Reviewerly/exHarmony huggingface dataset:
Random multi-scale kernel-based Bayesian distribution regression learning

The effective embedding estimation of distribution and the construction of regression model with strong representation ability are two key problems of distribution regression. This paper proposes a random multi-scale kernel-based Bayesian distribution regression (RMK-BDR) learning framework. Vector-valued kernel mean embedding (KME) estimators with a same dimension which is chosen adaptively to the data are introduced in the first stage of distribution regression learning. Then, a linear combination of multi-scale Gaussian kernels with different scale parameters randomly sampled from a predefined distribution is used as the regression model. Sparsity priors are added on those linear combination weights. Under the Bayesian inference theory, a prediction distribution of the response variable is obtained. A series of experiment results verify the effectiveness of the proposed algorithm.
Research on the Development of Computer Intelligent Proofreading System Based on the Perspective of English Translation Application

Due to factors such as algorithm and matching degree, English machine translation still has bottlenecks. Therefore, this paper proposes a computer intelligent proofing model based on the perspective of English translation application and conducts research and development. Experiments based on this model show that the computer intelligent proofing model based on the perspective of English translation application can bring better translation performance in terms of automatics score and more grammatical coherence. Finally, the development of computer intelligent proof reading system is discussed from both hardware and software aspects.
The Impact of Merger and Acquisition on Financial Intermediation: Empirical Evidence from Nigerian Banking Industry

The paper examines the impact of bank consolidation on financial inter-mediation using data from the Nigerian bank industry from 2002 to 2010. Two models were specified and estimated: one for the lending activity and the other for the deposit activities. The model for lending activity has an interest rate on the loan as the dependent variable and deposit rate represents the dependent variable in the deposit model. The results showed that merger and acquisition, which was the main policy instrument for bank consolidation, has a significant effect on both lending and deposit activities of the banks in Nigeria. The result also shows that changes in the degree of average competition in bank markets proxied by the spread between interest rate among the banks is positive and significant in both the loan and deposit markets. This confirms the high level of price competition among the banks. The consolidation exercise had significant positive effects on both financial inter-mediation and especially on deposit mobilization. The study concluded though, the consolidation policy might have had other side effects, it has at least led to higher deposit mobilization, higher competition but however, it has led to higher cost of borrowing and spread between lending and deposit rates.
A model of semantic web service in a distributed computer system

One of the main tasks of any distributed computer system is the analysis of the properties of the obtained data, and their further use for logical reasoning.This is a difficult task due to the frequently encountered dynamics and heterogeneity of information.The aim of the work is to present a knowledge-based web service model.The web service integrates with the technologies of the semantic web in order to study the data, their processing and inference.The goal is achieved as follows: the meta-metamodel of the web service ontology and the metamodel of the semantic web service are structured using UML class diagrams and formalized using ALC description logic.The scientific novelty of the results is the presentation of a service description through an ontology.Through the introduction of ontologies, a transition is being made in data mining.In contrast to the application of standards: SOAP, WSDL, HTTP, the use of ontologies is argued that they contain structured information about the functional and non-functional characteristics of the service, and give flexibility to the description.When working with ontologies, there is a powerful logical apparatus for searching, combining and comparing ontologies.By introducing ontologies, knowledge of the web service is represented.The development of an ontology / web service pair gives an advantage over the SAWSDL, OWL-S standards, because the web service developer has the ability to create a more accurate semantic description of the service.We presented the language features and constructs of ontology of web service descriptions.Step by step, we created semantic description to describe web services.After learning how to use OWL to formally express web service semantics, we moved on to the issue of actually adding semantics to service descriptions.For the first time, formal models of service ontology and semantic web service are proposed.
RETRACTED ARTICLE: The construction of hierarchical network model and wireless activation diffusion optimization model in English teaching

Abstract Wireless communication plays an important role in modern higher education. This paper first analyzes the storage mechanism and data structure of the hierarchical network model, then fits the time series of user behavior attribute data, and uses the information-filtering algorithm to filter the interference information and redundant information in the social network. The feature extraction of association rules applies fuzzy data clustering to the mining and clustering of relational data in hierarchical networks. The simulation results show that the algorithm model has high accuracy and reliability and improves the ability of deep mining in English teaching.
Geometric modeling of sheet transfer process from grippers to grippers

Abstract One of the important processes in operation of a printing machine is the transfer of a sheet of printed material from one cylinder to another. The transfer of the sheet is carried out using special grippers attached to the printing cylinder, and quality of printing depends on their accuracy. At the stage of printing process preparation, an important step in setting up the equipment is the correct installation of the grippers. The paper presents the results of developing a geometric model of the sheet transfer process from grippers to grippers. This process is visualized using a computer simulation. The presented model makes it possible to define the trajectory of the paper movement in the transfer zone, and track the untimely opening or closing of grippers. This may lead to printing process abnormality.
API Misuse Correction

Modern software development relies heavily on Application Programming Interface (API) libraries. However, there are often certain constraints on using API elements in such libraries. Failing to follow such constraints (API misuse) could lead to serious programming errors. Many approaches have been proposed to detect API misuses, but they still have low accuracy and cannot repair the detected misuses. In this paper, we propose FAMI, a novel approach to detect and repair API misuses automatically. FAMI uses fuzzy set theory to represent usage rules of five factors involving in any API method call: related method calls, exceptions, pre-conditions, post-conditions, and values of arguments. These fuzzy sets are trained from a large repository of high-quality production code. Then, given a piece of code, FAMI verifies each of its method calls with the trained fuzzy sets. If a factor violates a usage rule defined by a fuzzy set, the corresponding call is considered as an API misuse. FAMI performs an optimal search for editing operations to apply on the code until it has no API issue.
Model set adaptive filtering algorithm using variational Bayesian approximations and Rényi information divergence

Abstract The paper presents a model set adaptive filtering algorithm based on variational Bayesian approximation (MSA-VB) for the target tracking system with the model and noise uncertainties. The Rényi information divergence, as a criterion, is to choose the best match model that has the minimum divergence between candidate models and true mode. Subsequently, the model-conditioned estimation based on variational Bayesian approximation is proposed to estimate system state and measurement noise variances. To deal with the coupled noise intractability, the moments matching technique is used to obtain the mixed statistics of measurement noise at the fusion stage. The proposed algorithm is compared with the interacting multiple models (IMM) algorithm and the variational Bayesian-interacting multiple models (IMM-VB) algorithm via two scenarios for maneuvering target tracking, and simulation results show that the MSA-VB has improved estimation and tracking performance.
Refined CNNs for Face Recognition Applications on Embedded Devices

Deployment of deep learning models to embedded devices like smart phones or other smart end-user devices has been the hotspot in computer vision and artificial intelligence. However, the performance of recent state-of-the-art CNNs is dissatisfactory in real-world face applications. To solve this issue, we refined an efficient CNN architecture for face verification with extreme efficiency for real-time face applications in embedded environment, namely R-MobileFaceNet. We also proposed Dynamically Fuzzy Image Dataset, namely DFID, to evaluate the capacity of the models to deploy on embedded platforms. Our experiments proved our refined model was capable of embedded deployment, achieving higher accuracy improvement on DFID and neglectable accuracy loss on LFW. This paper also serves as an effective and efficient solution for deploying deep learning models to real-world face applications.
Multilevel Image Thresholding Based on Renyi Entropy Using Cuckoo Search Algorithm

In this paper, optimal thresholds for multi-level thresholding in image segmentation are gained by maximizing the Renyi entropy using cuckoo search algorithm. The proposed method is tested on standard set of images. Besides, the control parameter of the Renyi entropy is discussed. Experiment results show that the effect of segmented image is not significantly affected by varying the parameter value when the thresholds to 2 then to 5.
The spaces between gesture, sound and image

Improvised audiovisual performance is a rich field of creative practice, fuelled by the image-centricity of contemporary performance culture and increasingly affordable and powerful technology for live digital image projection. However, the domains of audio and visual creativity frequently remain separate. Contemporary literature on new interfaces for musical expression are frequently centred on aspects of design and expressivity within the framework of music performance alone, and discussions on interactive and graphic scores for contemporary music performance focus on the affordances of the visual score as a medium. Meanwhile there are limited frames of reference for examining improvised audiovisual performance as an integrated practice. In this chapter, we focus on integrating the concept of gestural surrogacy from the spectro-morphological tradition with concepts of multi-sensory feature binding to build up a materialist approach to audiovisual performance analysis. To support this investigation, we utilise case-studies of three distinct approaches to audiovisual performance practice, using a combination of motiongram and spectrogram analysis along with artistic comment from the authors of each piece.
Learning Data Structure Shapes from Memory Graphs

This paper presents a novel algorithm for automatically learning recursive shape pred- icates from memory graphs, so as to formally describe the pointer-based data structures contained in a program. These predicates are expressed in separation logic and can be used, e.g., to construct efficient secure wrappers that validate the shape of data structures exchanged between trust boundaries at runtime. Our approach first decomposes memory graph(s) into sub-graphs, each of which exhibits a single data structure, and generates candidate shape predicates of increasing complexity, which are expressed as rule sets in Prolog. Under separation logic semantics, a meta-interpreter then performs a systematic search for a subset of rules that form a shape predicate that non-trivially and concisely captures the data structure. Our algorithm is implemented in the prototype tool ShaPE and evaluated on examples from the real-world and the literature. It is shown that our approach indeed learns concise predicates for many standard data structures and their implementation variations, and thus alleviates software engineers from what has been a time-consuming manual task.
A Vectorization Approach to Language Identification of Social Media Short Texts

Language identification is an important task in machine translation, information storage and retrieval et al. However, traditional approaches cannot adapt those texts in social applications, which have the following characteristics: 1) shorter length, no more than 140 characters generally. 2) colloquial and anomalous grammar texts, which are generated directly by different users of the social platform. In this paper, we propose a vectorization-based approach, which exploits weighted n-gram statistical features and improved Cascade Forest approach to conduct accurate language identification of social media short texts. In order to classify a line of text, our approach firstly represents it by a fix-length feature vector, and then leverages a supervised classifier to assign a proper language to it. We implement our approach and conduct extensive evaluations on real-world social texts. The results show that our approach achieves better accuracy, precision and recall rates when compared with the state-of-art machine learning algorithms and off-the-shelf approaches. It also proves that our approach displays competitive performance in practice.
IRIT at TRAC 2020

This paper describes the participation of the IRIT team in the TRAC (Trolling, Aggression and Cyberbullying) 2020 shared task (Bhattacharya et al., 2020) on Aggression Identification and more precisely to the shared task in English language. The shared task was further divided into two sub-tasks: (a) aggression identification and (b) misogynistic aggression identification. We proposed to use the transformer based language model BERT (Bidirectional Encoder Representation from Transformer) for the two sub-tasks. Our team was qualified as twelfth out of sixteen participants on sub-task (a) and eleventh out of fifteen participants on sub-task (b).
RETRACTED ARTICLE: Research on new college Chinese teaching strategy based on SNS network platform and improved particle swarm optimization

Abstract With the rapid development of network technology, college Chinese teaching needs to be reformed as so to adapt to the network environment. This paper puts forward a new strategy of college Chinese teaching based on SNS (social network services) network platform, constructs the model of college Chinese teaching resource allocation, and designs the college Chinese teaching network teaching platform using SNS network method. The routing nodes of SNS network platform are optimized, and the improved PSO (particle swarm optimization) is used to optimize the location of SNS network platform nodes. Based on the node distribution model of PSO, the node overlay connectivity graph of the new SNS network platform of college Chinese teaching is reconstructed to realize the network coverage optimization. The experimental results show that the proposed SNS network platform has better performance of resource scheduling and network connectivity.
Study and Analysis of Data Analysis Systems (Reconstruction of a Learning Data from the Initial Data)

Data analysis methods have been widely used in various domains, such as medical, marketing, and agriculture. Since they have a good performance to reduce massive data. Unfortunately, those methods are limited without the usage of Computer Technologies (computer technologies, 'CT'), which lead to developing autonomous systems capable of making appropriate decisions in each situation, by the realization of algorithm and artificial intelligence (artificial intelligence, 'AI') tools. This paper presents the coupling of principal component analysis (principal component analysis, 'PCA') mathematical method and the counter propagation artificial neural network (counter propagation network, 'CPN'), as an objective to reduce and minimize the data before starting learning, improve the learning process results and accuracy of the classification and eliminate the obstacles detected between inputs objects. The results of this combination have been compared with the results of the standard CPN.
Comparison of Major LiDAR Data-Driven Feature Extraction Methods for Autonomous Vehicles

Object detection is one of the areas of computer vision that has matured very rapidly. Nowadays, developments in this research area have been playing special attention to the detection of objects in point clouds due to the emerging of high-resolution LiDAR sensors. However, data from a Light Detection and Ranging (LiDAR) sensor is not characterised by having consistency in relative pixel densities and introduces a third dimension, raising a set of drawbacks. The following paper presents a study on the requirements of 3D object detection for autonomous vehicles; presents an overview of the 3D object detection pipeline that generalises the operation principle of models based on point clouds; and categorises the recent works on methods to extract features and summarise their performance.
Students' Difficulties in Writing Descriptive Text: An Analysis Study

This research aimed to know students’ difficulties in writing descriptive text. This research intended to find out students’ difficulties in writing descriptive text. The subject of the study was the 10th grade students of SMAN 4 Senayang in the academic year of 2018/2019. The researcher used writing test to know the students’ difficulties in writing descriptive text. In this research, the researcher used qualitative descriptive research. A qualitative method is kind of research without using any calculation or statistic. Firstly, the researcher identification of students’ difficulties after that the researcher classified students’ difficulties into two types, consisting of difficulties in generic structure and language features. Based on the result of the test, the students has so many difficulties in writing descriptive text, as follows difficult choosing the topic related to the topic, difficult distinguish between identification and description paragraph, difficult developing ideas related to the topic, difficult writing descriptive text with correct grammar, and also lack of vocabularies.
A Comparative Study of Searching and Optimization Techniques in Artificial Intelligence

Solving a problem mean looking for a solution, which is best among others. Finding a solution to a problem in Computer Science and Artificial Intelligence is often thought as a process of search through the space of possible solutions. On the other hand in Engineering and Mathematics it is thought as a process of optimization i.e. to find a best solution or an optimal solution for a problem. These reduce search space and improve its efficiency. At each and every step of search,it select which have the least futility. In this paper ,We categorize the different AI search and optimization techniques in a tabular form on the basis of their merits and demerits to make it easy to choose a technique for a particular problem.
RETRACTED: Research on the Construction of Legal Mechanism of Socialist Harmonious Society Based on Computer Simulation

Abstract The perfect computer simulation legal mechanism has an important safeguard function and plays a key role in building a socialist harmonious society. Chinese current legal system is still imperfect in terms of legislation and law enforcement, such as lagging legislation, unfair legislation, unfair justice, etc. Building a harmonious society is a topic of common concern to the whole society and is widely recognized by the public. Machine learning is an algorithm that computer programs can make judgments and inferences through learning data, thus simulating human intelligence activities and improving the performance of the system itself. The application of machine learning has brought opportunities for social development and injected vitality into legal reform. Therefore, based on computer simulation, this paper introduces the important role of perfecting computer simulation legal mechanism in building a socialist harmonious society, and elaborates four kinds of computer simulation legal mechanisms in building a socialist harmonious society.
Extract the Similar Images Using the Grey Level Co-Occurrence Matrix and the Hu Invariants Moments

In the last years, many types of research have introduced different methods and techniques for a correct and reliable image retrieval system. The goal of this paper is a comparison study between two different methods which are the Grey level co-occurrence matrix and the Hu invariants moments, and this study is done by building up an image retrieval system employing each method separately and comparing between the results. The Euclidian distance measure is used to compute the similarity between the query image and database images. Both systems are evaluated according to the measures that are used in detection, description, and matching fields which are precision, recall, and accuracy, and addition to that mean square error (MSE) and structural similarity index (SSIM) is used. And as it shows from the results the Grey level co-occurrence matrix (GLCM) had outstanding and better results from the Hu invariants moment method.
Handwriting feature extraction method for writer verification independent of character type by using AdaBN and AdaIN

Writer verification is usually conducted by checking similarity between same type characters written by known and unknown writers. However, in the case where the same type characters do not exist in each writer’s documents, writer verification is very difficult. In this paper, we propose a method to extract the handwriting features independent of character types to solve this problem. The proposed model is based on AutoEncoder, and applying Adaptive Batch Normalization (AdaBN) and Adaptive Instance Normalization (AdaIN) for each layer of Encoder or Decoder to extract the objective features. We conducted a writer verification experiment using handwriting images pairs between different character types of ETL-1 Character Database (ETL-1). As a result of the experiment, we confirmed that the proposed method could perform writer verification with high accuracy even in such a case.
The Design of Information System for Physics Learning Media Laboratory

Abstract This study aims to develop information systems for physics learning media laboratory and facilitate students in obtaining academic information, especially physics education laboratory. The research method used is research and development with ADDIE model. The developed web-based information system then will be tested by students from the academic community/academic program of physics education and from outside people to use the online data system. Users who have used a web-based information system are required to fill out the online questionnaire available on the web page. The results obtained from 30 physics education program students are 92.0% with very good criteria and same criteria is obtained from 20 outside users or other institutions with value 91.0%. According to the result obtained from the test indicator, it can be concluded that the web-based information system that has been made meets the eligibility requirements to be used as laboratory services to meet the needs of physics education students.