Reviewerly/exHarmony huggingface dataset:
Simple and Effective Complementary Label Learning Based on Mean Square Error Loss

Abstract In this paper, we propose a simple and effective complementary label learning approach to address the label noise problem for deep learning model. Different surrogate losses have been proposed for complementary label learning, however, are often sophisticated designed, as the losses are required to satisfy the classifier consistency property. We propose an effective square loss for complementary label learning under unbiased and biased assumptions. We also show theoretically that our method assurances that the optimal classifier under complementary labels is also the optimal classifier under ordinary labels. Finally, we test our method on three different benchmark datasets with biased and unbiased assumptions to verify the effectiveness of our method.
Nested Deep Feature Attention Module for Underwater Image Enhancement

Abstract The captured underwater images always suffer degradations because of absorption and light scattering in water. Thus, underwater image enhancement becomes indispensable as a precondition to carry out underwater tasks. Thus, we put forward an end-to-end structure to solve the problem of underwater image degradation. Our network uses continuously stacked deep-layer feature extraction modules to exploit more significant features. For purpose of enhancing the image contrast, we recover high-frequency information by hiring the nested residual attention groups in feature extraction modules, which can adaptively extract more deeper features. Moreover, channel attention module is hired in residual groups to emphasize important information. Comparing with other state-of-the-art approaches, our network performs best on quantitative and qualitative evaluations at datasets EUVP and UIEBD.
Blended learning as a promising direction of informatization of higher education

The article discusses possible options for transitioning from the traditional form of organization of the educational process to blended learning at the university. The author reveals the advantages and disadvantages of blended learning organization models, which allow combining traditional face-to-face learning with the self-study work of students in a digital environment that corresponds to a new generation of professionals. The advantages of the models include the formation of educational routes for students, taking into account the goals of education, changes in the speciality and the labour market, and great freedom of the student when he can choose a convenient time, place and pace of learning; motivating students to study in traditional classes with a teacher, reducing the percentage of failed courses. Keywords: informatization, higher education transformation, blended learning models, learning trajectories.
A Multi-Objective Cloud Workflow Scheduling Strategy Based on the Modified HEFT Algorithm

Abstract Aiming at the issues of poor system resource utilization and the challenge of ensuring the quality of service (QoS) in scientific workflow scheduling in the current cloud computing environment. By combining the existing IaaS cloud resource model with the scientific workflow task model, the article proposed a cloud workflow scheduling model framework. Firstly, proposed multi-objective QoS constraints based on the execution time and costs in scientific workflow scheduling. Then, modified the HEFT algorithm which was a typical scientific workflow scheduling algorithm. Based on the modified HEFT algorithm makes full use of the idle time of resources and maximizes the multi-objective QoS requirements of users. The comparative experiments show that the scheduling strategy of the modified HEFT algorithm can effectively optimize the costs and execution time of scientific workflow scheduling and achieve higher system resource utilization.
THT: Transfer Head and Tail for Joint Entity Relation Extraction

Abstract Extracting entities and relations from unstructured text is the basic task of building knowledge graphs. Various methods based on sequence labeling have been proposed recently, but most of them use uniform token representations to identify entities. It is clear that the first and last words of the head and tail entities should have different representations. For this reason, we propose a method based on word interaction that provides different representations for the first and last words of entities. Specifically, initial token representation is obtained by BERT. Each token interacts with the next k tokens for the entity’s first token recognition, interacts with the previous k tokens for the entity’s last token recognition, and merges them to obtain the head entity. Then recognize the tail entities based on the head entities and form entity pairs. Finally, get the triples based on a simple multi-classification for relation determination. Extensive experimental results on four datasets show that THT performs better than state-of-the-art baselines.
Analysis of Tools for the Development of Conversational Agents

Conversational agents are being increasingly adopted in various domains, such as ecommerce and customer services, and as a direct communication channel between companies and end-users.Several tools have been developed to facilitate their definition and deployment.They exploit existing cloud infrastructures and artificial intelligence (AI) techniques to efficiently process users' input and extract conversational information.Major Information Technology (IT) companies, such as Google, IBM, Microsoft, and Amazon, have provided powerful tools to develop conversational agents.Still, choosing the most appropriate tool is not easy, as it may require high costs associated with automatic natural language processing (NLP) services and expertise in software engineering and AI.Therefore, this paper aims to analyze different tools to help developers and non-developers to choose the optimal tool for their specific scenario of creating a conversational agent.
A General Construction of Integer Codes Correcting Specific Errors in Binary Communication Channels

Integer codes have been successfully applied to various areas of communication and computer technology. They demonstrate good performance in correcting specific kinds of errors. In many cases, the used integer codes are constructed by computer search. This paper presents an algebraic construction of integer codes over the ring of integers modulo A=2n+1 capable of correcting at least up to two bit errors in a single b-byte. Moreover, the codes can correct some configurations of three or more erroneous bits, but not all possible ones. The construction is based on the use of cyclotomic cosets of 2 modulo A.
Security Authentication Mechanism of Spatio-Temporal Big Data Based on Blockchain

Spatiotemporal big data are a kind of data that marks time information and geographic location and has been widely applied in various fields. However, there are always security issues with spatiotemporal big data, especially in data collection and authentication. Traditional authentication protocols are less efficient in the face of ultra-large-scale IoT (Internet of Things, IoT) device verification, and the threat of single-point failure is relatively large. Given these complications, a group authentication scheme is proposed in this paper with blockchain spatiotemporal big data. The decentralization of the blockchain is utilized to solve the single point of failure, and the single-point authentication is combined with the group authentication, the authentication efficiency is improved through the group authentication, and the illegal nodes are accurately identified using the single-point authentication. The simulation results demonstrate that using the MHT (Merkel Hash Tree, MHT) algorithm for group authentication can effectively improve the authentication efficiency of the entire system when the number of users exceeds 200. The time overhead is only 4 ms when the number of users is 16,000. It can have a large throughput (400–500 tps) and a low latency (1–2 s) at the same time when the block size is 1500 KB. This study not only verifies the legitimacy of each device and protects the security of spatiotemporal big data, but also significantly reinforces the authentication efficiency compared with similar schemes.
Research and Design of Real Time Big Data Visualization Analysis Platform Based on Flink

Abstract Real-time big data analysis provides a better value than standard offline big data analysis. Academic groups are still researching and exploring the technologies of real-time big data analysis. Due to the vast scale and poor transmission rate of big data, there are several impediments in the research process, making it hard to fully access the potential value of data.A real-time large data visualization analysis platform based on Flink is presented to overcome this challenge.The platform first collects data in real time and sends it to the message middleware Kafka, and then the big data analysis platform reads the data stream in Kafka in real time and transmits it to the multidimensional operator developed with Flink as the framework for real-time Calculate, and uses WebSocket technology to push the calculation results to the front end in real time, and perform visual display, enabling real-time monitoring and analysis of data flow.The experimental findings suggest that the real-time big data analysis platform based on the integration of Flink and Kafka can accurately, promptly, and clearly portray the real-time data flow process and visual analysis.
Cervical Cancer Prediction Using SMOTE Algorithm and Machine Learning Approaches

Cervical cancer is one of the most successful types of treatment when diagnosed early. In this study, it is aimed to find and classify the disease with data mining methods on the digitized data set obtained as a result of the pap-smear test. Two-stage architecture has been proposed for the diagnosis of cervical cancer. In the first stage of the study, missing data were extracted from the used dataset, and in the second stage, a new dataset was obtained by using the Synthetic Minority Oversampling Technique (SMOTE) algorithm to balance the target classes in the dataset. By applying the majority voting (MV) method to the dataset used in the study, the structure with 4 target variables was reduced to a single target variable. On two data sets, Artificial Neural Network (ANN), Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN) algorithms from data mining methods were used for the diagnosis of cervical cancer. The results obtained from the original dataset and the dataset produced with Smote were compared. ANN is the best method evaluated according to classification success and F-score, and the major voted target variable in the balanced data group produced with the Smote algorithm gave the most successful result. The experimental results showed that the use of MV and SMOTE algorithms together increased the classification success from 93% to 99%.
Recapture Results and Classical Logic

Abstract An old and well-known objection to non-classical logics is that they are too weak; in particular, they cannot prove a number of important mathematical results. A promising strategy to deal with this objection consists in proving so-called recapture results. Roughly, these results show that classical logic can be used in mathematics and other unproblematic contexts. However, the strategy faces some potential problems. First, typical recapture results are formulated in a purely logical language, and do not generalize nicely to languages containing the kind of vocabulary that usually motivates non-classical theories—for example, a language containing a naive truth predicate. Second, proofs of recapture results typically employ classical principles that are not valid in the targeted non-classical system; hence non-classical theorists do not seem entitled to those results. In this paper we analyse these problems and provide solutions on behalf of non-classical theorists. To address the first problem, we provide a novel kind of recapture result, which generalizes nicely to a truth-theoretic language. As for the second problem, we argue that it relies on an ambiguity, and that once the ambiguity is removed there are no reasons to think that non-classical logicians are not entitled to their recapture results.
Analyzing the Use of Time-based User Behavior Data to Improve the Performance of Graph Neural Network-based Recommendation Systems

모바일 기기의 발전으로 인해 해당 산업군의 비약적으로 발전했으며 이로 인해 사용자들은 이전에 비해 손쉽게 다양한 추천을 받을 수 있게 되었다. 하지만 잘못된 추천의 경우 오히려 사용자 경험 품질을 저해시키는 문제가 발생한다. 이러한 문제를 극복하기 위해서 본 연구에서는 현재 사용자가 원하는 아이템을 보다 정확히 추천하기 위해 시간 기반의 데이터가 추천시스템에 미치는 영향을 실험을 통해 분석하였다. 실험 결과 F1, Precision@20 점수는 전체 데이터를 활용하는 것보다 최근 데이터만을 이용하는 것이 더 높은 점수를 보였으나, Recall@20 점수의 경우 오히려 낮은 점수를 보였다. 이러한 결과로 유추하였을 때 현재의 사용자가 원하는 아이템의 예측 정확도 높이기 위해서는 데이터의 개수보다, 시간을 기준으로 하여 데이터를 분류하여 학습하는 것이 보다 높은 성능을 낼 수 있음을 확인하였다. The evolution of mobile devices has led to leaps and bounds in this industry, allowing users to get recommendations more easily than ever before. However, in the case of incorrect recommendations, the quality of the user experience is compromised. In order to overcome this problem, this study analyzed the effect of time-based data on the recommendation system through experiments in order to more accurately recommend items that users currently want. The experimental results showed that the F1 and Precision@20 scores were higher using only recent data than using all data, but the Recall@20 score was lower. Inferring from these results, we found that to improve the prediction accuracy of items desired by current users, it is better to learn by classifying data based on time rather than the number of data.
Performance Improvement of HMR based Human Pose and Shape Estimation Model by using Multi-View Camera

인체생성모델 기반의 연구를 통해 센서가 필요없이 이미지만으로 모션데이터를 추정하는 연구가 활발히 진행되고 있다. 최근 대부분의 연구들이 단일 카메라 기반의 방식을 택하고 있으나 이는 가려진 영역에 민감하여 보이지 않는 영역이 있을 경우 모델의 성능이 떨어지는 문제가 있다. 이러한 부분을 해결하기 위해 본 연구에서는 다중카메라를 이용하는 멀티뷰 방식을 제안한다. 어떤 카메라에서 보이지 않는 영역이 다른 카메라에선 잘 보일 수 있다는 가정을 기반으로 본 연구에서는 두 개 카메라 이미지 입력에 대한 모델의 출력값을 반복 추정 과정에서 교차함으로써 두 카메라의 추정 정보를 활용할 수 있도록 하고 이를 통해 모델의 전체 성능을 개선하였다. 본 연구에서 제안하는 방식은 간단한 방식으로써 기존 모델구조를 확장하여 활용할 수 있는 장점이 있다. 제안한 방식의 효과에 관한 실험을 위해 기존 모델에 적용하여 10%이상의 성능향상을 보였다. 실험 코드는 https://github.com/kwonhyeokmin/MVHMR.git에서 확인할 수 있다. Through human body model based research, there is an active pursuit to estimate motion data using only image inputs without requiring sensors. However, most recent studies have opted for single-camera-based methods, which suffer from a decrease in performance when certain areas of the body are not visible in the image. To address this issue, this study proposes a multi-view-based method that using multiple cameras. Building on the assumption that a body area not visible in one camera may be visible in another, this study improves the overall performance of the model by utilizing the estimation information from two camera images in the iterative estimation process by cross-linking the output values of the model for the two camera inputs. The proposed method has the advantage of being a simple extension of existing model structures, allowing for broad applicability. The effectiveness of the proposed method was demonstrated by applying it to an existing model, resulting in more than a 10% performance improvement. The code is available in https://github.com/kwonhyeokmin/MVHMR.git.
Multi-camera Object Tracking Method based on Trajectory Prediction and Image Similarity Measurement

이 논문에서는 다중 카메라에서 동일 객체를 지속적으로 추적하기 위한 객체 재식별 방법을 제안한다. 다중카메라 네트워크에서 각 카메라의 설치 상황에 따라 서로 다른 시점, 높이, 조도를 가지며 인접하는 카메라가 검지하는 영역간의 거리가 서로 다르다. 이 논문에서 제안하는 방법은 다중 카메라 네트워크에서 다양한 설치환경에 따라 객체의 궤적을 이용한 위치 추정을 통해서 재식별하는 방법과 시각적 특징을 이용해 재식별하는 방법을 결합한다. 제안하는 방법은 다중 카메라 설치 상황에 따라 위치 기반 재식별 방법과 시각적 특징 기반재식별 방법의 가중치를 조절하여 정확도를 높인다. 이 논문에서는, 제안하는 방법을 구현하고 실제 도로에 설치한 다중 카메라에서 수집한 데이터를 기반으로 성능평가를 수행하여 제안하는 방법의 우수성을 보인다. This paper proposes a method for re-identifying objects continuously tracked in multi-camera network. In a multi-camera network, each camera has different viewpoints, heights, and lighting conditions, and the distance between the regions detected by adjacent cameras is also different depending on their installation. The proposed method combines re-identification based on the object"s location prediction and re-identification based on the visual features of objects. It enhances accuracy by adjusting the weights of location-based re-identification and visual feature-based re-identification methods according to the installation conditions. The proposed method is implemented and evaluated using data collected from multi-cameras installed on real roads to show its effectiveness.
An improved checkerboard detection algorithm based on adaptive filters

Checkerboard corner extraction is a crucial step in camera calibration. However, most existing algorithms are not good enough if the lens distortion is too large. This study aims to propose a checkerboard corner detection algorithm based on adaptive filters to address this problem. First, adaptive filters based on local image features are used to generate response maps of checkerboard images. Next, a non-max suppression and a scoring system are applied for further screening. Finally, the whole checkerboard structure is restored via inertia growth. The algorithm proposed is subject to rigorous experimental validation using synthetic and real images. Compared with several state of the art methods, our algorithm has the best performance.
Smart Solar Tracking System for Optimal Power Generation Using Three LDR's

The quantity of power provided by solar panels has significantly increased during the last several years. To maximise the energy output of solar panels, it is essential to periodically monitor the sun's location. The most common method of solar panel tracking is using a microcontroller to move solar panels in response to the position of the sun. The microcontroller, a programmable device, may be used to track and control various inputs and outputs. In the instance of solar panel tracking, the microcontroller may be set up to track the location of the sun and adjust the angle of the solar panels to produce more electricity. The microcontroller can be connected to a number of sensors, such as light sensors or GPS devices, in order to accurately track the location of the sun. These variables serve as the foundation for the Julian date, the time of day, the geographic coordinates, and one of the most widely used algorithms for solar panel tracking. This technique may be used by the microcontroller to locate the sun and adjust the angle of the solar panels accordingly. The microcontroller may then instruct servo motors or other actuators to move the panels as required. In order to prevent plagiarism, it is crucial to understand the core concepts and algorithms involved in solar panel tracking and to create a distinctive solution. Also, researchers must ensure that every source they use in their study is correctly referenced. In summary, microcontroller-based solar panel tracking is an essential part of solar energy systems that might improve sustainability and energy efficiency. With the proper programming and implementation, a microcontroller can accurately monitor the sun's location and adjust the angle of the solar panels, creating the maximum amount of electricity. By coming up with a unique and inventive application, researchers can aid in the development of fresh ways in the field of solar energy.
Application of Blockchain and IoT in Cap and Trade of Carbon Emissions

Rapid industrialization around the world has resulted in an exponential surge in greenhouse gas levels, particularly carbon dioxide (CO2). As of 2020, India is the third biggest carbon emitter worldwide accounting for 7% of global emissions. In India, around 25% of greenhouse gas emissions come from the industrial sector according to the reports of the International Energy Agency (IEA). Though there are laws regulating industrial emission, corruption in the pollution control boards and lack of transparency has led to inefficient pollution control. Hence, there is a need for an accountable and transparent procedure to monitor and limit the carbon footprint of the country. This paper focuses on developing a more accountable method to limit carbon emissions by implementing the carbon cap and trade technique. By utilizing the concept of Internet of Things (IoT), the project aims to develop and connect a resilient sensor network with blockchain. The emission levels from various locations are read using a sensor module which then updates the data to the cloud. The emission logs are then downloaded and stored using smart contracts on blockchain to prevent the data from being tampered or modified. The blockchain thus authenticates the emission logs and sends out a violation alert when the set threshold is crossed. The proposed project provides an efficient and transparent method for carbon emission monitoring and trading.
High Radix Design for Montgomery Multiplier in FPGA platform

Cryptographic systems, such as RSA, ECC, and SHE, rely heavily on the design of modular multiplication. As these systems involve large integers, the bit-width of the modulus is a critical factor in determining performance. Therefore, creating efficient hardware implementation of the Montgomery Multiplier (MM) algorithm is a significant challenge. Specifically, the performance of a modulus with a variable bit-width N is a key consideration. The Montgomery Multiplication algorithm can improve iterative modular multiplications and squaring required for point multiplication. This algorithm puts back the time-consuming divisions with multiplications and shifts, resulting in faster computations. A fixed-precision Montgomery Modular Multiplier with radix 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> (FPR2tm) performs an iterative computation of intermediate results connected to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> bits of the multiplier. However, this method assumes that the bit-width <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> of the modulus is fixed. While the intermediate results must be computed around one multiplier bit at a time, the Radix 2 design introduces a significant cycle delay. This delay can be reduced to 1/m when m > 1. Unfortunately, the resulting design is highly expensive in terms of the circuit's delay and area, which prevents the design of a high-radix Montgomery MM. When the radix is greater than 2, the cycle count decreases, but the multiplication becomes the clock period's bottleneck. To address these issues, a variety of low-latency, high-radix Montgomery Multiplication designs are shown in this paper. According to the experimental results, the design achieves a clock period of 41.75MHz and 140 Slice LUTs with low Area-Latency characteristics.
Detection and Classification of Leukaemia using Artificial Intelligence

Leukemia is a kind of cancer that affects the bone marrow and blood. It is typically diagnosed through a combination of symptoms, examination, and laboratory tests, such as the Complete Blood Count (CBC), which measures the types and counts of white blood cells, red blood cells, and platelets, among other things. The goal of this study was to create a diagnostic model for leukaemia using a CBC dataset that collected. The model was created by training machine learning techniques on a preprocessed dataset. Following that, accuracy, precision, and recall metrics were used to assess the model. The benefits of using a diagnostic model for leukaemia based on a CBC dataset include speed, efficiency, high performance, low cost, and automation. The advantages and limitations of these techniques will be evaluated and consolidate our findings to provide guidance for future research in this area.