Reviewerly/exHarmony huggingface dataset:
Design of a university English micro-course system based on mobile learning mode

Mobile teaching is the current development trend of English teaching. The traditional English teaching system has low load capacity due to the design defects of hardware, and it cannot be targeted to teach and stimulate students' learning interest when it is actually applied. To address the above problems, we design a university English micro-course system based on mobile learning mode. After optimizing the resource calling instruction memory and system communication connection port in the hardware part of the system, the system software part integrates English micro-lesson resources and recommends teaching resources by using collaborative filtering algorithm to complete the system design. The comparison experimental results show that the carrying capacity of the system is improved by about 53%, and the actual use can effectively improve students' English learning ability.
Assessment of Machine Learning-Based Audiovisual Quality Predictors

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.
A novel approach for <scp>nature‐based</scp> optimization algorithms: The threat factor approach

Abstract Nature‐inspired optimization algorithms especially those based on the hunting behaviors of the creatures assume that the hunting operations are performed in a safe environment. However, generally, there are threats in real‐life for the hunter‐animals. This paper focuses on these threat factors and proposes that they can be used to improve the searching abilities of the algorithms. Gray wolf optimization (GWO) algorithm was selected to present the proposed approach and it was assumed that there was a mountain lion as the threat factor living in the same habitat with the wolf pack. The relations between the two predators were modeled and used to improve the performance of the algorithm. Five experiments were conducted to test the performance of the proposed method and the results were compared with the GWO and four optimization algorithms from the literature. It is shown that the proposed algorithm obtained best results for 21 of the 50 benchmark functions, while its closest competitor achieved the best results for 16 functions. Besides, the results of the Wilcoxon signed‐rank test indicated that the proposed method is superior to all other methods. In addition, it was shown that the threat factor approach does not cause a significant increase in the processing time.
Research on an evaluation method of rice processing loss

In this article, from the perspective of rice processing loss, this paper proposes a method to evaluate the processing loss of rice produced by enterprises using data analysis. The specific content of this method is to build a fitting simulation model of unit power consumption and rice production rate based on production data of various types of enterprises across the country at different scales, equipment, and technological processes, thereby establishing a rice processing loss evaluation system; Based on the production data of the enterprise to be evaluated, an enterprise data fitting optimization model of unit power consumption and rice production rate was constructed; and the results of evaluation and diagnosis were combined with processing losses of rice processing enterprises. The application process of this evaluation method found the change law of unit power consumption and rice production rate in different dimensions, and gave evaluation conclusions and optimization plans for the evaluation of the production status of the enterprise. The application of this evaluation method to the grain industry can be a rice processing enterprise. Provide practical guidance on energy saving and consumption reduction during processing.
Emotional Maturity and Academic Achievement among Adolescents of Kashmir

The research study was carried to explore the Emotional Maturity and Academic Achievement among adolescents of Kashmir. Descriptive survey method was used in the current study and the respondents were selected through stratified random sampling technique. To meet the required goal 400 secondary school students were selected as adolescents (200 males and 200 females) among them 200 each from government and private schools of Kashmir division. Kulgam and Baramullah district represented the south and north Kashmir respectively for data collection. The tools used for data collection were Emotional Maturity scale by Sabapathy (2017) and Academic Achievement means the aggregate marks obtained by the sample group of students from there previous year examination.  To analyze and interpret the data percentage, mean, S.D, t-test and pearsons product movement correlation method were used as statistical techniques. The result findings revealed that 10.25%, 55.25% and 34.50% of low, moderate and high emotional maturity were found among adolescents of Kashmir respectively. A significant difference was found between male and female adolescents of Kashmir on emotional maturity. A significant difference was found between government and private school students on academic achievement. A significant positive correlation was found between emotional maturity and academic achievement among secondary school students of Kashmir.
Analysis of Computer Science Based on Big-Data Mining

The scientific construction of a first-class discipline construction evaluation system is of great significance to the promotion of discipline construction. As an important evaluation reference system, the third-party evaluation system must pay attention to its underlying data sources and calculation methods. Using the massive underlying data of the Scopus database, through the analysis of the computer disciplines of four Chinese universities, the development trend is discussed from the aspects of overall academic output, scientific research quality, and hot topics.
Leveraging digital platforms for disseminating health and nutrition information during COVID-19: reflections from Project <i>Samvad</i> in India

Countries around the globe, including India, are making strides to combat maternal and child health issues. To support these efforts, Digital Green implemented Project Samvad , a USAID-funded reproductive, maternal, newborn, and child health (RMNCH) project working across six states in India. The project used a human-mediated community video approach to improve maternal and child health outcomes. In early 2020, due to mobility-related restrictions and norms related to physical distancing resulting from the COVID-19 pandemic, Project Samvad ’s field activities stalled. This descriptive study highlights the project’s pivot to the use of digital tools to reach beneficiaries during the COVID-19 pandemic. The project delivered COVID-19 related information by layering it on the existing RMNCH messages. It used WhatsApp and Interactive Voice Response System to reach out to the community and frontline workers. The content of the messages was locally relevant, culturally appropriate, and addressed the concerns of the local community concerning their immediate needs related to RMNCH and COVID-19. The project recorded a listenership of 85,199 and a content viewership of 283,866 in three months. The initiative also oriented 918 frontline workers virtually on their roles and responsibilities during COVID-19. The insights from the adaptation of our approach in the context of COVID-19 can help project managers and policymakers address communication issues during the pandemic or any other emergency, using digital dissemination for reaching beneficiaries, building the capacities of partners, engaging community members and frontline workers for improved learning, and dealing with process-related challenges.
Exploiting Knowledge Embedding to Improve the Description for Image Captioning

Most existing methods for image captioning are based on the encoder-decoder framework which directly translates visual features into sentences, without exploiting commonsense knowledge available in the form of knowledge graph. Inspired by the success of information retrieval and question answering systems that leverage prior knowledge, we explore a knowledge embedding approach for image captioning. In this paper, we propose a Knowledge Embedding with Attention on Attention (KE-AoA) method for image captioning, which judges whether or how well the objects are related and augments semantic correlations and constraints between them. The KE-AoA method combines knowledge base method (TransE) and text method (Skip-gram), adding external knowledge graph information (triplets) into the language model to guide the learning of word vectors as the regularization term. Then it employs the AoA module to model the relations among different objects. As more inherent relations and commonsense knowledge are learned, the model can generate better image descriptions. The experiments on MSCOCO data sets achieve a significant improvement on the existing methods and validate the effectiveness of our prior knowledge-based approach.
A Power Customer Data Relational Algorithm Based on Magnanimity Fuzzy Address Matching

According to the short text and unstructured characteristics of customer address, a data association fusion method for address has been proposed. In this method, the address was mapped to a digital fingerprint by improved Simhash technology, which effectively reduced the dimension of massive addresses and simplified the similarity-matching process of multi-source heterogeneous addresses. Furthermore, the weight setting of the eigenvector of the simhash algorithm was improved by introducing special weight gain. A two-level index mechanism was established by the characteristics of address division and data structure of digital fingerprints; the time-consuming digital fingerprint comparison was greatly reduced. The experimental results showed that calculation efficiency was greatly optimized; accuracy and coverage of the comparison were ensured. Through address matching of different databases, information fusion can be completed and the goal which power customers' demands is connected to power grid equipment is achieved.
Research on FPGA High-Performance Implementation Method of CNN

Convolutional Neural Networks (CNN) are widely used in such fields as image recognition, object detection and image segmentation. These application scenarios have high requirements on real-time data processing capabilities. With the advantages of good flexibility, strong parallel processing ability and low power consumption, Field Programmable Gate Array (FPGA) is very suitable as a hardware platform for processing CNN. Based on the CNN model Lenet-5, this study will explore the high-performance implementation methods for using CNN in image recognition, and further design and implement a CNN hardware accelerator IP core based on FPGA. First, it uses Pytorch to train the network model and extract the weight parameters; then, it uses C language in the Xilinx High-level Synthesis (HLS) to model the forward inference structure of the CNN. The model designed is then optimized with different optimization strategies, including storage structure optimization, pipeline optimization and fixed-point quantization, so as to improve the performance of the hardware accelerator. Finally, the optimized design model is converted into a hardware accelerator through HLS. The experimental results demonstrate that the implementation performance of the CNN accelerator with different optimization strategies can be greatly improved, and it can meet the requirements of high-real-time application scenarios of CNN.
Data Visualization for Interdisciplinary Medical Research (Pilot Study)

Visualization is one of the key aspects of presenting the results of scientific research. The authors propose an experiment design to analyze the perception of medical data visualizations in solving analytical problems. Perception analysis involves processing a combination of data: eyetracking data, tasks solving performance (time spent, answer accuracy, etc.), think-aloud data, and subjective preferences survey. As the research material, the authors used an anonymized database on 60 patients with Multiple Sclerosis treated by Hematopoietic Stem-Cell Transplantation (HSCT). The database contained about 150 parameters. The study confirmed eligibility of the proposed approach.
Recursive Algorithms for Visual Target Tracking Under Colored Measurement Noise

Visual target tracking is often accompanied with colored measurement noise (CMN) caused by the object and camera frame dynamics, which require special estimation algorithms. In this paper, we consider the Kalman filter (KF) and unbiased finite impulse response (UFIR) filter modified for Gauss-Markov CMN. The algorithms are designed employing measurement differencing and demonstrate a better performance under the CMN then the standard algorithm. Experimental investigations are conducted for several benchmark trajectories, where a target is tracked using a video camera.
Acquisition and Modeling of Website Parameters

During the last 30 years, the web has evolved from simple information HTML pages to complex applications supporting business, television, newspapers, entertainment, and others. While there are many articles on website popularity, there has been little work in understanding the complexity of individual web pages. In the article, we present a measurement-driven study of the complexity of web pages today. We measured 426 866 web pages in about 12 weeks. Our study is devoted to two problems. The first problem was to describe the complexity of a web page with metrics based on the content they included and the kind of service they offered. The second focus of our study was to build probabilistic models of observed distributions. Such models can be used in HTTP request generators modelling the work of modern web systems. Separate models are proposed for each category of web pages and all pages together.
Ternary Classification of Product Based Reviews: Survey, Open Issues and New Approach for Sentiment Analysis

Now-a-days, it is very common that the customers share their thoughts about any product, brand and their experience in social media. The analysts collect these reviews and process it, to extract meaningful information about the product. The beauty of social media is, it’s involved in all the domains. So the analysts got reviews from different social media and platforms for almost all kind of thing. The Sentiment Analysis is applied to predict outcomes for getting useful information, for ex.; like predict the blockbuster for a movie, rating for any new launches and many more. This type of prediction is really helpful for the customer to buy any goods or take any services in this competitive world. This paper is focused on e-commerce website reviews which are normally in text form with some special characters and some symbols (emojis). Each word in this text set got some meaning in terms of context, emotion and prior experience. These characteristics contribute to some of the features of text data for prediction. The objective of this paper is to compile existing research works on text analysis and emotion based analysis. The open issues and challenges of document based sentiment analysis are also discussed. The paper concluded with proposing a new approach of multi class classification. Ternary classification for classes positive, negative and neutral is suggested primarily for product based text and emoji reviews on Twitter social media.
Zero-Shot Learning-Based Detection of Electric Insulators in the Wild

An electric insulator is an essential device for an electric power system. Therefore, maintenance of insulators on electric poles has vital importance. Unmanned Aerial Vehicles (UAV’s) are used to inspect conditions of electric insulators placed in remote and hostile terrains where human inspection is not possible. Insulators vary in terms of physical appearance and hence the insulator detection technology present in the UAV in principle should be able to identify an insulator device in the wild, even though it has never seen that particular type of insulator before. To address this problem a Zero-Shot Learning-based technique is proposed that can detect an insulator device, that has never seen during the training phase. Different convolutional neural network models are used for feature extraction and are coupled with various signature attributes to detect an unseen insulator type. Experimental results show that inceptionsV3 has better performance on electric insulators dataset and basic signature attributes; “Color and number of plates” of the insulator is the best way to classify insulators dataset while the number of training classes doesn’t have much effect on performance. Encouraging results were obtained.
Probabilistic Word Embeddings in Kinematic Space

In this paper, we propose a method for learning representations in the space of Gaussian-like distribution defined on a novel geometrical space called Kinematic space. The utility of non-Euclidean geometry for deep representation learning has recently been in vogue, specifically models of hyperbolic geometry such as Poincaré and Lorentz models have proven useful for learning hierarchical representations. Going beyond manifolds with constant curvature, albeit has better representation capacity might lead to unhanding of computationally tractable tools like Riemannian optimization methods. Here, we explore a pseudo-Riemannian auxiliary Lorentzian space called Kinematic space and provide a principled approach for constructing a Gaussian-like distribution, which is compatible with gradient-based learning methods, to formulate a probabilistic word embedding framework. Contrary to, mapping lexically distributed representations to a single point vector in Euclidean space, we advocate for mapping entities to density-based representations, as it provides explicit control over the uncertainty in representations. We test our framework by embedding WordNet-Noun hierarchy, a large lexical database, our experiments report strong consistent improvements in Mean Rank and Mean Average Precision (MAP) values compared to probabilistic word embedding frameworks defined on Euclidean and hyperbolic spaces. We show an average improvement of 72.68% in MAP and 82.60% in Rank compared to the hyperbolic version. Our work serves as evidence for the utility of novel geometrical spaces for learning hierarchical representations.
New space opens at Menomonee Falls Public Library

Brian is in Menomonee Falls checking out their public library where guests can create, invent and learn in their new Makerspace.
Fast Style Transfer with High Shape Retention

Since deep learning was introduced into style transfer, remarkable results have been achieved in it and it is widely used in multimedia fields, such as photography. However, the computational costs of the existing state-of-the-art (SOTA) arbitrary style transfer algorithms are still too complex to apply them on mobile device and high resolution, and their performance on shape retention is not satisfactory enough. To deal with the above problems, we propose a novel arbitrary style transfer algorithm. Specially, we propose a new network which ensures the low computational cost and high shape retention. Moreover, we propose the weighted style loss function to improve the performance on style migration. The experimental results show that the proposed algorithm achieves better results with lower computational cost than the SOTA algorithms.
Improving the Efficiency of Deduplication Process by Dedicated Hash Table for each Digital Data Type in Cloud Storage System

Data storage is one of the significant cloud services available to the cloud users. Since the magnitude of information outsourced grows extremely high, there is a need of implementing data deduplication technique in the cloud storage space for efficient utilization. The cloud storage space supports all kind of digital data like text, audio, video and image. In the hash-based deduplication system, cryptographic hash value should be calculated for all data irrespective of its type and stored in the memory for future reference. Using these hash value only, duplicate copies can be identified. The problem in this existing scenario is size of the hash table. To find a duplicate copy, all the hash values should be checked in the worst case irrespective of its data type. At the same time, all kind of digital data does not suit with same structure of hash table. In this study we proposed an approach to have multiple hash tables for different digital data. By having dedicated hash table for each digital data type will improve the searching time of duplicate data.
Voices, Combat, and Music

Role-playing video games can highlight a sense of player identification with a pre-made player-character through the inclusion of the voice; this can include diegetic character dialogue, the non-diegetic sung voice, and voice-overs. Ensuring that the player can connect with their player-character on an individual level is key to forming a suspension of disbelief and immersion with a game. Final Fantasy XV emphasizes collaboration between the player, player-characters, and non-playable characters during combat, cutscenes, and exploration. This camaraderie effect actively engages the player with multiple characters through the constant use of the diegetic spoken voice to communicate between the player-character, Noctis, and the three other character identities, Prompto, Ignis, and Gladiolus. The game’s opening rendition of Ben E. King’s “Stand by Me,” performed by Florence + the Machine, provides the player with four narrative themes that are present throughout the game. These themes include a coming-of-age story, character relationships, and the development of a greater evil within the narrative. Unique in its position as a pre-composed song, “Stand by Me” plants the idea that the player will not be alone in their journey, as character allies will stand by Noctis, from the beginning of the game. Final Fantasy XV uses vocalizations in both the diegetic and non-diegetic audio space in order to reflect the characteristics of the playable characters. The vocal relationship between the player’s characters provides directional feedback and information for the player within combat, alongside foretelling the stories of certain characters.