Reviewerly/exHarmony huggingface dataset:
Research on Text Classification Modeling Strategy Based on Pre-trained Language Model

Fine-tuning the pre-trained language model is the current mainstream method of text classification. Take the fine-tuning BERT model as an example, this kind of approach has three main problems: the first one is that the training of massive parameters will cause high training costs The second is that the model is very easy to over-fit in trainable samples, resulting in low transferability. Third, the fine-tuning model is not good at long text classification task. In this paper, we take the sentiment classification task as an example and use the classification accuracy as a metric. We compared two methods to problem one: using a compressed language model (decreased 0.1%) and using entirely frozen weight (reduced by 4%). For the second problem, in the case of using fixed weights (reduced by 4%), Convolutional Networks (CNN) and Capsule Networks (CAP) are used to extract n-gram features and clustering features so that the classification accuracy is improved (increased by 0.5% and decreased by 0.2% respectively). The corpus transfer test shows that CAP’s accuracy is greater than the fine-tuning model (increased by 0.6%). Meanwhile, this article proposes a method to process the long text classification task by expanding position embedding to support long text input (increased by 1.1%). Finally, this paper compares the training speed and parameter scale of different models under the combination of different strategies, and uses the F1 value, precision rate, recall rate and accuracy to measure each model.
Exponent of cyclic codes over

In this paper, we introduce the exponent of a cyclic code over the finite field with q = pm elements. Also, we prove that the exponent of the dual of cyclic code satisfies the condition n = lcm(e, r) where n is the length of the cyclic code, e = exp(C) and r = exp(C⊥ ).
Camera deflection detection and image correction for applications in ADAS algorithms

Cameras that are mounted on almost every modern vehicle can be deflected due to the different reasons such as mechanical or environmental. This deviation of the camera from reference position can affect performance of the camera based Advanced Driving Assistant Systems (ADAS) such as lane detection. In this paper one possible approach for camera deflection detection and image correction is given. The proposed approach is based on estimation of homography matrix that is used for image transformation after which nearest-neighbor interpolation is performed. The presented algorithm for image correction is implemented in TDA2xx System-On-Chip on ADAS Alpha development board. The approach is evaluated for different deflections of the automotive camera from the reference position.
Computer Practice Course Lightweight Online Teaching Design Based on CSCL

Computer practice teaching need efficient and lightweight online collaborative learning platform between teachers and students. Based on CSCL learning scenario, we propose a method of building online teaching platform of computer practice course by combining various lightweight mobile Internet tools, and conducts an empirical study in the course of “Innovation Engineering Practice”. The results show that lightweight online collaboration platform can promote efficient interaction between learners, improve self-management of learners, and improve learning effect and self-evaluation of learners.
Descriptive Stability of Fuzzy Rule-Based Systems

Fuzzy Rule-Based Systems (FRBSs) are endowed with a knowledge base that can be used to provide model and outcome explanations. Usually, FRBSs are acquired from data by applying some learning methods: it is expected that, when modeling the same phenomenon, the FRBSs resulting from the application of a learning method should provide almost the same explanations. This requires a stability in the description of the knowledge bases that can be evaluated through the proposed measure of Descriptive Stability. The measure has been applied on three methods for generating FRBSs based on three benchmark datasets. The results show that, under same settings, different methods may produce FRBSs with varying stability, which impacts on their ability to provide trustful explanations.
Peculiarities of the structure of the headline complex in the youth sports media

This article reviews the headline structure on the online news portals that are dedicated to sport events. The object of this research is a typical model of heading complex, while the subject is the specificity of text heading strategy during a notable sport event, such as the Olympic Games in Tokyo. The article employs the method of continuous sampling of headlines, classification and interpretation of the acquired material, comparative analysis, as questionnaire-based survey of young journalists &amp;ndash; students of the faculty of Journalism. The answers of respondents indicated that these information portals have become a traineeship for the students majoring in sports journalism. The novelty of this article is defined by the following factors: most common headline structure on the sports information portals consists of two simple sentences; there are four types of relationships between the parts of headline. The author determines the reason for the dominance of the two-part headline complex: on the one hand, it is explained by the general evolution of headlines of the Internet media &amp;ndash; from drawing attention towards purely informative, which virtually replaces reading of the material. On the analyzed Internet portals, this is complemented by the tendency towards copying the structure of the post in social media: the image and short description it, which can unfold to a larger text upon the desire of the reader. It is assumed that namely the prevalence of young journalists in sports media and the focus on young audience that follows sports events t leads to the increase of headlines of such type.
Factors Affecting the Personal Development and Online Education of the Computer Science Students During Covid-19 Pandemic Lockdown

In this study, the factors affecting personal development and online education of undergraduate computer science students during pandemic lockdown have been investigated. Due to sudden outbreak of Covid-19, undergraduate students were forced to shift to home-schooling, an experience they never tried before. The results of the conducted survey and some academic performance features were combined to identify the factors affecting personal development and online education. Online lecture attendance and having personal timetable were found to be effective factors on personal development. We observe that final year students are more likely to take part in personal development activities. The availability of the technological facilities has direct effect on online course attendance and satisfaction, moreover, there was strong correlation between online education satisfaction and personal development.
Optimized scheduling techniques focused on powerful heuristics leveraging cloud services soft computing

Purpose Determining the roles of multiple CSPs is important because it affects job costs and time off. The primary objective of this work is to ensure an efficient and complex distribution of resources in cloud-based computing. Workflow study of various algorithms such as ant colony optimization (ACO), differential evolution algorithm, genetic algorithm, particle swarm optimization (PSO), hybridization of the above algorithms (ADGP). For research, CSP’s tools are put all over the world. Design/methodology/approach The main objective of this study is to effectively introduce cloud-based computing in CSPs. The algorithm minimizes resource response time and overall workflow tasks. It seeks to improve load balancing by modifying the algorithm to support load balancing. In the proposed multipurpose scheduling methods, the ADGP algorithm performs better than any other proposed algorithm during the resource response. This algorithm was found to be superior to the selected 200 sources and thousands of tasks. It reduces resource response time by copying service nodes through several sites. As this algorithm moves faster to the best solution, the response time of the resource is reduced compared to other algorithms. Findings Hybrid ACOs perform best when it comes to resource management when workloads are uniformly spread across multiple virtual machines. However, hybrids PSOs are better suited to choosing the best options to minimize costs. Overall, an optimal cloud-based scheduling solution can be successfully simulated using CloudSim in CSP to share resources between end-users to support consumers and users effectively. Originality/value Hybrid ACOs perform best when it comes to resource management when workloads are uniformly spread across multiple virtual machines. However, hybrids PSOs are better suited to choosing the best options to minimize costs. Overall, an optimal cloud-based scheduling solution can be successfully simulated using CloudSim in CSP to share resources between end-users to support consumers and users effectively.
A suite of Process Metrics to Capture the Effort of Developers

Software repositories are a valuable support for large teams working together on a project. Besides the code, a repository includes useful data related to development practices. This paper proposes novel metrics applied to data extracted from repositories and related to the development. Complete automation has been achieved for extracting data from repositories and for computing metrics, as well as representing them. This has been performed by using available tools, such as PyDriller, and our own developed components. The value of the proposed metrics is to reveal the effort of developers and their practices, in order to highlight strengths and weaknesses, then suggest improvements.
Discrete Wavelet Transforms in Walsh Analysis

A review of discrete wavelet transforms defined through Walsh functions and used for image processing, compression of fractal signals, analysis of financial time series, and analysis of geophysical data is presented. Relationships of the discrete transforms considered with wavelet bases recently constructed and frames on the Cantor and Vilenkin groups are noted.
Two Layer Tensor Form Convolutional Sparse Coding for Stereo Matching

Stereo matching is an important research topic in the field of computer vision. It recovers depth information from a pair of color images. Unfortunately, converting multi-dimensional (more than two-dimensional) data into two-dimensional data, such formulations ignore the spatial structure of multi-dimensional images/data. Tensors can be used to describe high-dimensional data structure, which can retain the hidden structure of data, but cannot obtain the deep features that helps to improve the performance of the algorithm. Therefore, it is very important to establish a deep tensor model. In this paper, we propose a two layer tensor form convolutional sparse coding model, which can automatically learn the deep convolutional kernel. Based on the learned two layer convolutional kernels, a two-layer dictionary learning model is established. Then, a new weighted matching cost method is constructed, which combines shallow and deep features. The experimental results on the Middlebury benchmark v2 and Middlebury benchmark v3 show that the proposed two layer tensor convolutional sparse coding is effective for stereo matching.
Developing English Writing Material in Recount text based on Blended Learning for Grade X Computer Network Engineering

This study aims to develop English writing material for grade X Computer Network Engineering at SMK Negeri 2 Binjai based on Blended Learning. This study was conducted by Research and Development study (R & D) Design through six phases; gathering information and data, analyzing the data, designing new material, validating by expert, revising and final product. It was conducted at SMK Negeri 2 Binjai in grade X, Computer Network Engineering Department students which is consist of 34 Students. The instrument of collecting data was observation, questionnaires, interview. Based on the data, I has gotten that from the students book material in writing material are not appropriate with their major or needs. Then, there is writing material that should be developed based on syllabus and their need. the appropriate of blended learning can be a solution for students to having a critical thinking. It also about the ways of applied technology in learning English especially in writing material. In analyzing students need, the questionnairesdistributed for 34 students and also conducting the interview to the teacher. After the material has been design, the writing material has finished. The product have been validated by expert. The result show 86,1% from the English lecturer and 90,7 % From English teacher. It means that the writing material book for Computer Network Engineering Department are categorized as “relevant” for the students at SMKN 2 Binjai.
Cloud Data Storage Security: The Challenges and a Countermeasure

Cloud computing for utility-based services has emerged rapidly in recent years. Characteristics of cloud computing like on-demand network access, ubiquitous access to a shared pool of computing resources, pay as you go model, scalability gave popularity over traditional computing technologies. Now, by moving to the cloud environment, organizations can focus on quality of services by saving cost and time in infrastructure setup. Although the features of cloud computing empower the users, but few features like scalability, multitenancy leads threat to confidentiality and integrity. As a user of cloud does not have administrative control over physical resources and client data resides on vendor's premises, threat to confidentiality, integrity, and privacy may lead to customers to think before switching to a cloud environment. In this paper, we investigate flexible distributed storage integrity auditing mechanism, utilizing the homomorphic token and distributed erasure-coded data.
Face Translation based on Semantic Style Transfer and Rendering from One Single Image

Many avatar characters have been animated in films or games, which always need a lot of time for post-processing with the computer graphics technologies. In recent years, lots of deep learning based methods have been proposed for face translation and image generation, which always require a large amount of data for training. However, there are few samples for special characters' prototype. In this paper, we present one face translation framework for translating human faces to that with visual effects from one single prototype image. The proposed framework consists of three modules. We first design one module to generate semantic face mask–the semantic mask generating (SMG) module. According to the semantic mask, the face color tone can be changed to that of the prototype. So we design the semantic color transfer (SCT) module. For the local textures, we design the deformation and rendering (DR) module. Experiments show that the proposed framework can generate images with prototype's visual effects while preserving the original person's identification and expression information.
A cloud-based system of faculty management with radar chart analysis applied

This paper describes a design of faculty management system based on cloud computing technology. A unique model is developed to enable performing radar chart analysis on evaluating faculty performance from the aspect of teaching, research, administration and university operation. These data are stored in a database at the cloud platform in where further analysis and tracing back can be performed to explore deeper potential of faculties. With such system, university leader board can make a proper strategic plan of faculty management in time to get acceleration on development. Meanwhile, individual faculties can be guided to be promoted following a professional pathway.
Public Governance of the Blockchain Revolution and Its Implications for Social Finance: A Comparative Analysis

There is hardly anyone who has not heard of blockchain nowadays, even though an exact idea about what blockchain is and how it works is far from common. The knowledge most of the people seem to have of Nakamoto’s original concept is related to cryptocurrencies; to the point that blockchain itself is often referred to as the “Bitcoin protocol”. The most important and promising uses of this technology, however, lie far beyond sectors as distinct as health care, aerospace, energy, and education. The aim of this work is to investigate the potential for blockchain application beyond the monetary system and to compare some of the existing laws that have a direct impact on blockchain as a whole in the context of social finance and social innovation. We consider examples of “blockchain in action” in different areas and geographic locations—the EU, Israel, and East Asia are the selected case studies—and discuss their unique characteristics. Their comparative review helps extract what blockchain-friendly regulatory framework would look like and what the existing challenges are for its implementation on a wide scale, starting from the regulatory framework of reference.
Study on deep CNN as preprocessing for video compression

In the recent years, video compression and picture quality have become more intense topic in research areas. In addition, user prerequisite for better resolution and higher quality video compression is increasing. Versatile video coding (VVC) is the latest emerging video coding standard specially designed for video compression. However, its frequency-based transform techniques are vulnerable on high-frequency noise, which results in increased bitrate or low picture quality. To resolve such unintended attack, we apply denoising convolutional neural network (DnCNN) to input video of codecs as a preprocessing since the DnCNN model was studied for image denoising with the capability of handling Gaussian denoising with residual learning strategy. In this paper we demonstrate experimental results that how DnCNN model helps for noised video data in terms of quality and bitrate.
Dynamic Strategies and Opponent Hands Estimation for Reinforcement Learning in Gin Rummy Game

Gin Rummy Card game is an old and popular game, which was created by Elwood T. Baker and his son C. Graham Baker in the 20th century. This imperfect information card game allows players to create strategies and mathematical calculations to maximize their win rate. In this paper, we presented an AI Gin Rummy player by using Java. Based on the game's rules, we developed both a discard algorithm and a draw algorithm to maximize the win rate by switching different strategies. Moreover, to win more points, we developed dynamic strategies, which can be more responsive and intelligent. The strategies’ algorithms are dynamic based on previous games’ results. By implementing discard and draw algorithms, the win rate increases from 50% to 57.85%. Including our dynamic strategies further increase the win rate to 67.735% (among 100,000 games).
Privacy Protection in IoT Applications

Privacy protection is a normal security requirement in network environment. The applications of IoT make the privacy protection issue more significant, because some IoT devices are used to catch personal private information such as health condition, medical figures, and location information.
“Smart” upgrading of teaching equipment following the whole life cycle of multimedia equipment in Colleges and Universities

How to make the investment of classroom equipment have a positive impact on the teaching effect is a problem that every school must pay attention to when upgrading the teaching information equipment. The teaching equipment should match with the teaching methods adopted by teachers, and the improvement of teaching informatization should not be done in a hurry, especially in terms of upgrading the already built multimedia classroom to the smart classroom. It should be changed according to the rules, otherwise, it is more likely to result in the waste of construction funds. By analyzing several functions of the smart classroom and the life cycle of each equipment in the classroom based on the maintenance records of the multimedia classroom, this study aims to offer a feasible scheme of “smart” upgrading of the multimedia classroom accordingly. It also draws a conclusion on how to carry out the teaching in the smart classroom.
A Novel Scatter-enhanced Correlation Feature Learning Method

Canonical Correlation Analysis (CCA) is an essential algorithm in the feature learning field. However, it does not utilize supervised information, and it failed to solve nonlinear problems. Therefore, this paper proposes a novel feature learning algorithm called Scatter-enhanced Canonical Correlation Analysis (SeCCA). This paper integrates the internal structure information and supervised information of the data and embeds them into the canonical correlation framework. The excellent image recognition performance of this algorithm can be demonstrated by extensive experimental results.
A Fast and Robust Embedded Optical Flow Unit on Motion Estimation

In the last few decades, deep learning is already being widely used to yield excellent performance, automate processes, pattern detection and natural language processing. Among different types of deep neural networks, convolutional neural network has been most extensively studied. In this study, the authors have introduced a network architecture named Fast Unit of Optical Flow (FUOF), which enables the network to estimate optical flow through a fast and robust approach. Our proposed method, FUOF based on the updating of FlowNetC, which includes a layer that correlates feature vectors at different image locations. We also introduced the Extended Sobel method to extract the spatial information to optimize further computation time. Moreover, to fuse the spatial and temporal information flexibly and comprehensively, two different fuse networks are defined as FUOF-Sum and FUOF-Concat in our study. The fuse mode's main difference is on the summarizing of each channel for concatenating of channels for the feature extraction this simple yet powerful idea validated by the experiments with MPI Sintel and Flying Chairs datasets. According to our results, the network with FUOF can achieve a competitive efficiency on the accuracy of standard error measure, convergence speed, and robustness for optical flow estimation.
Bayesian Nonparametric Classification for Incomplete Data With a High Missing Rate: an Application to Semiconductor Manufacturing Data

During the semiconductor manufacturing process, predicting the yield of the semiconductor is an important problem. Early detection of defective product production in the manufacturing process can save huge production cost. The data generated from the semiconductor manufacturing process have characteristics of non-normal distributions, random missing patterns and high missing rate, which complicate the prediction of the yield. We propose the Dirichlet Process - Naive Bayes model (DPNB) that can simultaneously impute missing values and address classification problems. Since the DPNB is based on the infinite Gaussian mixture model, it can estimate complex data distributions and make predictions for missing datasets with some missing patterns due to nice properties of the Gaussian distribution. The DPNB also performs well for high missing rates since it uses all information of observed components. Experiments on various real datasets including semiconductor manufacturing data show that the DPNB has better performance than state-of-the-art methods in terms of predicting missing values and target variables as percentage of missing values increases.