Reviewerly/exHarmony huggingface dataset:
More Than Accuracy: An Empirical Study of Consistency Between Performance and Interpretability

Expected calibration error (ECE) is a popular metric to measure and calibrate the inconsistency between the classification performance and the probabilistic class confidence. However, ECE is inadequate to reveal why the deep model makes inconsistent predictions in specific samples. On the other hand, the class activation maps (CAMs) provide visual interpretability, highlighting focused regions of network attention. We discover that the quality of CAMs is also inconsistent with the model’s final performance. In this paper, to further analyze this phenomenon, we propose a novel metric—VICE (Visual Consistency), to measure the consistency between performance and visual interpretability. Through extensive experiments with ECE and VICE, we disclose that the model architectures, the pre-training schemes, and the regularization manners influence VICE. These phenomena deserve our attention, and the community should focus more on a better trade-off in model performance and interpretability.
Optimising the Stability in Plan Repair via Compilation

Plan repair is the problem of solving a given planning problem by using a solution plan of a similar problem. Plan repair problems can arise in execution contexts, that is, when an agent performing the plan has to deal with some unexpected contingency that makes the given plan invalid. Repairing a plan works often much better than replanning from scratch, and is crucial when plans have to be kept stable. There is no planning system until now that guarantees to find plans at the minimum distance from an input plan. This paper presents the first approach to such a problem; we indeed introduce a simple compilation scheme that converts a classical planning problem into another where optimal plans correspond to plans with the minimum distance from an input plan. Our experiments using a number of planners show that such a simple approach can solve the plan repair problem optimally and more effectively than replanning from scratch for a large number of cases. Last but not least, the approach proves competitive with LPG-ADAPT.
TELEINCLUSIVE LEARNING: AN EXPLORATORY STUDY ON STUDENTS' EXPECTATIONS OF USING TELEPRESENCE ROBOT FOR ATTENDING CLASSES

Telepresence robots allow the user to attend events remotely and actively participate by controlling the robot's movement and directly addressing bystanders.Moreover, these robots proved an effective means for students with serious health issues to continue their learning process.Most of the previous studies investigated the impact of this technology on the user experience, failing to address their expectations before the adoption.The present work explored what undergraduate students with special motoric needs expect by employing a telepresence robot for attending classes.We found that respondents were concerned about their ability to control the robot, not to bother bystanders proficiently.Additionally, they were uncertain about how the look of the robot would have made them appear to their peers.Despite the potential of the telepresence robot to enhance social interactions, we found that students did not have specific expectations.
Chapter 3. Qualitative Analysis Using pXRF

This chapter overviews aspects of qualitative and semi-quantitative analysis by XRF from the perspective of paintings and pigment analysis. We review concepts of peak identification, measurement optimizations, data normalizations, and the advantages and limitations of using XRF for the analysis of compositionally heterogenous materials.
Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers

Pretraining language models on large text corpora is a common practice in natural language processing. Fine-tuning of these models is then performed to achieve the best results on a variety of tasks. In this paper, we investigate the problem of catastrophic forgetting in transformer neural networks and question the common practice of fine-tuning with a flat learning rate for the entire network in this context. We perform a hyperparameter optimization process to find learning rate distributions that are better than a flat learning rate. We combine the learning rate distributions thus found and show that they generalize to better performance with respect to the problem of catastrophic forgetting. We validate these learning rate distributions with a variety of NLP benchmarks from the GLUE dataset. The source code is open-source and free software, available at https://github.com/TheMody/NAS-CatastrophicForgetting .
Fraud Claims Detection in Insurance Using Machine Learning

Insurance fraud is an illegal activity that is deliberately done for financial gain.In the present era, this is the prime severe problem encountered by various insurance companies all over the globe.In most cases, some shallow or deep holes in process of fraudulent claims investigation have been identified as a major cause.Due to this reason, the need of using computer techniques to curb fraud activities grew as a motivation, providing not only a reliable and robust environment for customers but also reducing fraud claims to a large scale.We presented our research by automating the assessment process of insurance claims through various data techniques, where identification of false claims will be automatized by utilizing Data Analytics and Machine Learning methods.Moreover, the system has the ability to potentially develop heuristics around fraud indicators.Therefore, this model creates a positive impact on the whole insurance industry with increased company credibility as well as customer satisfaction.
Survey of Secure Communications of Internet of Things with Artificial Intelligence

The Internet of Things(IoT) with artificial intelligence aims to build a network that enables smart devices to communicate autonomously and efficiently. It is one of the fastest-growing fields in the information era. It encompasses billions of smart devices worldwide. With the explosive growth of the number of Internet of Things access devices, the types of access network devices are also complex and diverse, especially the heterogeneity of the network is becoming more and more prominent. Faced with such a huge and complex network architecture and communication environment, the existing network security architecture is facing huge challenges. The paper first analyzes the existing three-tier architecture of the IoT and possible security issues, including DDoS Attack, Replay Attack, SQL Injection, Man-in-The-Middle Attacks, and illegal Access. Internet security solutions, including Security Framework, Security Routing Protocol, Authentication Protocol, Data Encryption Transfer & Storage, Key Exchange Protocol and Intrusion Detection, and then explore the possible applications of quantum computing and quantum communication in IoT security, including Quantum Security Hardware Used in IoT, Quantum Key Distribution (QKD) in IoT, Intrusion Detection During QKD with Deep Learning (DL) and Data Encryption Protocol in IoT Based On Quantum Walks (QW), and finally discussed the opportunities and trends of existing security solutions for the IoT Challenges, these challenges and trends can be used as potential research directions for future IoT security.
STRUCTURAL-FUNCTIONAL MODEL OF INTERACTION BETWEEN EMPLOYERS, EDUCATIONAL ORGANIZATIONS OF SECONDARY AND HIGHER VOCATIONAL EDUCATION AND PUBLIC SERVICES

The article presents a structural and functional model of interaction between employers, educational institutions of secondary / higher professional education and public services including target, content, procedural blocks and considered as a set of regular, functionally related components that make up a certain integral system. The target component includes the goal, tasks and interaction principles. In the content block, the authors point out educational, scientific and innovative, informational and analytical fields, implementing the constructive and meaningful function of cooperation between employers and educational organizations. The procedural block contains the applicable forms of work, which must be implemented in accordance with special conditions. The listed blocks of the model are interconnected and necessary for understanding the work of the regional information and analytical portal “PERSPECTIVE-RRO” and its component database “Inclusive vocational education in Vologda region”, designed to store, systematize, analyze information about inclusive vocational education in Vologda region.
STaR: Knowledge Graph Embedding by Scaling, Translation and Rotation

The bilinear method is mainstream in Knowledge Graph Embedding (KGE), aiming to learn low-dimensional representations for entities and relations in Knowledge Graph (KG) and complete missing links. Most of the existing works are to find patterns between relationships and effectively model them to accomplish this task. Previous works have mainly discovered 6 important patterns like non-commutativity. Although some bilinear methods succeed in modeling these patterns, they neglect to handle 1-to-N, N-to-1, and N-to-N relations (or complex relations) concurrently, which hurts their expressiveness. To this end, we integrate scaling, the combination of translation and rotation that can solve complex relations and patterns, respectively, where scaling is a simplification of projection. Therefore, we propose a corresponding bilinear model Scaling Translation and Rotation (STaR) consisting of the above two parts. Besides, since translation can not be incorporated into the bilinear model directly, we introduce translation matrix as the equivalent. Theoretical analysis proves that STaR is capable of modeling all patterns and handling complex relations simultaneously, and experiments demonstrate its effectiveness on commonly used benchmarks for link prediction.
Loan Chain: A Blockchain-Based Framework for Smart Credit Lending

Abstract. Credit transactions require an extra level of security, trust, and privacy. Although the bank's credit transaction system technology has gradually improved, it still has high labour costs, inefficiencies, and security concerns. This study proposes a standardized blockchain application model in fintech and discovers that blockchain has broad prospects that can be realized through distributed consensus, smart contract execution, cryptographic algorithms, and distributed ledgers, which increase transparency, efficiency, and fairness in the financial industry. This study also deployed an automated machine learning system at the stage where banks qualify borrowers, optimizing the original credit model.
Glaucoma Detection Based-on Convolution Neural Network and Fundus Image Enhancement

The number of glaucoma sufferers is expected to increase to around 111.8 million in 2040 due to aging and population growth. It is necessary to develop technology to detect glaucoma automatically with a high degree of accuracy and efficiency. This research made the glaucoma classification system by comparing CNN architecture (AlexNet, GoogleNet, ResNet-18, ResNet-50, and ResNet-101) and implementing the CLAHE method as a pre-processing step. The evaluation system based on accuracy shows that the implementation of CLAHE can improve accuracy by about one to two percent. The average accuracy on Resnet-101 reached 88.24%, followed by average accuracy of ResNet-50, ResNet-18, AlexNet, and GoogleNet architectures with 87.82 %, 86.01%, 85.68%, and 85.63% accuracy, respectively. From all experiments, the best accuracy result is 89.59% on the ResNet-50 architecture experiment with a batch size of 16.
AutoTransition: Learning to Recommend Video Transition Effects

Video transition effects are widely used in video editing to connect shots for creating cohesive and visually appealing videos. However, it is challenging for non-professionals to choose best transitions due to the lack of cinematographic knowledge and design skills. In this paper, we present the premier work on performing automatic video transitions recommendation (VTR): given a sequence of raw video shots and companion audio, recommend video transitions for each pair of neighboring shots. To solve this task, we collect a large-scale video transition dataset using publicly available video templates on editing softwares. Then we formulate VTR as a multi-modal retrieval problem from vision/audio to video transitions and propose a novel multi-modal matching framework which consists of two parts. First we learn the embedding of video transitions through a video transition classification task. Then we propose a model to learn the matching correspondence from vision/audio inputs to video transitions. Specifically, the proposed model employs a multi-modal transformer to fuse vision and audio information, as well as capture the context cues in sequential transition outputs. Through both quantitative and qualitative experiments, we clearly demonstrate the effectiveness of our method. Notably, in the comprehensive user study, our method receives comparable scores compared with professional editors while improving the video editing efficiency by 300 $$\boldsymbol{\times }$$ . We hope our work serves to inspire other researchers to work on this new task. The dataset and codes are public at https://github.com/acherstyx/AutoTransition .
BACHELORS’ PROFESSIONAL COMPETENCIES IN THE EDUCATION PROGRAMME “SOFTWARE ENGINEERING”

The article focuses on the development of bachelors’ professional competencies in the education programme 09.03.04 "Software Engineering". In order to develop the content of professional competencies and indicators of their formation, the subject area of software engineering, the federal state educational standard of higher education (3 + +) and the international experience of CDIO were analyzed. In addition, "Programmer" and "System Analyst" professional standards as well as the employers' requirements for IT specialists were studied. The authors have identified professional competencies based on the main types of professional activities such as software development, project and research activities in the field of information technologies.
Spark Meets MPI: Towards High-Performance Communication Framework for Spark using MPI

There are several popular Big Data processing frameworks including Apache Spark, Dask, and Ray. The Apache Spark software provides an easy-to-use high-level API in different languages including Scala, Java, and Python. Spark supports parallel and distributed execution of user workloads by supporting communication using an event-driven framework called Netty. Some efforts - including RDMA-Spark and SparkUCX - were made in the past to optimize Apache Spark on High-Performance Computing (HPC) systems equipped with high-performance interconnects like InfiniBand. In the HPC community, Message Passing Interface (MPI) libraries are widely adopted for parallelizing science and engineering applications. This paper presents MPI4Spark which uses MPI for communication in a parallel and distributed setting on HPC systems. MPI4Spark can launch the Spark ecosystem using MPI launchers to utilize MPI communication inside the Big Data framework. It also maintains isolation for application execution on worker nodes by forking new processes using Dynamic Process Management (DPM). It bridges semantic differences between the event-driven communication in Spark compared to the application-driven communication engine in MPI. MPI4Spark also provides portability and performance benefits as it is capable of utilizing popular HPC interconnects including InfiniBand, Omni-Path, Slingshot, and others. The performance of MPI4Spark is evaluated against RDMA-Spark and Vanilla Spark using OSU HiBD Benchmarks (OHB) and Intel HiBench that contain a variety of Resilient Distributed Dataset (RDD), Graph Processing, and Machine Learning workloads. This evaluation is done on three HPC systems including TACC Frontera, TACC Stampede2, and an internal cluster. MPI4Spark outperforms Vanilla Spark and RDMA-Spark by 4.23x and 2.04x, respectively, on the TACC Frontera system using 448 processing cores (8 Spark workers) for the GroupByTest benchmark in OHB. The communication performance of MPI4Spark is 13.08x and 5.56x better than Vanilla Spark and RDMA-Spark, respectively.
Comparison of the Methods of Image Slicing After Initial Image Processing Using the Statistical Confidence Limits Technique

The use of image segmentation in image processing is of great importance in analyzing and extracting information from images, and one of the most important segmentation techniques is the threshold technique, which is considered one of the simplest techniques of image division in image processing. The statistical methods play an important role in the process of image segmentation. Statistical confidence in image processing, preliminary processing, as it removed noise from the images, and here the obscure noise was used. After that, the resulting images were cut, the initial processing process with the global Otsu threshold technology and a group of local techniques, namely Niblack, sauvola and local Bernsen, and the split image quality was measured by statistic measures namely Jaccard Similarity Coefficient and Maximum Signal to Noise Ratio (PSNR). as was the application of the methods mentioned on the images and the comparison between the methods of treatment in order to obtain the best results that appear in the image in which it appears and reduce noise.
A model for data ethics instruction for non-experts

The dramatic increase in use of technological and algorithmic-based solutions for research, economic, and policy decisions has led to a number of high-profile ethical and privacy violations in the last decade. Current disparities in academic curriculum for data and computational science result in significant gaps regarding ethics training in the next generation of data-intensive researchers. Libraries are often called to fill the curricular gaps in data science training for non-data science disciplines, including within the University of California (UC) system. We found that in addition to incomplete computational training, ethics training is almost completely absent in the standard course curricula. In this report, we highlight the experiences of library data services providers in attempting to meet the need for additional training, by designing and running two workshops: Ethical Considerations in Data (2021) and its sequel Data Ethics &amp; Justice (2022). We discuss our interdisciplinary workshop approach and our efforts to highlight resources that can be used by non-experts to engage productively with these topics. Finally, we report a set of recommendations for librarians and data science instructors to more easily incorporate data ethics concepts into curricular instruction.
High-level and Low-level Feature Set for Image Caption Generation with Optimized Convolutional Neural Network

Automatic creation of image descriptions, i.e. captioning of images, is an important topic in artificial intelligence (AI) that bridges the gap between computer vision (CV) and natural language processing (NLP). Currently, neural networks are becoming increasingly popular in captioning images and researchers are looking for more efficient models for CV and sequence-sequence systems. This study focuses on a new image caption generation model that is divided into two stages. Initially, low-level features, such as contrast, sharpness, color and their high-level counterparts, such as motion and facial impact score, are extracted. Then, an optimized convolutional neural network (CNN) is harnessed to generate the captions from images. To enhance the accuracy of the process, the weights of CNN are optimally tuned via spider monkey optimization with sine chaotic map evaluation (SMO-SCME). The development of the proposed method is evaluated with a diversity of metrics.
Experimental Implementation of An All-Optical Reservoir Computer Using Photonic Time Stretch and Spectral Mixing

Reservoir computing (RC) has been widely used in processing temporal information and classification tasks due to its high efficiency in training and testing. In this paper, we have experimentally demonstrated the performance of an all-optical reservoir computer based on time stretch and spectral mixing. Spectral comb lines of the stretched optical pulse are chosen as virtual nodes in the reservoir layer. Nonlinear spectral mixing is achieved through phase modulation and semiconductor optical amplification. A simple temporal waveform classification task was implemented using the demonstrated RC system to verify the approach.
Autonomous Communication Relay Position Planning Based on Predictive Model

When operating in complex mountainous areas, the mission UAV must fly at low altitudes to execute a certain mission. The mission UAVloses wireless communication with the ground station due to the occlusion of the mountains along the way. To this end, this paper proposes a multi-UAV autonomous communication relay position planning method based on the predictive model. The relay UAV is therefore introduced that must always maintain communication transmission between the mission UAV and the ground station. The method includes the establishment of a visibility model, the lightweight characterization of terrain, and the description of the predictive model optimization problem. Simulation results show that the method can autonomously plan the relay UAV position that satisfies the relay function according to the trajectory of the mission UAV in complex mountainous areas.
Different Methods of Linear Phase IIR Filer Realization

Signal processing has become one of the most popular research topics. Researchers have designed a variety of digital filters to exclude unwanted random noises during the transmission or extract part of the signal in the desired range. There is a prevalent trend for digital filters to replace analog ones since they do not require hardwires. All the performances are operated on a single processor and are free from the effect of external factors. Finite impulse response (FIR) and infinite impulse response (IIR) filters in digital filters respectively provide infinite and finite impulse responses. In the application, it is preferred to have a linear phase digital filter, and FIR filters are naturally linear. However, FIR filters have higher orders and group delay than IIR filters, so researchers found various ways to implement linear phase IIR filters for improvement. This paper introduces and compares Powell and Chau Linear Phase IIR Filter, Kwan Linear Phase IIR Filter, and Xiao, Oliver, and Agathoklis Linear Phase IIR Filter.
Descriptive metadata management for scientific events and conferences registration in Ukraine

This conference proceeding presents the results of a national survey conducted among providers of academic conferences and scientific events as well as their participants, with an aim to gain perspectives regarding the awareness of the impact of predatory scientific conferences on research output dissemination and academic events’ metadata curation. The total number of valid responses was 1270, 80 % of which were dissatisfied with the current practice of academic event registration in Ukraine. Therefore, we proposed that implementation of a platform for academic events would simplify the processes of unification and collection of information on scientific events and simultaneously would help to automate the processes of their registration. The system will provide access to verified information about scientific events in all fields of knowledge and will enable scientists to track events and plan participation in them. In addition, the system will provide information about providers of events that will help to protect young researchers from predatory events’ nets.