Reviewerly/exHarmony huggingface dataset:
Explore Connection Pattern And Attention Mechanism For Lightweightimage Super-Resolution

Despite the great success of CNN (Convolutional Neural Network) in SISR (Single Image Super-Resolution), the increase in network depth leads to higher computational complexity and memory usage, which extremely hinders real-world applications. To solve this problem, we propose a lightweight cascade fusion network (CFNet) by stacking the cascade fusion block (CFB), which adopts both cascade connections and fusion connections to fully utilize the extraction ability of convolution. Specifically, Cascade connections help to transfer low-level features of source, and fusion connections help to obtain hierarchical features produced by intermediate convolution layers. To further improve the performance, we also design an efficient low-dimensional pixel attention (LPA) mechanism for SISR tasks and summarize several design guidelines. Thanks to LPA module, our CFNet improves the final reconstruction quality with little parameter cost. Extensive experimental results show that the proposed CFNet achieves a better trade-off against the state-of-the-art methods in terms of performance and model complexity. Our codes for CFNet are available at https://github.com/knowback/CFNet.
<i>The Encoding Process for Seasons One and Two of Israeli and Palestinian</i>Sesame Street

The programs portrayed a utopian world, by resolving “the conflict” through the creation of the separate and newly independent Palestinian Street. Internally to it, they modeled and offered opportunties for the simulation of pro-social inter-“group” contact and friendship, including through targeted behavior change curricular components. This chapter describes the process of producing the series, from the intended encoding, or curriculum design, to the adapted encoding of the curriculum into a screenplay with characters, to the expressed encoding, the ultimate episodes edited together, to their broadcast and evolution over two seasons. The series satisfied the five contact hypothesis “requirements” for successfully reducing prejudice, including by substituting official support recommended for interpersonal encounters with the brand-name appeal of Sesame Workshop, together with Israeli and Palestinian elites’ approval of the series. As discussed in Part II, parents’ trust of the name may have helped boost children’s exposure. For decoding analyses, the author edited together a mock episode featuring the key elements necessary to compare the Palestinian, Jewish Israeli, and Arab/Palestinian Israeli children’s decodings against the producers’ encodings within the context of the Israeli-Palestinian ethno-political and Arab-Israeli multi-state conflicts
On using a Low-Density Flash Lidar for Tracking Closely Spaced Road Vehicles

This paper focuses on developing an algorithm for tracking closely-spaced road vehicles using a low-density flash lidar. Low-density flash lidars have poor spatial resolution that can causes detections from multiple targets to be merged/unresolved when the targets being tracked are closely spaced. Previous solutions for target tracking with unresolved measurements have typically focused on tracking with exactly two targets with a radar. In this paper, a novel solution based on Probability Density Function (PDF) truncation of the target states is presented to handle the unresolved measurement problem with more than two road targets. The solution proposed can work with a computationally inexpensive data association algorithm and requires no sensor modeling. For illustration, the proposed algorithm is evaluated using simulations for a three target tracking scenario in MATLAB, and the results show that the proposed algorithm can reliably maintain tracks of multiple targets even when the detections are unresolved.
도시지역 지하도로 연결로 접속부의 효율적인 교통제어를 위한 요소기술 우선순위 선정

PURPOSES : This study prioritizes the potential technology for establishing an efficient traffic control in the ramp junction of urban deep underground tunnels in the future. We considered most of the applicable technologies that ensure traffic safety at the on-off ramp junction. METHODS : This study proposes a methodology to prioritize the applicable technology for establishing efficient traffic control in the ramp junction of an urban deep underground tunnel using an analytical hierarchy process (AHP). First, an AHP structure was developed. Second, an individual survey was conducted to collect the opinions of road and transportation experts. Based on the survey results, weights were estimated depending on the relevant criteria of the developed structure. The estimated weights were verified using the consistency index (CI) and consistency ratio (CR). In addition, a sensitivity analysis was performed to confirm the reliability of the estimated weights. Finally, the potential technology for an efficient traffic control in the ramp junction of an urban deep underground tunnel was prioritized. RESULTS : In the first level of hierarchy, traffic demand control had the highest priority, and ramp metering, section speed control, and shoulder lane control were selected in the second level of hierarchy. CONCLUSIONS : These results implied that prioritizing would be useful in establishing traffic operation strategies for traffic safety when constructing and opening deep underground tunnels in urban areas in the future.
Basic Approaches to Ethnographic Film Usage as a Method of Developing of Ethnocultural Competence of Future Journalists

The article discusses the features of the application of the methodology of visual education based on the use of ethnographic films in the organization of independent work of future journalists. On the basis of emic and ethical approaches, the main functions of ethnographic cinema are identified and described in the light of creating an image of ethnoculture in the process of training future journalists. The principles of representation of ethnoculture in the media sphere are considered, on the basis of which the ethnological motivation of future journalists is formed as the basis of ethnocultural competence.
Robust image watermarking using the social group optimization algorithm

Watermarking images is essential for several reasons like security, validation etc. The extracted watermark should have similar characteristics as that of the watermark image embedded during the watermarking process. Also, it is important that the watermark image should not differ the features of the host image after embedding. In order conceive this features, it is important to have a technique of watermarking. In this paper, such an attempt has been made to incorporate the novel techniques of watermarking using single value decomposition (SVD), discrete wavelet transformation (DWT) and social group optimization algorithm (SGOA). The simulation-based experiment has been carried out to evaluate the performance of the technique in terms of structural similarity index (SSI). The Matlab environment is used for the simulations.
Optimization approach to the choice of explicable methods for detecting anomalies in homogeneous text collections

The problem of detecting anomalous documents in text collections is considered. The existing methods for detecting anomalies are not universal and do not show a stable result on different data sets. The accuracy of the results depends on the choice of parameters at each step of the problem solving algorithm process, and for different collections different sets of parameters are optimal. Not all of the existing algorithms for detecting anomalies work effectively with text data, which vector representation is characterized by high dimensionality with strong sparsity.The problem of finding anomalies is considered in the following statement: it is necessary to checking a new document uploaded to an applied intelligent information system for congruence with a homogeneous collection of documents stored in it. In such systems that process legal documents the following limitations are imposed on the anomaly detection methods: high accuracy, computational efficiency, reproducibility of results and explicability of the solution. Methods satisfying these conditions are investigated.The paper examines the possibility of evaluating text documents on the scale of anomaly by deliberately introducing a foreign document into the collection. A strategy for detecting novelty of the document in relation to the collection is proposed, which assumes a reasonable selection of methods and parameters. It is shown how the accuracy of the solution is affected by the choice of vectorization options, tokenization principles, dimensionality reduction methods and parameters of novelty detection algorithms.The experiment was conducted on two homogeneous collections of documents containing technical norms: standards in the field of information technology and railways. The following approaches were used: calculation of the anomaly index as the Hellinger distance between the distributions of the remoteness of documents to the center of the collection and to the foreign document; optimization of the novelty detection algorithms depending on the methods of vectorization and dimensionality reduction. The vector space was constructed using the TF-IDF transformation and ARTM topic modeling. The following algorithms have been tested: Isolation Forest, Local Outlier Factor and One-Class SVM (based on Support Vector Machine).The experiment confirmed the effectiveness of the proposed optimization strategy for determining the appropriate method for detecting anomalies for a given text collection. When searching for an anomaly in the context of topic clustering of legal documents, the Isolating Forest method is proved to be effective. When vectorizing documents using TF-IDF, it is advisable to choose the optimal dictionary parameters and use the One-Class SVM method with the corresponding feature space transformation function.
Towards Better Support for Undergraduate Software Engineering Teams

Team-based projects are increasingly used within software engineering education because they can teach valuable communication and collaboration skills to help prepare students for professional software engineering positions. However, team-based projects are not without their downsides: in particular, poor communication or a lack of participation can endanger the success of the project. We propose identifying metrics and building a predictive model to help instructors detect when teams are facing harmful dynamics, and evaluations to assess the metrics and their impact on teams in undergraduate software engineering courses.
Language Scaling

Language scaling aims to deploy Natural Language Processing (NLP) applications economically across many countries/regions with different languages. Language scaling has been heavily invested by industry since many parties want to deploy their applications/services to global markets. At the same time, scaling out NLP applications to various languages, essentially a data science problem, remains a grand challenge due to the huge differences in the morphology, syntaxes, and pragmatics among different languages. We present a comprehensive survey and tutorial on language scaling. We start with a clear problem description for language scaling and an intuitive discussion on the overall challenges. Then, we outline two major categories of approaches to language scaling, namely, model transfer and data transfer. We present a taxonomy to summarize various methods in literature. A large part of the tutorial is organized to address various types of NLP applications. Finally, we discuss several important challenges in this area and future directions.
Multicultural creativity in education

This paper is devoted to the main problems with defining the concept of multicultural education, analyzing various approaches to the organization of this type of education and highlighting the main goals of multicultural education in the modern world.
Designing a Digital Education Ecosystem

The aim of this scientific publication is to examine the challenges for the digital education ecosystem in a crisis situation caused by COVID-19. In the current aggravated situation, with introduced movement restrictions around the world, the most important step to instant and high-quality adaptation of the educational system is to make it flexible. This will allow gaining complete and necessary knowledge, introducing the maximum variety of forms and methods of teaching and the technical perfection of teaching equipment. The modern trend of digital transformation of the branches of the socio-economic system determines the urgent need for the operational adaptation of the educational environment to the professional and additional needs of society, technical modernization and the priorities of social and economic development. A change in the management of education is needed in order to find solutions for the modernization of education in a digital environment in which the quality of education is maintained at a level corresponding to the socio-economic processes.
Research on Railway Rescue Scheme Matching Method Based on Confidence Mixed Similarity

In recent years, the railway emergency accompanied by the rapid development of China’s railway can not be ignored. The important enlightenment to us from the accident death toll and huge property loss is that we need to make a reasonable dispatch of rescue resources in time to provide decision support for the formulation of rescue plan. Case-Based Reasoning (CBR), is to select one or more most similar cases from the case base according to the current new events combined with certain calculation methods, and modify its solution according to the actual situation to get the solution of the new problem. Therefore, this paper proposes a confidence mixed similarity case matching method based on case-based reasoning system. First of all, the rough set theory is used to determine the weight of each attribute of the railway emergency. Secondly, the rescue cases of similar structures are obtained by using the calculation of structural similarity. Third, in order to better mine the similarity between the attributes of accident cases, the attributes are classified to calculate the attribute similarity. Fourth, the historical cases can be screened by combining structural similarity and attribute similarity. Finally, the decision tree gain algorithm is used to establish a decision tree to determine the confidence index of the rescue plan. At the end of this paper, we extract some cases from the case base and calculate the similarity and confidence between them and the target cases to obtain the rescue plan, which verifies that the results obtained by the analysis are logical and reliable.
A brain-inspired localization system for the UAV based on navigation cells

Purpose To solve problems of low intelligence and poor robustness of traditional navigation systems, the purpose of this paper is to propose a brain-inspired localization method of the unmanned aerial vehicle (UAV). Design/methodology/approach First, the yaw angle of the UAV is obtained by modeling head direction cells with one-dimension continuous attractor neural network (1 D-CANN) and then inputs into 3D grid cells. After that, the motion information of the UAV is encoded as the firing of 3 D grid cells using 3 D-CANN. Finally, the current position of the UAV can be decoded from the neuron firing through the period-adic method. Findings Simulation results suggest that continuous yaw and position information can be generated from the conjunctive model of head direction cells and grid cells. Originality/value The proposed period-adic cell decoding method can provide a UAV with the 3 D position, which is more intelligent and robust than traditional navigation methods.
RETRACTED ARTICLE: Rock and soil reconstruction of mountain slope based on embedded Internet of things and evaluation of college students’ physical health

Based on embedded technology and wireless mobile communication technology, the development of physical data acquisition system is realized, and the overall architecture of physical data acquisition system is designed. The system is composed of sensor terminal and application service center. The application service center takes S3C2440 development board as the core, carries out remote information collection and data transmission through the Internet of things and uses VC + + 6.0 as the software development environment of sensor terminal to realize remote management and data analysis. Then, it studies the flexible configuration method of IOT embedded terminal supporting multi-sensor access, as well as the corresponding hardware interface, communication protocol, communication protocol, etc., and designs the middleware and application layer configuration tool. The network embedded terminal equipment realizes the interaction and configuration function of the terminal. Before the geotechnical reconstruction of the slope, the geological conditions of the slope were measured by the measurement technology, and the extreme value balance analysis method was used to detect the slope surface to observe the stability. The results show that if the rising coefficient is less than or equal to 1.0, the rising is unstable. When the safety factor exceeds 1.15, the recalculation rate of some treatment regimens remains stable. Then, the embedded Internet of things is used to detect the physical health of college students. Based on the college students’ physical health evaluation method, the evaluation model of college students’ health test is established. The evaluation grades of boys and girls are relatively concentrated in the passing and good groups, which indicates that the overall situation is good. In this paper, through the study of the mountain slope geotechnical transformation and college students’ physical health evaluation, it is applied to the embedded Internet of things, so as to promote the development of geotechnical engineering and college students’ physical health.
Forensic examination of effects of rheumatoid arthritis on handwriting characteristics

Abstract Background Rheumatoid arthritis is known to produce deformity of the joints and muscles which can affect the handwriting of an individual significantly. Examination of such handwriting specimens can become a challenging task for the document examiners. This can affect the accuracy of their opinions upon the related cases. Thus, the present study was conducted to determine the effects of rheumatoid arthritis on handwriting characteristics qualitatively and statistically. The study also included qualitative analysis and comparison of line quality defects of copied writings and writings affected by rheumatoid arthritis. Results Handwriting characteristics were analyzed qualitatively and statistically in old writings vs affected writings. All these handwriting characteristics were also analyzed qualitatively in copied writings vs affected writings. The results of the study showed significant deteriorating effects of rheumatoid arthritis on almost all handwriting characteristics, except for characteristics such as slant, retouching, and overwriting. The pictorial appearance of letters in the writings executed before and after the onset of illness was even observed to appear different altogether in case of some individuals with severely affected writings. Moreover, the analysis of copied writings indicated the execution of writing by a different person. Handwriting characteristics such as slant, spacing, and alignment were observed to show insignificance in analyzing differences between copied and genuine writings affected by the illness. Conclusion It has been concluded that rheumatoid arthritis causes deterioration of letters, tremor, disturbed rhythm, and a huge variation in other handwriting characteristics. The copied writings have also presented line quality defects like artificial tremor, hesitations, retouching, and unnatural pen lifts at all wrong places. The determination of these peculiarities may assist the document examiners in the differentiation of copied and affected writings.
Yemeni Banknote Recognition Model based on Convolution Neural Networks

Differentiating between paper currencies with different designs is a challenging task for visually impaired individuals and automated banking machines. Safe and accurate paper currency recognition systems are highly required, because of the wide use of Automated Teller Machines (ATMs), foreign exchange, automatic selling of goods, and automated banking services. With the advances of pattern recognition techniques, many real-life problems have been resolved. This paper presents a robust method to recognize various Yemeni paper currency using pre-trained models. To perform effective recognition processes, deep learning approach is used. Three pre-trained models are implemented which are AlexNet, DenseNet121, and ResNet50. The obtained results of the proposed method achieved high validation accuracy. This robust model reaches a value of 96.99%, 96.83%, and 99.04% in terms of validation accuracy using ResNet-50, DenseNet121, and AlexNet respectively.
Context aware Named Entity Recognition with Pooling

Natural Language Processing is concerned with processing and analyzing natural language with the help of a computer, i.e., any language that is spoken or written by us. Natural language processing is one of the fundamental elements of AI. Named Entity Recognition is a standard Natural Language Processing problem which deals with information extraction. NER involves the automatic scanning through unstructured text to locate entities.Many of the Named Entity Recognition systems available are not context aware. Many a times a named entity appears in a text and it is used in that context in some other context. For eg. the word Washington may be either a person or location. The actual named entity depends upon the context in which the word Washington is used. It is possible that the NER might not recognize it correctly since none of it saves the context anywhere. This paper revolves around the exploration about making the algorithm more accurate by making use of different pooling mechanisms. The pooling mechanisms can be used along with the BiLSTM + CRF architecture to get the entire sentence context.
Saliency based Subject Selection for Diverse Image Captioning

Image captioning has drawn more and more attention because of its practical usefulness in many multimedia applications. Multiple criteria such as accuracy, detail or diversity exist to evaluate the quality of generated captions. Among them, diversity is the most difficult because for a given image, its multiple captions should be generated while retaining their accuracy. We approach to diverse image captioning by explicitly selecting objects in an image one by one as a subject in generating captions. Our method has three main steps: (1) After generating scene graph of a given image, we first give selection priority to the nodes (namely, subjects) in the scene graph based on the size and visual saliency of objects. (2) With a selected subject, we prune a portion of the scene graph structure that is irrelevant to the subject to have subject-oriented scene graph for accurate captioning. (3) We convert the subject-oriented scene graph into its more sentence-friendly abstract meaning representation (AMR) to generate the caption whose the subject is the selected root. In this way, we can generate captions whose subjects are different from each other, achieving diversity. Our proposed method achieves comparable results with other methods in both diversity and accuracy.