches roughly 85%– 95% despite its simplistic structure. Additionally, the outcomes demonstrated that the key components of the suggested model may operate in real time and are adaptable to changes in path topology.
Prediction of College Students' Physical Test Effect Based on BP Neural Network

With economic advancements in our country, people's aspiration for a better life fuels their enthusiasm for sports, and thus physical exercise has become increasingly important in people's minds. Meanwhile, China is also promoting the physical fitness test of college students nationwide, with the view to encouraging regular physical exercise among college students. According to the survey, the 800/1000-meter long-distance running has become the most challenging test for college students. As long-distance running is physically demanding, if students seldom take on exercises, some of them are likely to experience physical discomfort during the test. To raise college students' awareness of physical fitness and set goals for their daily exercise, this paper studied the impact of students' gender, height, weight, and vital capacity, etc. on their performance of the 800/1000-meter running. From the students who passed the physical fitness test of Three Gorges University in 2016, 60 groups of test results of these students were randomly selected as the research object. By analyzing the connections between these data, a prediction model was built. Subsequently, through K-Fold cross-validation, the students' actual scores of 800/1000-meter running and predicted values were compared and analyzed. The experimental results showed that the BP neural network prediction model can effectively predict the performance of students in the 800/1000-meter long-distance running. The predicted results can provide a reference for college students' to set goals for daily exercise, thus arousing their interest in daily exercise.
COVID-19 Vaccination Twitter Data Sentiment Analysis Using Natural Language Processing

Statistics from Twitter, due to the emergence of social structures and social media, sentiment analysis has become relevant to a growing range of applications. In India, the vaccine stress for prevention in competition with COVID-19 began on January 16, 2021. Tweets about vaccines such as Covaxin, Sputnik, Pfizer and BioNTech are included in the statistics. The paper reflects everyone’s views, which were used to provide feedback on the COVID-19 vaccination in January 2021. With COVID-19 as the keyword, we employed the Naive Bayes classifier to do sentiment analysis. Also, utilizing the API technique, we manually crawl the data for statistics using the retrieved entries taken from Twitter. We can extract the subjective facts of a file using device learning techniques and natural language processing and attempt to classify it based on its polarity, which includes first-rate, neutral and terrible. It is a useful evaluation to consider whether we might also, additionally, if most people expect excellent things from it, its stock markets will rise, and so on. Sentiment analysis is definitely a long way from being solved due to the language’s complexity (objectivity/subjectivity, negation, lexicon, syntax and so on), but it is also why it is a great experience to work on. In this project, you will use an approach of developing a model based entirely on possibilities to try to classify messages from Twitter into feelings. In micro-blogging platform, people can express themselves quickly and spontaneously using any means of sending tweets.
A Naming Pattern Based Approach for Method Name Recommendation

Method names in software projects are significant for developers to understand the method functionality. Existing state-of-the-art automated approaches tend to explore tokens composing method names from method contexts. However, the method name is not a simple combination of tokens, as it is structured and contains many repetitive naming patterns (e.g. “get __”, “create __”). Through a large-scale empirical analysis on 15M methods from 14K real software projects developed with Java codes, we found repetitive naming patterns in method names. In addition, the names of two function-similar methods usually have the same naming pattern. Based on our empirical study, we propose a naming pattern-based approach for method name recommendation, named Nam-Pat. Specifically, for a target method, NamPat first retrieve the most similar method from the training data by estimating their body code similarity. Then, the name of the most similar method is used as the pattern guider to provide the naming pattern, and NamPat combines it with the context information of the target method to perform method name recommendation. To verify the effectiveness of the proposed approach, we conducted experiments on 17M methods from a widely used Java dataset. Experimental results show that compared with Code2vec, Code2seq, MNire, and Cognac, NamPat improves the state-of-the-art approaches in precision (5.8%-27.1%), recall (11.1%-60.1 %), and F-score (8.5 %-43.9%), which proves the effectiveness of our proposed approach.
Data Processing: Neural Techniques, Part 1

In addition to specific applications, we also apply algebraic techniques to computational methods leading to such applications.
Trust Recommended Algorithm Based on DDPG and Attention Mechanism

In view of the insufficient researches on the dynamic change of user trust in existing trust-based recommendation algorithms, a DDPG-TRA algorithm based on deep reinforcement learning and attention mechanism is proposed, which can catch the user interests and dynamically update user trust. The algorithm firstly uses the state module combined with the attention mechanism to model the user's state, and then recommends item. When the user adopts the item, it integrates social trust relationships and similarity relationships to predict item score, and computes the error between the predicted score and the actual score. Finally, the algorithm gets the recommendation reward and updates the trust with the score difference. We conduct experiments on three datasets and compared with other algorithms, and the proposed algorithm can get more accurate results.
Blockchain: an ambitious technology for managing SCM

Trust has become a critical management element upstream (manufacturers, producers, etc.) and downstream (distributors, wholesalers, etc.) of SCM, especially in security-sensitive sectors. SCMs are increasingly complex, and SC members must store and manage a huge amount of related information to provide evidence of regulatory compliance to national authorities and the most demanding customers. The complexity and fragmentation of modern SC networks impede the development of robust traceability and transparency mechanisms. In this article, a trustless and secure distributed architecture for SC traceability is presented. To assess the feasibility of a blockchain-based SCM approach, a case study on the SC traceability of some companies is presented. The applicability of the model is further illustrated by the development of fully functional smart contracts and a specific private blockchain. In addition, the various links between the proposed blockchain-based model and its managerial implications are presented.
Kinematic-constrained Path Planning for Catamarans

Unmanned surface vehicles (USVs) are performing a variety of tasks today. Catamarans are a kind of widely used USVs. A well-designed path planning is a basis for USVs' safe navigation. However, some methods proposed may generate wrong results due to incomplete state records, and as far as we know, few researchers have emphasized and analyzed this question. This paper also proposes a bidirectional A*-FMM method to generate close-to-optimal paths for catamarans in known environments, considering their kinematic constraints. A two-dimensional FMM result is utilized to guide the three-dimensional A* search, making the path planning procedure more time-saving and space-saving. Our method can generate a continuous and reasonable trajectory to follow. Experiments and simulations are carried out to validate the effectiveness and efficiency of our algorithm.
Experimental Implementation of a Trajectory Planner for Autonomous Driving

Automation of road vehicles faced a disruptive growth in the last decades and it’s expected to deeply change many aspects of the society. In last years we assisted to a spread of autonomous driving pilot projects and proof of concepts by means of experimental prototypes both from companies and research groups. In this paper, a trajectory planner for autonomous driving is implemented and tested on a prototype vehicle. A full description of Model predictive control (MPC) formulation adopted is provided and implementation choices are motivated. A performance and reliability analysis is presented by means of a comparison between simulation and experimental results collected on a racetrack. Conclusions and future development directions are drawn.
Fast Path Planning Method for Agricultural Robot Automatic Guidance based on Cubic Spline Interpolation in Strip Farming

In the autonomous navigation of agricultural robots in the field, the number of path nodes determines the smoothness of the robot's movement. However, the process of defining node positions is time-consuming and labor-intensive. In this study, a path planning method based on cubic spline interpolation is proposed to reduce the time of node position definition and configuration. At the same time, the use of this method can also maintain the stability of the steering control of agricultural robots, so that the robot can move smoothly. First, select several path nodes in the strip field as control points using cubic spline interpolation. Combines the complete path of a polyline segment by interpolating the path of the start point, control point, and landmark point between the straight path and the turn path. A small number of path node locations are defined by the global navigation satellite system with real-time kinematics (GNSS-RTK) system, and a robot dynamics system is used to actually simulate the feasibility of the proposed model in a strip field environment. The simulation results verify the effectiveness of the proposed algorithm. This method can reduce the number of path node position definitions and maintain the accuracy of the robot's route travel.
Research on Fall Detection Method of Empty-nesters Based on Computer Vision

Due to the frequent occurrence of falls of empty nesters, a timely and accurate fall detection method is designed based on computer vision. Using background subtraction, the moving objects are detected from videos, and then the thresholds of four fall features of the human body are set, including width-height ratio, centroid change rate, effective area ratio, and inclination angle. If the threshold condition is met, the body fall is determined. Combined with multi-characteristics, the method accurately identifies non-fall behaviors such as walking, squatting, and sitting down. The experimental results show that the fall detection algorithm based on multi-feature fusion accurately judges the fall. The algorithm has the advantages of a low amount of computation and satisfiable robustness.
Research on Resource Scheduling Algorithm Based on Greedy Strategy

With the advent of the era of big data and the popularity of smart phones, the impact of cloud computing on people’s daily life is increasing, and the improvement of living standards has also led to the rapid development of the level of science and technology. Cloud computing resource scheduling algorithm has always been the focus of cloud computing scientists. How to shorten the task completion time, improve the utilization of resources, reduce the energy consumption of the server and ensure the user QoS has been a hot topic in the field of cloud computing. This paper uses Java cloud computing simulation platform – CloudSim simulation platform. On the basis of CloudSim, create tasks with attributes such as instruction length, file size, and virtual resources including attributes such as memory, bandwidth and execution speed. In this experiment, the total execution time from transmission to completion on a single virtual resource is calculated according to the tasks and various attributes of virtual resources, and the total execution time is greedy, and the best allocation method with the least total execution time is selected. The time and the total execution time of the total transmission time and the shortest time are achieved. Under the condition of ensuring the user QoS, the load balancing is achieved.
Markerless Visual Servoing Using Virtual Face Tag and Image Moment Invariants

A novel normalized-centered image moment invariant-based visual servoing scheme is presented. A fresh combination of the moments invariants is proposed for planar symmetrical objects that are parallel to the image plane at the desired position. The output of the face detection module in the image plane, that is a rectangle around the face is used as a virtual target, treated as a planar symmetrical object which always remains parallel to the image plane. The proposed method completely decouples the DoF and is 2D translation, rotation, and scaling invariant. It was experimentally validated on a real robotic arm using a haar cascade-based face detection system to accomplish visual servoing tasks for both 2D printed faces and actual 3D faces. Unlike traditional deep learning based visual servoing schemes, the proposed markerless visual servoing technique does not require a 3D model of the face or a reference image/pose at the desired position and it can also work with unfamiliar faces without prior training .
The Effects of Model Capacity in Modelling Variability between Training and Testing Environments for Automatic Speech Recognition

Automatic Speech Recognition (ASR) applications have increased greatly during the last decade due to the emergence of new devices and home automation hardware that can benefit a lot from allowing users to interact hands free, such as smart watches, earbuds, portable translators and home assistants. ASR implemented for these applications inevitably suffers from performance degradation in real life scenarios. Most ASR systems expect that the working environments are similar to the training environment, which is often not the case, especially for new applications with limited data availability. This study is concerned with experimentally showing the effect of variations in the environment on different ASR models and the capacity of different models to improve performance when provided with training data similar to the testing environment. The experiments were conducted using discrepant training and testing datasets with varying levels of discrepancy. These tests can help researchers for novel applications identify suitable models according to the anticipated variabilities between the training data used and the real-life application.