Reviewerly/exHarmony huggingface dataset:
The Effect of Learning Strategy and InterpersonalCommunication on the Students AchievementReading Comprehension English Language at SMPNegeri 1 Selesai Kabupaten Langkat Tahun Ajaran2018 / 2019

This study aims (1) to find out the results oflearning to read comprehension in English students with Geniuslearning strategies higher than those taught by Expositorylearning strategies, (2) To determine reading comprehensionskills in English students who have high interpersonalcommunication and interpersonal communication low, (3) todetermine the interaction between learning strategies andinterpersonal communication towards reading studentsunderstanding of English. The population in this study was 9(nine) classes in SMP Negeri 1 Selesai. The research sampleswere taken 2 classes, namely class VIII-1 and VIII-3. The resultsof this study are: (1) The results of learning to read studentsunderstanding of English taught with Genius Learning strategiesare higher than students taught by Expository learningstrategies. This can be seen from the data analysis that obtainedwhere the value of Fcount= 13.08, while the value of Ftable = 3.99so that Fcount = 13.08 > Ftable= 3.99. (2) The ability to readEnglish language comprehension of students who have highinterpersonal communication is higher than those who have lowinterpersonal communication. This can be seen from the dataanalysis that obtained where the value of Fcount= 13.35 whileFtable= 3.99, so that Fcount= 13.35 > Ftable= 3.99. (3) There isan interaction between learning strategies and interpersonalcommunication in influencing the results of learning to readstudents understanding of English. This can be seen from theanalysis of the data obtained where the value of Fcount= 28.19while Ftable= 3.99 so that Fcount= 28.19 > Ftable= 3.99.Summary this research found interaction between learningstrategy and the interpersonal communication on the studentsachievement in English language, for teacher must taughtinstructional strategy find with students characteristic imployingthe interpersonal communication for students achievement.
PENINGKATAN PRESTASI BELAJAR MATEMATIKA SISWA DENGAN MODEL PEMBELAJARAN KOOPERATIF TIPE TEAM INVESTIGATION SMP NEGERI 2 CEPER KLATEN

The purpose of this class action research is to (1) improve students' mathematics learning achievement through the Group Investigation learning model in class VII F SMPN 2 Ceper, Ceper District, Klaten Regency 2017/2018 Academic Year. (2) Describe how to overcome the constraints of applying the Group Investigation learning model to improve mathematics learning achievement of class VII F SMPN 2 Ceper. Based on the results of the study it can be concluded: (1) The application of the Group Investigation learning model can improve the learning achievement of grade VII F SMPN 2 Ceper, which is indicated by: students of class VII F as many as 32 students have increased learning achievement that is before action only 53.1% of students study completely, after the action becomes 100%. How to overcome constraints, namely the teacher must be skilled in applying Group Investigation learning model is: a) reviewing the concepts and basic competencies that students will learn, b) forming heterogeneous study groups both in terms of ability, gender, religion, tribes etc., c) prepare everything about learning as well as possible so that when learning runs smoothly, d) minimize the number of group members so that learning takes place effectively with no passive group members or playing alone during group discussions, e) giving gifts at the best group presentations
Bandleaders in the Idea Business: In Conversation with Lou Rosenfeld

An interview with Lou Rosenfeld, co-author of Information Architecture for the Web and Beyond, originally Information Architecture for the World Wide Web, where he lays out his part in the history of information architecture as a community of practice, the emergence of interaction design and user experience from that budding community, dynamics of professional organizations, and the importance of and need for information architecture within businesses and organizations.
A Study on Foreign Worker Acceptance Policy of Korea:Focusing on Shipbuilding

This paper considers whether employment opportunities for Korean workers decreased due to the intake of foreign workers after the downturn in the shipbuilding industry starting in 2015. The paper clarifies the following points.
Enabling Synergy: Improving the Information Infrastructure for Planetary Science

In this whitepaper we advocate that the Planetary Science (PS) community build a discipline-specific digital library, in collaboration with the existing astronomy digital library, ADS. We suggest that the PS data archives increase their level of curation to allow for direct linking between the archival data and the derived journal articles. And we suggest that a new component of the PS information infrastructure be created to collate and curate information on features and objects in our solar system, beginning with the USGS/IAU Gazetteer of Planetary Nomenclature.
Noise-Aware Saliency Prediction for Videos with Incomplete Gaze Data.

Deep-learning-based algorithms have led to impressive results in visual-saliency prediction, but the impact of noise in training gaze data has been largely overlooked. This issue is especially relevant for videos, where the gaze data tends to be incomplete, and thus noisier, compared to images. Therefore, we propose a noise-aware training (NAT) paradigm for visual-saliency prediction that quantifies the uncertainty arising from gaze data incompleteness and inaccuracy, and accounts for it in training. We demonstrate the advantage of NAT independently of the adopted model architecture, loss function, or training dataset. Given its robustness to the noise in incomplete training datasets, NAT ushers in the possibility of designing gaze datasets with fewer human subjects. We also introduce the first dataset that offers a video-game context for video-saliency research, with rich temporal semantics, and multiple gaze attractors per frame.
Sentiment Analysis of Online Lectures in Indonesia from Twitter Dataset Using InSet Lexicon

The implementation of lectures on various campuses in Indonesia has been emphasized since the outbreak of coronavirus. Online lectures are used as a solutionto continue teaching and learning activities during pandemic. But the implementation of lectures raises a variety of opinions in the community, especially among lecturers. It also raises the attitude of the pros and cons fromvarious parties. For this purpose, data mining from Twitter analyzes sentiment on the topic of online lectures. The data is classified into 3 classes, i.e. positive, negative, and neutral. This research was conducted with a lexicon-based approach technique using InSet Lexicon as an Indonesian opinion dictionary. The determination of the sentiment class for each sentence is obtained from the result of the polarity score calculation. Classification results from 5811 tweet data were found to contain 63.4% negative tweets, 27.6% positive tweets, and 8.9% neutral tweets. Testing of classification results was done by cross-validation method and confusion matrix witha comparison of training data and test data is 8:2gave accuracy value of 79.2%, precision by 72.9%, recall by 62.8%, and f-measure of 67.4%.
XATR with occlusion and super-resolution

Standard ATR algorithms suffer from a lack of transparency into why the algorithm recognized a particular object as a target. We present an enhanced Explainable ATR algorithm that utilizes super-resolution networks to provide increased robustness. XATR is a two-level network, with the lower level using Region-based Convolution Neural Networks (R-CNNs) to recognize major parts of the target, known as vocabulary. The upper level employs Markov Logic Networks (MLN) and structure learning to learn the geometric and spatial relationships between the parts in the vocabulary that best describe the objects. Image degradation due to noise, blurring, decimation, etc., can severely impact XATR performance as feature content is irrevocably lost. We address this by introducing a novel super-resolution network. This network uses a dynamic u-net design. A ResNet is on the encoder path while the imagery is reconstructed with dynamically linked upsampling heads in the decoder path. The network is trained on high resolution and degraded imagery pairs to super-resolve the degraded imagery. The trained dynamic u-net then super-resolves unseen degraded imagery to improve XATR’s performance compared to lost performance when using the degraded imagery. In this paper, we perform experiments to 1) Determine the sensitivity of XATR to image corruption 2) Improve XATR performance with super-resolution and 3) Demonstrate XATR robustness to image degradation and occlusion. Our experiments demonstrate improved recall (+40%) and accuracy (+20%) on degraded images when super-resolution is applied.
Penggunaan Media Buku Pop Up Berbasis Budaya Untuk Meningkatkan Konsentrasi Belajar Siswa Pada Pembelejaran Daring (PTK Kelas 4 MIN 1 Kepahiang)

This research aims to increase the student learning concentration of students in grade 4 to MIN 1 Kepahiang by applying media learning pop up book. This research is a classroom action research. The subjects of this study were 11 students consisting of 4 male students and 7 female students. The research procedure is in the form of a cycle. Each cycle consists of four stages including planning, implementing actions, observing and reflecting. The research instrument was in the form of observation sheet media learning pop up book and student learning concentration on online learning. The data analysis technique used is the observation sheet using the formula of the average score, the highest score, the lowest score, the difference in score and the range of values for each criterion. Thus the use of media learning pop up book can increase the learning concentration of grade 4 students of MIN 1 Kepahiang. Keywords: Media Learning Pop Up Book, Concentration.
한국인의 중국어 작문 속 어색한 문장 고찰

The purpose of this paper is to analyze the type and frequency of awkward sentences in Chinese written text by Koreans. An awkward sentence is a sentence that has no formal errors, such as vocabulary or usage, but is considered inappropriate or unnatural in the context. Awkward sentence types can be divided into semantic level, text level, and context level. Awkward sentences at the semantic level include inadequate information presentation, indefinite instructions, and improper use of conjunctions. Awkward sentences at the text level include inconsistent themes, logical contradictions, and breakthrough content development. Awkward sentences at the context level include using inappropriate expressions in context.As a result, awkward sentences with the highest frequency of Korean writing in Chinese occurred at the text level, and most of them were due to the leaf of contents. An awkward sentence caused by a leap of content is usually a result of the author’s logic or way of thinking not following any evidence or steps. The next most frequent occurrences are awkward sentences that result from inappropriate use of conjunctions, logical contradictions, or inconsistencies in the topic.
Optimization of Entropy Encoding Using Random Variations

This article describes an optimization method concerning entropy encoding applicable to a source of independent and identically-distributed random variables. The algorithm can be explained with the following example: let us take a source of i.i.d. random variables X with uniform probability density and cardinality 10. With this source, we generate messages of length 1000 which will be encoded in base 10. We call XG the set containing all messages that can be generated from the source. According to Shannon's first theorem, if the average entropy of X, calculated on the set XG, is H(X)≈0.9980, the average length of the encoded messages will be 1000*H(X)=998. Now, we increase the length of the message by one and calculate the average entropy concerning the 10% of the sequences of length 1001 having less entropy. We call this set XG10. The average entropy of X10, calculated on the XG10 set, is H(X10)≈0.9964, consequently, the average length of the encoded messages will be 1001*H(X10)=997.4 . Now, we make the difference between the average length of the encoded sequences belonging to the two sets (XG and XG10) 998.0-997.4 = 0.6. Therefore, if we use the XG10 set, we reduce the average length of the encoded message by 0.6 values in base ten. Consequently, the average information per symbol becomes 997.4/1000=0.9974, which turns out to be less than the average entropy of X H(X)≈0.998. We can use the XG10 set instead of the X10 set, because we can create a biunivocal correspondence between all the possible sequences generated by our source and ten percent of the sequences with less entropy of the messages having length 1001. In this article, we will show that this transformation can be performed by applying random variations on the sequences generated by the source.
Pseudo-Label Guided Unsupervised Domain Adaptation of Contextual Embeddings

Contextual embedding models such as BERT can be easily fine-tuned on labeled samples to create a state-of-the-art model for many downstream tasks. However, the fine-tuned BERT model suffers considerably from unlabeled data when applied to a different domain. In unsupervised domain adaptation, we aim to train a model that works well on a target domain when provided with labeled source samples and unlabeled target samples. In this paper, we propose a pseudo-label guided method for unsupervised domain adaptation. Two models are fine-tuned on labeled source samples as pseudo labeling models. To learn representations for the target domain, one of those models is adapted by masked language modeling from the target domain. Then those models are used to assign pseudo-labels to target samples. We train the final model with those samples. We evaluate our method on named entity segmentation and sentiment analysis tasks. These experiments show that our approach outperforms baseline methods.
CBRNE sensors: intelligent systems

“Intelligent Systems”, machines that respond to the world around them and machines that interact with humans to change the dynamic of physical or social interaction. In the chemical, biological, radiological, nuclear, and explosives (CBRNE) detection world the focus is currently on the former definition with the advent of what some call “smart systems” based upon the common goal of creating CBRNE sensors that can respond and adapt to the environment in which they operate. Responses can be as simple as tipping and cuing of additional assets or resources to address changes in environment or operating conditions. Or on deeper level, the control systems and algorithms that operate/control these systems autonomously adapting to changes in both the operational characteristics and current conditions. Ideally a system could self-monitor its inherent capabilities and, for example, adjust dwell or sampling times base upon learned or defined characteristics. The concept of self-learning or machine learning within a sensor aligns with the current popularism of artificial intelligence (AI). However, within the CBRNE sensor community there is an inherent lack of the depth and breadth of data to actualize a functional AI to address these problems. In reality the information or data could be quite limited and the need to be able to operate anywhere in the world without long periods of acclimation must be stressed. Therefore CBRNE Intelligent Systems must be able to operate in a traditional sense, turn it on and function, and be adaptable to “long term” operations adjusting to both environmental and operational characteristic changes.
PAUD Teachers' Speaking Skill in Teaching English in Tampan District

The aim of this research is to describe about PAUD teachers’ speaking skill in Tampan district. The method of this research was survey design, there was no treatment and data manipulating on that research variable, independent variable, its only describe the result of PAUD teachers’ speaking skill in teaching English in Tampan district Pekanbaru. This research data was collected by using a test, speaking. Scientifically, the result of PAUD teachers’ speaking skill has been known. Their speaking skill in teaching English was low. It was 33.5 on poor level. The result showed that the teachers should improve their skill in teaching English. Besides, this research result is expected to be as one of references in deciding the method, media, or resources to be applied in teaching English for early childhood students. In conclusion, PAUD teachers in Tampan district seldom use or practice their English whether with their colleague or students.
On quantum neural networks

The early definition of a quantum neural network as a new field that combines the classical neurocomputing with quantum computing was rather vague and satisfactory in the 2000s. The widespread in 2020 modern definition of a quantum neural network as a model or machine learning algorithm that combines the functions of quantum computing with artificial neural networks deprives quantum neural networks of their fundamental importance. We argue that the concept of a quantum neural network should be defined in terms of its most general function as a tool for representing the amplitude of an arbitrary quantum process. Our reasoning is based on the use of the Feynman path integral formulation in quantum mechanics. This approach has been used in many works to investigate the main problem of quantum cosmology, such as the origin of the Universe. In fact, the question of whether our Universe is a quantum computer was posed by Seth Lloyd, who gave the answer is yes, but we argue that the universe can be thought of as a quantum neural network.
Research Guides: Information Literacy Toolkit: Videos & Tutorials

Resources to help with planning or implementing information literacy concepts and assignments in classes.
She Persists: In Conversation with Abby Covert

Interview with Abby Covert, author of How to Make Sense of Any Mess, on writing her book while teaching information architecture to design students, the prospects of those currently practicing information architecture, a maturity model for the field, and how to critique masterworks.
Advanced Study on Stakeholder Approach, Stakeholders Mental Model: A Visualization Test with Cognitive Mapping Technique

With regard to the stakeholder strategy of corporate governance, the idea of this paper is to evaluate the mental models of actors in the business. The use of the cognitive map to interpret these diagrams in order to explain the stakeholder approach's ways of thought and conceptualization. The paper takes a view of corporate governance and explores the stakeholder paradigm. A cognitive mapping method is also required.
Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights

The growing quantity and complexity of data pose challenges for humans to consume information and respond in a timely manner. For businesses in domains with rapidly changing rules and regulations, failure to identify changes can be costly. In contrast to expert analysis or the development of domain-specific ontology and taxonomies, we use a task-based approach for fulfilling specific information needs within a new domain. Specifically, we propose to extract task-based information from incoming instance data. A pipeline constructed of state of the art NLP technologies, including a bi-LSTM-CRF model for entity extraction, attention-based deep Semantic Role Labeling, and an automated verb-based relationship extractor, is used to automatically extract an instance level semantic structure. Each instance is then combined with a larger, domain-specific knowledge graph to produce new and timely insights. Preliminary results, validated manually, show the methodology to be effective for extracting specific information to complete end use-cases.
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes

We introduce RaidaR, a rich annotated image dataset of rainy street scenes, to support autonomous driving research. The new dataset contains the largest number of rainy images (58,542) to date, 5,000 of which provide semantic segmentations and 3,658 provide object instance segmentations. The RaidaR images cover a wide range of realistic rain-induced artifacts, including fog, droplets, and road reflections, which can effectively augment existing street scene datasets to improve data-driven machine perception during rainy weather. To facilitate efficient annotation of a large volume of images, we develop a semi-automatic scheme combining manual segmentation and an automated processing akin to cross validation, resulting in 10-20 fold reduction on annotation time. We demonstrate the utility of our new dataset by showing how data augmentation with RaidaR can elevate the accuracy of existing segmentation algorithms. We also present a novel unpaired image-to-image translation algorithm for adding/removing rain artifacts, which directly benefits from RaidaR.
Audio-Driven Emotional Video Portraits

Despite previous success in generating audio-driven talking heads, most of the previous studies focus on the correlation between speech content and the mouth shape. Facial emotion, which is one of the most important features on natural human faces, is always neglected in their methods. In this work, we present Emotional Video Portraits (EVP), a system for synthesizing high-quality video portraits with vivid emotional dynamics driven by audios. Specifically, we propose the Cross-Reconstructed Emotion Disentanglement technique to decompose speech into two decoupled spaces, i.e., a duration-independent emotion space and a duration dependent content space. With the disentangled features, dynamic 2D emotional facial landmarks can be deduced. Then we propose the Target-Adaptive Face Synthesis technique to generate the final high-quality video portraits, by bridging the gap between the deduced landmarks and the natural head poses of target videos. Extensive experiments demonstrate the effectiveness of our method both qualitatively and quantitatively.
Author Correction: Deep learning for high-resolution and high-sensitivity interferometric phase contrast imaging

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Image translation to enhance IR2VIS image registration

Infrared to Visible (IR2VIS) image registration suffers from the challenge of cross-modal feature extraction and matching. Conventional methods usually design the same keypoint detector for both Infrared (IR) and Visible (VIS) images. The VIS images are even converted to gray-scale images before the keypoint detection. IR and VIS gray-scale images have different properties which might not be applicable for the same feature detector. Therefore, this paper proposes an IR2VIS image registration method, namely, Image Translation for Image Enhanced Registration (ITIER). The IR images are first translated to realistic VIS images by Wavelet-Guided Generative Adversarial Network (WGGAN) for the convenience of cross-modal feature detection. Then the keypoint detection and matching and the homography transformation, which have been integrated into our ITIER, are conducted on the translated and original VIS images. Experimental results demonstrate that the IR2VIS image registration accuracy is greatly enhanced by the image-to-image translation procedure, which transfers IR images to realistic VIS images.