Reviewerly/exHarmony huggingface dataset:
The Influence of Bandit-Based User Openness Feature on Recommendation Diversity and Accuracy

Collaborative filtering algorithms have obvious advantages in recommendation accuracy, and Bandit's algorithm is a strategy to address diversity needs. The COFIBA algorithm combines the collaborative filtering algorithm with the Bandit algorithm to provide a solution for recommending the balance of diversity and accuracy. However, COFIBA does not consider the influence of time characteristics, and COFIBA is a cumulative regret. It is relatively slow to solve the problem of diversity. Therefore, this paper proposes a learning-based model. On the one hand, it introduces the openness characteristics of users to achieve diversity recommendation, and relies on the "exploration-feedback-update" strategy to adjust the user's openness. At the same time, the time factor is incorporated into the COFIBA algorithm as a feature, and the change of user interest with time is analyzed to ensure the accuracy of recommendation. The experimental results show that the combination algorithm with time and open features has a significant improvement in the diversity and accuracy of the results compared with the COFIBA algorithm.
DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets

Machine learning is taking an increasingly relevant role in science, business, entertainment, and other fields. However, the most advanced techniques are still in the hands of well-educated and -funded experts only. To help to democratize machine learning, we propose DeepNotebooks as a novel way to empower a broad spectrum of users, which are not machine learning experts, but might have some basic programming skills and are interested data science. Within the DeepNotebook framework, users feed a cleaned tabular datasets to the system. The system then automatically estimates a deep but tractable probabilistic model and compiles an interactive Python notebook out of it that already contains a preliminary yet comprehensive analysis of the dataset at hand. If the users want to change the parameters of the interactive report or make different queries to the underlying model, they can quickly do that within the DeepNotebook. This flexibility allows the users to interact with the framework in a feedback loop—they can discover patterns and dig deeper into the data using targeted questions, even if they are not experts in machine learning.
Iterative Representation Learning for Entity Alignment Leveraging Textual Information

Entity alignment (EA), which aims to find equivalent entities in different knowledge graphs (KGs), is an essential task for aggregating KGs from different sources. State-of-the-art EA solutions mainly rely on KG structure representation for judging the equivalence of entities while overlooking textual representation. Additionally, the lack of supervision signals for learning structural representation has not attracted enough attentions. In this work, we propose an iterative representation learning process that can increase the scale of training set with confident EA pairs selected from each round and generate structural representation with higher quality. Additionally, we point out that structural and textual representations are in essence two complementing views for EA and propose to integrate them to convey more comprehensive signals. The experimental results reveal that our solution outperforms the state-of-the-arts by a large margin.
Applying Statistical Approach to Topic Analysis for more Comprehensive and Appropriate Modeling

Topic analysis is useful to construct probabilistic modeling and interpret real data. It is applied to data analysis in various fields. Topic analysis is expected to construct modeling appropriately and obtain interpretable results to assist our decision making about the field. More and more data of more and more fields give us many problems to solve with topic analysis. In this paper, we focus on treating complex data, constructing an appropriate model and interpreting data structure more meaningfully with topic analysis. To solve these problems, we suggest combinning topic analysis methods and statistical approaches. Finally, some results of experiments to study utility of our suggesting methods are given.
Chinese Long Text Sentiment Analysis Based on the Combination of Title and Topic Sentences

The long text in our study involves a complete discourse structure with a title and approximately 1500 Chinese characters within. Long text sentiment analysis has faced a lot of difficulties on the grounds that a long text often comprises multiple sentiments and diverse focuses. This paper proposes to analyze the sentiment of long texts based on the combination of title and topic sentence. Based on the fine-labeled texts, the analysis extracted the important features of topic sentences, such as location, feature words, degree of topic relevance and emotional words. Then two topic sentences were extracted from each text that can best represent the topic through weighted calculation of these multi-dimensional features. Next this paper combined the two topic sentences with the title to complete the sentiment analysis of the whole document. The topic sentence extraction method has achieved good results in the task of extracting and judging key emotional sentences of news in the 6th COAE (Chinese Opinion Analysis Evaluation) (2014), which shows that the method is effective. This method has been put into practice in the National Language Public Opinion Monitoring system, with an accuracy of 0.82.
Populational Announcement Logic (PPAL)

Populational Announcement Logic (PPAL), is a variant of the standard Public Announcement Logic (PAL) with a fuzzy-inspired semantics, where instead of specific agents we have populations and groups. The semantics and the announcement logic are defined, and an example is provided. We show validities analogous to PAL axioms and their proofs, and also provide a proof of decidability. We briefly talk about model checking and compare the framework against probabilistic logic. We conclude that the main advantage of PPAL over PAL is the flexibility to work with previously defined agents.
UWB SAR image segmentation algorithm based on polynomial analysis of statistical distribution

Aimed at the segmentation problem of ultrawideband synthetic aperture radar (UWB SAR) image, a novel algorithm based on polynomial analysis of statistical distribution is proposed in this letter. Firstly, we estimate the probability density function (PDF) for each pixel via sparse decomposition of which the dictionary consists of orthogonal polynomials (OP). Secondly, the weighting coefficients of corresponding OP are used to segment image using their maximum value. The experimental results validate the efficiency of this algorithm.
Concatenated kernel codes

Concatenated codes introduced by Forney in 1966 received wide attention due to their extensive usage in space missions. Thereafter, many concatenated codes were constructed on the similar lines and successfully employed in digital communication setup. In this paper, concatenated kernel code is defined. An example of concatenated kernel code and its trellis is constructed to demonstrate the importance of defined code and its computation. For the constructed code, the presence of homomorphism is tested using Blum–Luby–Rubinfeld Linearity test. Further, for the constructed trellis of the example code the properties such as proper, co-proper, biproper, one-to-one, minimal and minimal proper are investigated.
Subspace Learning Augmented with Class Conditional Probability Estimation Based on SVM Classifier in Domain Adaptation

The rapid evolution of data has challenged traditional machine learning methods and leads to the failure of many learning models. As a possible solution to the lack of sufficient labeled data, transfer learning aims to exploit the accumulated knowledge in an auxiliary domain to develop new predictive models. This article studies a specific type of transfer learning called domain adaptation, which works based on subspace learning in order to minimize distance between class conditional probability distributions of source and target domains and to preserve source discriminative information. SVM classifier trained on source domain data has been used to predict target domain data labels to facilitate subspace learning. In this work, subspace learning is formulated as an optimization problem and experiments have been carried out on the real- world datasets. The results of experiments indicate that the proposed method outperforms several exiting methods at this field in the term of accuracy in two object recognition benchmarks: Offlce-Caltech10 and Office datasets.
Building Ad-Hoc Clouds with CloudAgora

The public Cloud market has become a monopoly, where a handful of providers - which are by default considered as trusted entities - define the prices, accumulate knowledge from users' data and computations and strengthen their already privileged position. As a remedy we propose CloudAgora, a platform that democratizes the Cloud market by allowing individuals and companies alike to compete on equal terms as potential resource providers, while enabling users to access low-cost storage and computation without having to blindly trust any central authority. During the demo, the attendees will be able to interact with CloudAgora through an easy-to-use UI, which will allow them to act both as users and as providers. As users, the attendees will have the chance to request storage or compute resources, upload data and outsource task processing over remote infrastructures. As providers, they will be able to participate in auctions, serve requests and offer validity proofs upon request. Moreover, the audience will experience first hand how the underlying blockchain technology is used to record commitment policies, publicly verify off-chain services and trigger automatic micropayments.
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The purpose of this study is to examine the structural relationship among factors affecting learning engagement, satisfaction, continuance intention...
Applications of Deep Learning in Unified Credit Management of Commercial Banks

With the rapid development of new technologies, such as cloud computing, mobile internet, financial technology and big data, commercial banks in China have to step up their efforts to use new technologies such as artificial intelligence to innovate. In this paper, we focus on detecting the image orientation and rotating the image to the right angle automatically based on convolutional neural network in unified credit management of banks. First, in view of the characteristics of scanned images in banking systems, we analyze the process of credit evaluation and approval of Zhengzhou bank in detail. Then, we convert the problem of skew correction into image classification in unified credit management of commercial banks, and we fine-tuning the pretrained VGG16 model with TensorFlow for image classification. Finally, we develop a deep learning platform in bank of Zhengzhou to test the performance of the proposed method. In contrast to traditional image processing methods, the test results on bank's real dataset show the superiority of our proposed method, and the proposed method has been applied in the process of credit approval to improve the automation and intelligence level of Zhengzhou Bank to a certain extent.
Target-Monitoring Learning Companion Design

Under the big wave of artificial intelligence, one kind of critical technology is chatbot, whose virtual identity can play an important role in a virtual community. By proposing the system model of an Internet community and its chatbots, this paper designs target-monitoring (TM) intelligent agent and applies in real learning community. For a learning community platform, the system outputs can be assessment outcomes and learning attitudes, which can be transformed to linguistic variables by fuzzification. Moreover, a learning chatbot is designed with three-layered structure: fuzzified status, decision table and generated sentence. For learning, certain teaching strategies can be coded in the decision table to make chatbot generate advice sentences to learners. To prove the idea, a chatbot structure for learning monitoring is implemented within LINE community, and a real learning experiment is carried out. Moreover, such chatbot and its community system structure can be not only used in learning; an extended experiment is planned to construct a TM agent for operation management.
Human Emotion Recognition Using Combination of Shape-Texture Signature Feature

In the previous Chaps. 2 , 3 , and 4 consist of feature descriptors individually distance signature, shape signature, and texture signature and Chaps. 5 and 6 are considered as combined descriptors such as distance and shape (D-S) and distance and texture (D-T). In course of Shape-Texture (S-T) signature, respective stability indices and statistical measures supplement the signature features with a view to enhance the performance task of facial expression classification. Incorporation of these supplementary features is duly justified through extensive study and analysis of results obtained thereon.
A Test Case Design Method for Context Aware Android Applications

Context-aware applications are programs that use information about the environment of the user and objects and people which are present at that location and do some actions automatically according to this information. One of the most important branches in the field of context aware applications are android applications that are becoming more and more widespread today. In this paper, we introduce a test case design approach for context-aware android applications. In this approach we use the information in the manifest file and static analysis of the source code to find context-aware and sensitive points of the application and definitions and uses of these points. This information is added to the control-flow graph of the application. Then we apply data flow testing criteria to the resulting model. The evaluation results show that the all-uses criteria have acceptable error detection rate and it can cover all context-aware points of the application using fewer tests than all-du-path coverage criteria.
An Intelligent Road Waterlogging Sensor for Traffic Safety: Principle and Algorithm

Road waterlogging affects the behavior of driver-vehicle unit, thus road waterlogging perception technique is highly important to traffic safety. In this work, a pressure-guiding waterlogging perception method is introduced, and this sensor is modeled based on the principle of differential pressure to realize the real-time measurement of the road waterlogging level. In order to decrease non-linear measurement error of this waterlogging sensor under complex road environment, this paper proposed an adaptive correction algorithm according to the principle of data fusion. The experimental results show that this proposed method has much higher stability and measurement accuracy than typical measurement methods of the road waterlogging level.
THE IMPACT OF ACTIVITY BASE JOYFUL LEARNING ON ACADEMIC ACHIEVEMENT OF STUDENTS AT ELEMENTARY LEVEL

This  research  study  was  conducted  to  determine  the  impact  of  activity  base  joyful  learning  on academic achievement of students at elementary level in science. Sixty pupils of grade five from GPS Chak No.29NB  Sargodha  were  selected  as  the  population  of  study.  The  experimental  research  design  was adopted.  The  pretest  was  conducted  on  students to  mark  equal  the  two  groups  and  results  were  obtained.The  participants  were  divided  into  groups,  namely  experimental  group  and  control  group.  Thirty  pupils were volunteered for the experimental group sample and the remaining thirty pupils were nominated as the sample  of  control  group.  The  experimental  group  was  educated  through  activity  base  joyful  learning methodology while the control group was taught through traditional method. MCQ tool was developed and administered to students before and after the activity and lesson delivery. The impact of activity base joyful learning on academic achievement of students was acquired through mean value, standard deviation and „t‟ Test scores of pre-test and post-tests. The study outcome reveals that the student’s scores were significantly increased  in  experimental  group  strategy  as  compared  to  control  group  method.  The  study  perceived  that the  activity  base  joyful  learning  method  can  enhance  the  academic  achievement  of  the  pupil.  The  finding recommends  that  the  uses  of  activity  base  joyful  learning  methodology  are  more  effective  to  improve  and develop the enhanced skill and academic achievement of students.
Hand Motion Based Human Computer Interaction Using 3D Convolutional Neural Network

Electronic devices have achieved smart functionality due to the emergence of action recognition. More specifically, hand motions classification is one of the main research areas among computer vision research communities, which have tremendous potential in the field of human computer interaction. However, there are various challenges related to the computational cost of this approach. In this paper, a compact method is proposed to recognize hand actions by utilizing 3D convolutional neural network. Without using multiple sensors, our method is merely based on a mono camera. A network is trained using multiple videos of human hand motions, and then an image sequence is forward to the trained neural network for recognizing the corresponding action. Our trained neural network is dubbed as HMRNet, an abbreviation for hand motions recognition network. The HMRNet is trained and tested using 20BN Jester dataset, and experimental results showed that both the estimated error and the accuracy of the proposed network are superior compare to other traditional methods including sensors and features based. The computational efficiency and logical steps of our approach are straightforward that any user interface-based device equipped with a camera and proper hardware can adapt this method. This method allows users to interact with the personal computers in a very interactive and futuristic way, and has the potential to eliminate the use of input devices including mouse and touch pen.
An Intelligent English Teaching Scheme Based on Buchberger Algorithm

This paper introduces an intelligent English teaching system based on the Buchberger algorithm. Its design idea is to analyze students' weak links according to the students' learning situation, and to provide targeted teaching suggestions according to the analysis results. On the basis of an overview of the intelligent teaching system, the cognitive ability of students is quantitatively evaluated. By improving the design of data mining algorithm of decision tree, the teaching strategies which are in accordance with students' cognitive ability are mined from a large number of learning records, thus realizing intelligent teaching. The system structure and system characteristics determined by design ideas are introduced. The indispensable parts of intelligence are discussed, such as the expression of domain knowledge in intelligence, the student level assessment model, and the inference mechanism of teaching strategies.
Wireless Transmitter Identification Using Multicore Path Network

Due to the widespread use of wireless communication network, the criminals easily access the Internet through these distributed points for illegal activities. However the identity of the software layer is easily falsified. If the hardware characteristics can be analyzed from the wireless transmitter, it will greatly improve the accuracy of the wireless transmitters. This paper proposes a framework for identifying wireless transmitters using multicore path network (MPN). The nonlinear power amplifier (PA) model uses volterra series to describe the nonlinear behavior of the wireless transmitters. In the proposed MPN, cyclic spectrum features are extracted as the network input. The framework not only reuses features by residual branch path but also explores new features by dense connection path. Meanwhile the MPN merges different scales through different convolution kernels. Through simulation results, we demonstrate that the proposed scheme can be superior than recent methods and has moderate computational complexity.
Design of Embedded Vacuum Packaging System Based on Single-Chip Microcomputer Touch Screen

In order to improve the intelligent control ability of the embedded vacuum packaging machinery, the control system is optimized and the embedded vacuum packaging system based on the touch screen of single chip microcomputer is constructed. The system is composed of many modules and uses 51 single chip microcomputer as the digital processing chip of energy-saving control of embedded vacuum packaging machinery. Combined with the touch screen control technology of single chip microcomputer, the automatic assembly control design of the embedded vacuum packaging system is carried out, and the optimal transmission protocol of the energy-saving control system of the embedded vacuum packaging mechanical equipment is designed. In the program loading module, the instruction loading and output conversion control of the embedded vacuum packaging system are realized, and the intelligent design of the embedded vacuum packaging system is realized in the embedded integrated processor.
N. F. Annensky - Social and Political Figure of Liberal Narodnik Movement

For the first time, the article provides a comprehensive biographical survey of Nikolai Feodorovich Annensky (1843-1912) - an outstanding representative of the moderate Narodnik movement of the last third of the XIX - the beginning of the XX century. The paper describes Annensky’s achievements in the spheres of professional (zemstvo statistics), publicistic and organizational (social and political) activity. It is shown that, having gained a well-deserved ethical reputation in democratic circles, Annensky came out as a mediator between old and new generations of the Narodniks.
The Manufacture of The KETA Chemistry Game for Voltaic Cells Learning Materials

Abstract Education games are designed to stimulate students’ thinking capability and improve their concentration and problem-solving skill. The KETA Chemistry game, based on chemistry literacy, employs a virtual laboratory and hidden objects to help students learn voltaic cell materials. The purpose of this research was to develop a mobile education game as alternative media for learning voltaic cells through a simulation of real-life experiments. Research and Development method was employed. Although theoretically, the method consists of five stages, only two of them were applied in this research (define and design). The final product of the research is The KETA Chemistry, an education game, which combines 2D and 3D elements in its gameplay and the next stage of development, the media will be validated and said to be validated and said to be feasible if it meets the media’s worthiness.
A Multi-UAV Co-Simulation Environment for Safety and Performance Analysis

Co-Simulation can provide important insights on how well mission strategies perform. Simulations can provide insights on which strategies could provide safety guarantees because of the high investments necessary for the creation of a multi-UAV system. We propose a co-simulation framework of multiple UAVs for the analysis of missions in order to evaluate different flight strategies with great efficiency. To make the environment as realistic as possible, we introduced external threats such as winds, sectors with flight restrictions, and aircraft crashes. We validated our framework using a case study of an air surveillance system, where two types of flight strategies comparing two different flight strategies. We can analyze tradeoffs between the flight strategies, in terms, of chances of UAV collision, running out of energy, and performing tasks.