Reviewerly/exHarmony huggingface dataset:
Evaluating the education quality in the College of Fine Arts "Design Department as a model"

Education quality evaluation is one of the objectives of education quality. The evaluation includes assessing the education standards and academic program outcomes to develop intellectual, scientific and practical concepts for the educational structure. It considers the determination of the rates of the number of accepted students and graduates. The research focuses on "what are the levels of education quality according to the evaluation mechanisms in the design department" to enhance the quality system and the objectives of theoretical and applied education.It is identifying the levels of education quality and evaluating it according to the numbers and rates of graduate students of the design department branches for morning and evening studies from 2003 to 2021. This represents the research community and its sample. Using the stratified approach and analyzing data and information in a sequential or orderly manner to reach the most important conclusions:1-The levels of student admission numbers are unbalanced and unstable during the admission years, which affects the level of education and quality2- The graduation rates ranged between 60% and 70%, which was the highest. The average GPA is 80% was almost a fixed percentage relative to the number of students in the three groups, which indicates cognitive support and a practical orientation that came with the standards of quality and learning
Pieturzīmes emuāros

On the internet, people can reach an audience in the way they want, for example, through social media. A blog is a website, often updated daily, in which users write about their thoughts or experiences relating to a subject. Nowadays, blogs have various functions: informational, entertaining, and communicative. Blogs can include texts, photos, videos, and links to other blogs. Writing a blog is subordinate to its audience. Readers ask questions on various issues in the comments. Punctuation marks are used to express emotions and attitudes in the text. The aim is to examine the use of punctuation marks – exclamation marks, question marks, ellipsis, dashes, and double quotation marks – in Latvian authors’ blogs. The empirical base of the study consists of about 500 units of broad syntactic constructions, which have been extracted manually and from the “Latvian Language Blog Corpus”. 27 personal, one woman + man blog, and five cooperative blogs have been examined. Attention is paid to punctuation marks used in blogs and commentaries of blogs in various syntactic constructions, e.g., utterances and parenthesis. The study uses descriptive, comparative, and quantitative research methods. A quantitative analysis of the blog entries shows that expressive use of graphical means is more frequent in personal blogs. Bloggers use emoticons in personal and cooperative blogs.
Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification

Artificial neural network (ANN) is powerful in the artificial intelligence field and has been successfully applied to interpret complex image data in the real world. Since the majority of images are commonly known as private with the information intended to be used by the owner, such as handwritten characters and face, the private constraints form a major obstacle in developing high-precision image classifiers which require access to a large amount of image data belonging to multiple users. State-of-the-art privacy-preserving ANN schemes often use full homomorphic encryption which result in a substantial overhead of computation and data traffic for the data owners, and are restricted to approximation models by low-degree polynomials which lead to a large accuracy loss of the trained model compared to the original ANN model in the plain domain. Consequently, it is still a huge challenge to train an ANN model in the encrypted-domain. To mitigate this problem, we propose a privacy-preserving ANN system for secure constructing image classifiers, named IPPNN, where the server is able to train an ANN-based classifier on the combined image data of all data owners without being able to observe any images using primitives, such as randomization and functional encryption. Our system achieves faster training time and supports lossless training. Moreover, IPPNN removes the need for multiple communications among data owners and servers. We analyze the security of the protocol and perform experiments on a large scale image recognition task. The results show that the IPPNN is feasible to use in practice while achieving high accuracy.
Visualization and Data Analysis of Multi-Factors for the Scientific Research Training of Graduate Students

With the change of graduate education from quantity expansion to quality promotion, how to improve the quality of graduate cultivation has aroused wide concern. However, existing scientific quantitative methods tend to investigate the results of graduate training, with a lack of attention to the multidimensional data during the training process. Thus, exploratory analysis of multidimensional data in the graduate training process and accurate grasp of the key process factors affecting graduate academic competence is an indispensable task for achieving the stated goals of graduate education. In this paper, a visual analytic system of graduate training data is proposed to help users implement in-depth analysis based on the graduate training process. First, a questionnaire is designed about the training process to identify multidimensional data timely and accurately. Then, a series of data mining methods are utilized to further detect key factors in the training process, which will be used to make academic predictions for first-year graduates. Meanwhile, an interactive visual analytic system has been developed to help users understand and analyze the key factors affecting the graduate training process. Based on the results of the visual analysis, effective suggestions will be provided for graduate students, supervisors, and university administrators to improve the quality of graduate education.
Pengaruh Kecerdasan Emosional dengan Kenakalan Siswa

The relationship between emotional intelligence and delinquency is always framed by cause and effect. Many theories of child development explain this relationship. In this context, this study aims to see the relationship between emotional intelligence and delinquency in class XI students at SMK Negeri 1 Nganjuk. This research method is quantitative with a linear correlation approach. A correlation study is a research that involves collecting data to determine the existence of a relationship and the degree of relationship between two or more variables. In accordance with the research findings, the research hypothesis is that there is a significant relationship between emotional intelligence affecting the formation of juvenile delinquency Based on the analysis, the results obtained in this study are: (1) the emotional intelligence aspects in the high category namely 19 respondents as much as 47%. (2) The juvenile delinquency aspects in the low category, namely 20 respondents as much as 50%. (3) Correlation results show a negative relationship between emotional intelligence and juvenile delinquency with the value of the correlation coefficient -325 and p=0,000 &lt; 0,05. This means that the higher the emotional intelligence, the lower the juvenile delinquency.
An empirical study on the complexity, security and maintainability of Ethereum-based decentralized applications (DApps)

The Ethereum blockchain's smart contract is a programmable transaction that performs general-purpose computations and can be executed automatically on the blockchain. Leveraging this component, blockchain technology (BT) has grown beyond the scope of cryptocurrencies and can now be applicable in various industries other than finance. In this paper, we investigated the current trends in Ethereum-based decentralized applications (DApps) to be able to categorize and analyze the DApps to measure the complexity of smart contracts behind them, their level of security and their correlation to the maintainability of the DApps. We leveraged the source code analysis, security analysis, and the developmental metadata of the DApps to infer this correlation. Based on our findings, we concluded that the maintainability of Ethereum DApps is proportional to the code size, number of functions, and, most importantly, the number of outgoing invocations and statements in the smart contracts.
Game of Words for Cognitive Test Results in Class 2 Citizenship Education Learning in Elementary Schools

This researcher in the background of the facts in the field that class II SDN Pejangkungan there are some students have difficulty with intellectual ability. The purpose of this study was to determine the influence of the use of the game of greeting words on the results of cognitive tests in learning civics class II in elementary school. This study uses quantitative experimental research methods with nonequivalent control group design. The results of hypothesis testing using paired sample T - Test t-Test obtained the value of the count of cognitive test results = 11.235&gt; ttable 2.045. So it can be concluded that there is an influence on the media of word games and letter cards to the results of cognitive tests in learning PKN Class II in elementary school.
DIANNA: Deep Insight And Neural Network Analysis

The growing demand from science and industry inspired rapid advances in Artificial Intelligence (AI).The increased use of AI and the reliability and trust required by automated decision making and standards of scientific rigour, led to the boom of eXplainable Artificial Intelligence (XAI).DIANNA (Deep Insight And Neural Network Analysis) is the first Python package of systematically selected XAI methods supporting the Open Neural Networks Exchange (ONNX) format.DIANNA is built by and designed for all research software engineers and researchers, also non-AI experts. Statement of needAI systems have been increasingly used in a wide variety of fields, including such data-sensitive areas as healthcare (Alshehri & Muhammad, 2021), renewable energy (Kuzlu et al., 2020), supply chain (Toorajipour et al., 2021) and finance.Automated decision-making and scientific research standards require reliability and trust of the AI technology (Xu, 2019).Especially in AI-enhanced research, a scientist need to be able to trust a high-performant, but opaque AI model used for automation of their data processing pipeline.In addition, XAI has the potential for helping any scientist to "find new scientific discoveries in the analysis of their data" (Hey et al., 2020).Furthermore, tools for supporting repeatable science are of high demand (Feger, 2020).DIANNA addresses these needs of researchers in various scientific domains who make use of AI models, especially supporting non-AI experts.DIANNA provides a Python-based, user-friendly, and uniform interface to several XAI methods.To the best of our knowledge, it is the only library using Open Neural Network Exchange (ONNX) (Bai et al., 2019), the open-source, framework-agnostic standard for AI models, which supports repeatability of scientific research.
Effects of Using Flashcards to Teach Vocabulary in Public Schools in Sidoarjo, East Java

The purpose of this study is to know the effect of using flashcards for teaching vocabulary of the third graders at a public elementary school in Sidoarjo, East Java, Indonesia. This study used quantitative research with a pre-experimental design by applying pre-test and post-test instruments. Quantitative data were analyzed using the Independent T-test formula test to determine the difference between pre-test and post-test on the students’ achievement using flashcards. In the pre-test value there is an average value of 50 and the post-test value has an average value of 66 while for the t-test value of 7.615 was higher than t-table 2.003 at the level of significant 0.05 with the degree of freedom (df ) 56. The results of this study found that the use of flashcards had an effect on the students’ achievement of the vocabulary mastery.
Geo-Metric

In this work we take a novel perception-centered approach to quantify distortions on 3D geometry of faces, to which humans are particularly sensitive. We generated a dataset, composed of 100 high-quality and demographically-balanced face scans. We then subjected these meshes to distortions that cover relevant use cases in computer graphics, and conducted a large-scale perceptual study to subjectively evaluate them. Our dataset consists of over 84,000 quality comparisons, making it the largest ever psychophysical dataset for geometric distortions. Finally, we demonstrated how our data can be used for applications like metrics, compression, and level-of-detail rendering.
A local probabilistic model of features and segmentation learned by optimizing prediction

One classical criterion for finding good features is that they should be predictable over space and time such that they explain a large part of the input, can be attributed to whole objects, and are distinguishable from noise. Natural images frequently contain object boundaries though, across which the predictions fail. Here, we optimize predictions that can take boundaries into account, such that we can extract where boundaries are in the image from the activations. We formalize the features to be optimized as the activations in a convolutional neural network. We then form a prediction as a product of predictions from neighboring locations. Each neighbor predicts a mixture of a uniform distribution and a Gaussian around its feature vector, corresponding to the cases with a boundary between the neighbor and the predicted location and without a boundary between them. We optimize the features and the prediction such that the predicted probability is higher for the feature vector at the predicted location than for feature vectors from randomly chosen locations (a contrastive predictive coding loss). We used unlabeled natural images from the MS COCO database to learn linear and deeper feature maps.The early linear features converge towards local averages of opponent colors and Gabor-like grating patterns that point to the neighboring locations for which they are predictive. This is superficially consistent with our knowledge about early visual processing. We also evaluate the boundaries extracted from our model on the Berkeley Segmentation Database of human contour annotations. To extract contours from our model we use a computer vision method called globalization. Our contours are reasonable without further adjustment (all models F>= 0.63). Thus, we present a probabilistic model of the feature maps in early visual processing that can take object boundaries into account and can be learned without supervision.
Human-machine shared autonomy approach for non-full-time effective human decisions

In shared autonomy, humans and intelligent robots jointly complete real-time control tasks with their complementary capabilities for improved performance unattainable by either side independently. Many existing methods tend to assume that human decisions are “effective”, i.e., these decisions promote task completion and effectively reflect the true human intention. However, in reality, human decisions can often be “ineffective” to a certain extent due to many reasons, such as fatigue or inattentiveness, which leads to task failure. In this work, we propose a novel deep reinforcement learning-based shared autonomy strategy for human-machine systems, so that the system can complete the correct goal even when human decisions are ineffective for a long period. Specifically, we use deep reinforcement learning to train an end-to-end mapping from system states and human decisions to the value of decisions to explicitly judge whether the human decisions are ineffective. If they are ineffective, the robot takes over the system for better performance. We apply our method to real-time control tasks, and the results show that it can timely and accurately judge the effectiveness of human decisions, allocate control authority, and ultimately improve system performance.
Foreground Bias: Inconsistent Target Effects Reduced When Searching Across Depth

Attentional guidance in scenes is influenced by a multitude of factors, some of which operate jointly and some independently. The semantic context, which relates incoming visual properties with prior knowledge, is among the most influential factors. Recent research has demonstrated a strong foreground bias in scene processing, supported by both eye-tracking data (more fixations to foreground), and visual search (faster and more accurate target detection in foreground). However, it is unclear whether this foreground prioritization is influenced by semantic context. Here, we examined how attention was deployed during search, depending on whether the target was consistent with the foreground or background. For each scene, targets were selected to be semantically consistent with either the foreground or background (e.g., toaster in kitchen, printer in office). Targets became inconsistent when swapped between foreground and background. To account for size differences across depth, the visual angle of large objects in the background was comparable to small objects in foreground. Thus, we implemented a fully crossed factorial design with: Depth (foreground vs. background), Consistency (semantically consistent vs. inconsistent), and Size (small vs. large) as within-subjects factors. Participants searched for these targets and response times (RT) were collected. Results indicated significant main effects of depth and consistency, with faster RT for foreground and semantically consistent targets. However, there was also a 2-way interaction of depth and size, and a 3-way interaction. Further analyses of the 3-way interaction revealed faster RT for consistent targets only for small foreground and large background targets. To further control for size, only targets of comparable visual angle were included in a subsequent analysis. Here, the effect of semantic consistency was significantly smaller in the foreground than background region. We conclude the Foreground Bias modulates the effects of semantics by decreasing its impact in space near the viewer.
<i>Onception</i>: Active Learning with Expert Advice for Real World Machine Translation

Abstract Active learning can play an important role in low-resource settings (i.e., where annotated data is scarce), by selecting which instances may be more worthy to annotate. Most active learning approaches for Machine Translation assume the existence of a pool of sentences in a source language, and rely on human annotators to provide translations or post-edits, which can still be costly. In this article, we apply active learning to a real-world human-in-the-loop scenario in which we assume that: (1) the source sentences may not be readily available, but instead arrive in a stream; (2) the automatic translations receive feedback in the form of a rating, instead of a correct/edited translation, since the human-in-the-loop might be a user looking for a translation, but not be able to provide one. To tackle the challenge of deciding whether each incoming pair source–translations is worthy to query for human feedback, we resort to a number of stream-based active learning query strategies. Moreover, because we do not know in advance which query strategy will be the most adequate for a certain language pair and set of Machine Translation models, we propose to dynamically combine multiple strategies using prediction with expert advice. Our experiments on different language pairs and feedback settings show that using active learning allows us to converge on the best Machine Translation systems with fewer human interactions. Furthermore, combining multiple strategies using prediction with expert advice outperforms several individual active learning strategies with even fewer interactions, particularly in partial feedback settings.
Residual learning-based two-stream network for RGB-T object tracking

RGB-T object tracking is a branch of visual tracking that has been widely applied to many fields, such as intelligent transportation and urban monitoring. Due to the interference of background clutter and occlusion, the existing trackers still suffer from the problems of unreasonable modal fusion strategy, insufficient feature extraction, and loss of semantic information. To solve these problems, we propose a residual learning-based two stream network for RGB-T object tracking. The overall feature extraction network is composed of three branches, and multi-layer convolutions are utilized to extract the features of visible, thermal, and fused images, respectively. First, aiming at improving the effectiveness of feature extraction, a weight generation module for hierarchical feature weight calculation is designed to guide the direction of feature fusion. Then, the residual block is employed to replace the single-layer convolution in order to increase the depth of the network, by which deeper semantic features are learned and the loss of semantic information is alleviated. Finally, a loss function with a penalty term is developed to adjust our network toward the direction of the best tracking performance of the dual modalities. This overcomes the negative impact of poor mode on model training. Experiments implemented on public RGB-T datasets indicate that our algorithm outperforms the recent state-of-the-art trackers in terms of both precision rate and success rate. Compared with the second-best comparison algorithm, our tracker improves the above two metrics by 0.4% and 1.5%, respectively. Our codes are available at https://github.com/MinjieWan/Residual-learning-based-two-stream-network-for-RGB-T-object-tracking.
Interactive nonlocal joint learning network for red, green, blue plus depth salient object detection

Research into red, green, blue plus depth salient object detection (SOD) has identified the challenging problem of how to exploit raw depth features and fuse cross-modal (CM) information. To solve this problem, we propose an interactive nonlocal joint learning (INL-JL) network for quality RGB-D SOD. INL-JL benefits from three key components. First, we carry out joint learning to extract common features from RGB and depth images. Second, we adopt simple yet effective CM fusion blocks in lower levels while leveraging the proposed INL blocks in higher levels, aiming to purify the depth features and to make CM fusion more efficient. Third, we utilize a dense multiscale transfer strategy to infer saliency maps. INL-JL advances the state-of-the-art methods on five public datasets, demonstrating its power to promote the quality of RGB-D SOD.