Reviewerly/exHarmony huggingface dataset:
Distribution-Aware Hierarchical Weighting Method for Deep Metric Learning

In this paper, we propose distribution-aware hierarchical weighting (DHW) method for deep metric learning. First, we formulate the distributions of different classes according to the form of gaussian curves, and update distributions as the training process. Second, depending on the learnable distribution, we propose a loss function named distribution-aware loss with dynamic mining margins and hierarchical degrees of weights to make full use of samples. The experimental results show that our algorithm outperforms other state-of-the-art methods in terms of retrieval and clustering tasks. Code is available at https://github.com/zhuyinong1/DHW-master.
A Study on the Applicable Methods of Flipped Classroom for Cross-Border Education

Together with the advance of globalization, the internationalization of higher education is becoming more and more diverse. Cross-border, broad-based higher education associations is more complex than that of high school and university within the same country and poses more challenges in terms of quality assurance. On the other hand, with the recent development of ICT, Flipped Classroom, a form of education that utilizes e-learning, has been attracting attention in higher education in many countries, including Japan. Based on the results of a questionnaire survey to Japanese and Chinese university students and an analysis of case studies of pre-entrance education initiatives using e-learning, this paper discusses the possibilities and challenges for applicable methods to flipped classroom of pre-entrance education as an attempt to facilitate cross-border educational associations.
Recovering contemporary genre histories – the development of chick lit as seen through the internet archive’s wayback machine and wikipedia’s history page

Abstract In the perception of literary scholars, the investigation of genre histories is still closely linked to ‘offline’ archival work. However, the Internet has been publicly accessible since 1991, and over the last thirty years, numerous new literary genres have emerged. They have often been proclaimed, defined, spread, marketed, criticized, and even pronounced dead online. By now, a great deal of this digital material is said to have disappeared. What many scholars do not consider, however, is that parts of the web are archived, for example by the Internet Archive and Wikipedia, which make their archives publicly available via the Wayback Machine and the history page respectively. This makes it possible to track early online definitions of contemporary genres and their development. In this paper, I will use the chick lit genre, which emerged in the second half of the 1990s, as a case study to show the benefits of including web archives in the reconstruction of contemporary genre histories. An analysis of both the first extensive and long-running fan websites, which are now offline but well-documented in the Internet Archive, and the history page of the Wikipedia article on chick lit will challenge some of the narratives that have long dominated chick lit research.
Malaysia : Balancing national development, national security, and cybersecurity policy

The purpose of this chapter is threefold. First, to examine the Internet booms and the growth of the Information and Communication Technology (ICT) in Malaysia and how it impacts Malaysia’s national security. Second, it also examines the nature and substance of Malaysia’s cybersecurity frameworks. Third, it discusses Malaysia’s strategy to manage the multitude of cybersecurity challenges within its border, including the emerging trend of security threats such as cyber warfare, cyberterrorism, and cybercrime.
Object Detection on Monocular Images with Two- Dimensional Canonical Correlation Analysis

Accurate and robust detection of objects from monocular images is a fundamental vision task. This paper describes a novel approach of holistic scene understanding that can simultaneously achieve multiple tasks of scene reconstruction and object detection from a single monocular image. Rather than pursuing an independent solution for each individual task as most existing work does, we seek a globally optimal solution that holistically resolves the multiple perception and reasoning tasks in an effective manner. The approach explores the complementary properties of multimodal RGB images and depth data to improve scene perception tasks. It uniquely combines the techniques of canonical correlation analysis and deep learning to learn the most correlated features to maximize the modal cross-correlation for improving performance and robustness of object detection in complex environments. Extensive experiments have been conducted to evaluate and demonstrate the performances of proposed approach.
Acquisition of the Width of a Virtual Body through Collision Avoidance Trials

The progress of immersive technology enables researchers and developers to construct work spaces that are freed from real-world constraints. This has motivated us to investigate the role of the human body. In this research, we examine human cognitive behaviors in obtaining an understanding of the width of their virtual body through simple yet meaningful experiments using virtual reality (VR). In the experiments, participants were modeled as an invisible board, and a spherical object was thrown at the participants to provide information for exploring the width of their invisible body. Audio and visual feedback were provided when the object came into contact with the board (body). We first explored how precisely the participants perceived the virtual body width. Next, we examined how the body perception was generated and changed as the trial proceeded when the participants tried to move right or left actively for the avoidance of collision with approaching objects. The results of the experiments indicated that the participants could become successful in avoiding collision within a limited number of trials (14 at most) under the experimental conditions. It was also found that they postponed deciding how much they should move at the beginning and then started taking evasive action earlier as they become aware of the virtual body.
Aspect-Oriented Sentiment Classification using BiGRU-CNN model

People on the Internet have generated a large amount of commentary data to share their opinions about products and services in their daily lives which include large commercial value. For these comment sentences, they often include several comment aspects, and the sentiment varies on these aspects, making the overall meaning of the sentence meaningless for polarization. The purpose of the aspect-level sentiment classification is to recognize target’s sense extremity in context. Deep Learning is evolving in an increasingly mature direction, and the utilization of deep learning methods to detect emotion has become increasingly popular. A sentiment classification model is propsoed by combining a convolutional neural network and a bidirectional gated recurrent unit.Bidirectional gated recurrent unit is similar to Long short-term memory, a time cyclic neural network with a lesser processing complexity. The model first extracts sequence features of the text through the bidirectional gated recurrent unit and then extracts the local static features of the text through the convolutional neural network. Finally, the Sigmoid classifier is used for the final sentiment classification.
A Multi-Modal Edge Consistency Metric Based on Regression Robustness of Truncated SVD

In this paper, we propose a novel edge consistency metric for multi-modal correspondence. It is based on a novel observation on image truncated SVD (singular value decomposition) termed regression robustness, which describes the fact that, a good approximation from image truncated SVD can be inherited even if the eigen-images change due to expansion and channel-dependent offsets. Compared to state-of-the-arts, multi-modal edge consistency metric can simultaneously handle multiple images with complex modality changes, including local variation, gradient reverse, intensity order change, and texture loss. Its complexity is almost linear to pixel number. Remarkable accuracies have been achieved in experiments.
Interpolation and masking effects of heteroassociative compressive transformations

Abstract The article considers heteroassociative image transformations, which perform mutual mapping of two neighbouring or nested regions of arbitrary shape within rectangular fragments or blocks covering the image. Specifically, the study focuses on heteroassociative compressive transformations. In the article, we provide theoretical justification of various types of heteroassocia-tive transformations performed using neural network based converters and numerical methods. We also study interpolation and masking effects of the transformations, which can be observed during image processing. These effects are accounted for by the possibility to represent an image as a sum of the deterministic component of the output (which is either linear of nonlinear function of the in-put) and the additive stochastic component of the output responsible for the prediction error. The article analyses occurrence, genetic and heuristic algorithms used to optimise the input and output configuration of the fragments covering the image. The algorithms used allowed us to maximise (minimise) the deterministic and the stochastic component and thus regulate interpolation and masking effects of heteroassociative transformations.
Application of The Big 6 Skills Model and Information Literacy Skills for Surveying Subject at Vocational School

Abstract This study aims to identify students’ information literacy skills using the Big 6 Skills model in Vocational High Schools’ survey subjects. The research method used is descriptive quantitative with survey design. The total sample is 80 vocational students using simple random sampling. The big skills 6 model consists of 6 aspects: Definition; Strategic Information Seeking; Location and Access; Use of Information; Synthesis and Evaluation. This study indicates that the students’ information literacy skills in formulating problems and information-seeking strategies are quite good. Meanwhile, the ability to allocate sources, locations, access, and synthesis and evaluation such as utilizing information, organizing information, and evaluating information is still lacking. Thus it can be concluded that the information literacy skills of SMK students are not optimal.
Personalized Recommender Systems: An Empirical Analysis

A recommender system is a type of information gathering/analysis system that aims to predict the rating that a particular user will give to an item. The main goal of such systems is to efficiently recommend relevant items of interest to their audience for prolonged interaction with the product and maximizing user satisfaction. They are mainly used in commercial applications, some highly popular examples being Netflix, Amazon, etc. This paper aims to provide a descriptive overview of the different algorithms present in the field and undertake research to explore and evaluate the error values obtained.
A novel approach towards voice-based video content search

This paper describes challenges and solutions for building a successful voice-based video content search system. People have been typing to search queries online for the past several decades. But it is easier and more convenient to use voice search than typing the whole text. Voice based search is dominating the market since last few years. With the expanding dominance of smartphones this voice search also steadily growing. We have implemented voice search instead of web search, specifically it is voice-based video content search. A particular word has been provided as voice input in a video file and it is tried move directly to that point of the video. The ffmpeg tool has been used to convert the video file into corresponding file format. Speech recognition module has been used to convert audio file into text. The required attributes from speech recognition module provide us all the possible words similar to the given audio input, so that we won't miss any possible word or sentence. The texts are divided and saved in different locations in an array for every interval of 3 seconds. To access the voice input word, the index value has been multiplied to the duration to get the timestamp where the input word lies. Then the user can select the timestamp shown on the screen and move to the intended location of the video.
GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Semantic Segmentation

Existing CNN-based methods for semantic segmentation heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation. State-of-the-art segmentation networks widely exploit conventional scale-transfer operations, i.e., up-sampling and down-sampling to learn multi-scale features. In this work, we find that these operations lead to scale-confused features and suboptimal performance because they are spatial-invariant and directly transit all feature information cross scales without spatial selection. To address this issue, we propose the Gated Scale-Transfer Operation (GSTO) to properly transit spatial-filtered features to another scale. Specifically, GSTO can work either with or without extra supervision. Unsupervised GSTO is learned from the feature itself while the supervised one is guided by the supervised probability matrix. Both forms of GSTO are lightweight and plug-and-play, which can be flexibly integrated into networks or modules for learning better multi-scale features. In particular, by plugging GSTO into HRNet, we get a more powerful backbone (namely GSTO-HRNet) for pixel labeling, and it achieves new state-of-the-art results on multiple benchmarks for semantic segmentation including Cityscapes, LIP, and Pascal Context, with a negligible extra computational cost. Moreover, experiment results demonstrate that GSTO can also significantly boost the performance of multi-scale feature aggregation modules like PPM and ASPP.
Face Image Quality Assessment for Model and Human Perception

Practical face image quality assessment (FIQA) models are trained under the supervision of labeled data, which requires more or less human labor. The human labeled quality scores are consistent with perceptual intuition but laborious. On the other hand, models can be trained with data generated automatically by the recognition models with artificially selected references. However, the recognition scores are sometimes inaccurate, which may give wrong quality scores during FIQA training. In this paper, we propose a labour-saving method for quality scores generation. For the first time, we conduct systematic investigations to show that there exist severe contradictions between different types of target quality, namely distribution gap (DG). To bridge the gap, we propose a novel framework for training FIQA models by combining the merits of data from different sources. In order to make the target score from multiple sources compatible, we design a method called quality distribution alignment (QDA). Meanwhile, to correct the wrong target by recognition models, contradictory samples selection (CSS) is adopted to select samples from the human labeled dataset adaptively. Extensive experiments and analysis on public benchmarks including MegaFace has demonstrated the superiority of our in terms of effectiveness and efficiency.
Research on Computer Network Information Security Problems and Prevention Based on Wireless Sensor Network

With the continuous improvement of China's scientific and technological level, computer network has become an indispensable part of people's daily life. It can not only effectively improve the efficiency of production and life, and shorten the distance between people, but also further promote the speed of China's social and economic development, which has a positive impact on the realization of China's modernization. Under the new information security demand environment at present, we should pay attention to the related information security work and formulate effective security measures and strategies. In order to effectively prevent these information security problems, people should actively adopt firewall technology, encryption technology, network access control technology and network virus prevention technology for effective protection. This paper analyzes the security problems in the application of wireless sensor networks and explores the mechanism of defending information security, hoping to strengthen the security and stability of wireless sensor networks through effective measures, so that people can better enjoy the convenience brought by the network age.
A prohibition sign recognition method based on multi-features fusion

In order to realize prohibition sign recognition, this paper proposes a new prohibition sign recognition method based on multi-features fusion. The method is mainly divided into three steps: image preprocessing, image rough classification and feature fusion classification and recognition. In the step of image preprocessing, it includes color standardization, image interception based on template matching and core image extraction. In the step of image rough classification, rough classification is carried out based on the color statistics proportion of prohibition sign image. In the step of feature fusion classification and recognition, firstly, the core image grid feature, sampling feature of image scanning, geometric invariants moment feature and the core image region connectivity feature of the prohibition sign image are extracted; then, the different distance calculators and maximum error values are set for the above four types of features to realize the initial recognition based on a single type of features; finally, the weighting method is used for the feature fusion to realize the recognition of prohibition signs.
Research on Hydraulic Characteristics of Navigable Tunnels of High-Dam Navigable Structures and Ship Navigation Test

Abstract To deal with the problem of ship navigation under the ultra-small section coefficient of navigable tunnels of high dam navigation structures, a large-scale hydraulic physical model combined with a self-propelled ship model was used to study the characteristics of the unsteady flow of the tunnel discharge and the navigation parameters of ships at different speeds. This paper analysed the influence of the ship lock discharge on the tunnel water level fluctuation and flow velocity, proposed parameters such as the size of the navigable tunnel for 1,000-ton ships, the recommended speed and the amount of ship sinking, which provide references for the design of navigable tunnels for high-grade waterways.
An Information Service Platform for Geographical Indication Products in Guangxi

Geographical indication products are a symbol of quality. However, not all geographical indication products can be specifically known by consumers. In response to the problem, an information service platform that integrates information services, route planning services and traceability services for geographic indication products in Guangxi is developed by mainly using SSM (SpringMVC, Spring, Mybatis), AngularJS, Ajax and ArcGIS API for JavaScript technologies. In the platform design, SSM and AngularJS are used to build the back-end structure and the front-end structure of the platform, respectively, which the front-end interaction relies on Ajax and ArcGIS API for JavaScript is used to develop the map-related functional services. In the implementation of the system functions, while KindEditor is used to write and edit the content of rich text format and the built FastDFS server base on Alibaba Cloud ECS is used to store pictures and files, the classes QueryTask and RouteTask provided by ArcGIS API for JavaScript are used to implement the localization of origin scope or regional protection scope and the shortest path planning for the distribution of geographical indication products, respectively. Moreover, the traceable quick response codes are generated based on qrious. The implementation of the platform is conducive to the development of geographical indication products.
Effectiveness android-based learning media on basic competence of service company financial statements class X AKL at SMK Negeri Darul Ulum Muncar Banyuwangi

Abstract This research aimed to use Android-based learning media on the basic competency of service company financial statements class X AKL at SMK Negeri Darul Ulum Muncar Banyuwangi. This research applied quasi-experiment research. The participants of this research 34 students of class X AKL 1 and 35 students of class X AKL 2. Class X AKL 1 as an experimental class and X AKL 2 as a control. The data analysis method used in this research was the level of effectiveness using t-test. The results on the large-scale test’s effectiveness level were indicated by the significance level of 0.000 &lt; 0.050, which showed a significant difference from the experimental class to the control class. The effectiveness stated that the Android-based learning media was effective in the basic competency of service company financial statements. The suggestion for further development is that Android-based learning media is used in the basic competency of service company financial statements and can be developed in other basic competencies with the same characteristic.
Design of edupark bukik chinangkiek’s physics e-book with a scientific approach

Abstract Alam takambang jadi guru is a Minangkabau philosophy that suggests to us that everything in nature can be used as a learning resource, one of them is Bukik Chinangkiek. In the preliminary research, it was known that there are many physics concepts in Bukik Chinangkiek edupark, but they have not been packaged into the form of learning materials. Very rapid technological advances have changed the all-digital life, so learning materials must follow the times, namely in digital form or e-book. The aim of this research is to produce a edupark Bukik Chinangkiek’s physics e-book with a scientific approach to the valid. This type of research was a design research using a plomp development model in the prototyping phase. The validity test of the physical edupark e-book used a validity questionnaire to three experts with different expertize. The data analysis technique used a Likert scale with the Aiken’s V formula. The conclusion of this research was that the edupark physics Bukik Chinangkiek’s ebook developed was valid for use as learning material in schools.
Consolidating Pattern-Based Approach for Detecting Sarcasm in Numerical Portions of the Text

Sarcasm is consistently depicted as a circumstance where the customer will in general utilize positive words to pass on a negative opinion via online media. Leaps and bounds in business innovation have added to the tenacious flood and overall choice of web-based media. Which brings out plentiful information in the form of comments and tweets. Due to which it prompts a few inconsistencies in the current methodology of sentiment analysis. It's crucial to analyze the intended sarcasm in user words for effective automatic sentiment analysis of gathered information. We present a first report in distinguishing sarcasm by consolidating both Pattern-based methodology and Numerical sarcasm detection module where the tweets that are delegated non-sarcastic in Pattern-based methodology are fed into the Numerical sarcasm detection module to recognize sarcasm present in numerical bits of the content. At first, to get an understanding into the problem, we execute a rule-based methodology. Be that as it may, the genuine framework set up is the Deep learning (DL) model, which outperform other state-of-the-art methods on dataset of tweets containing numbers. It shows that extraordinary thoughtfulness regarding text containing numbers might be valuable to improve cutting edge in sarcasm detection.