Reviewerly/exHarmony huggingface dataset:
HAB (High Altitude Balloon) Satellite as A Transponder for Lander & Rover

This paperproposes a faster and effective method for the Rover to communicate between the Lander or the Satellite on different Celestial bodies with the use of High-Altitude Balloon (HAB) Satellite. There are no Technical possibilities of running a Global Navigation Satellite System (GNSS) over there. Establishing and maintaining a Telemetry link between the Rover and the Base Station on Earth is the crucial part in any and every Expedition mission. The destination of interest may be located outside the pre-determined communication range. In these cases, the rover is needed to go farther from what was decided before. By doing so, the communication link may get weakened, or worse; Suspended. HAB Satellite acts as a Transponder/ Relay-Antenna between them, thus establishing the connection. This project proposes a method to overcome data loss and effective communication.
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness

Message Passing Neural Networks (MPNNs) are a common type of Graph Neural Network (GNN), in which each node's representation is computed recursively by aggregating representations (messages) from its immediate neighbors akin to a star-shaped pattern. MPNNs are appealing for being efficient and scalable, how-ever their expressiveness is upper-bounded by the 1st-order Weisfeiler-Lehman isomorphism test (1-WL). In response, prior works propose highly expressive models at the cost of scalability and sometimes generalization performance. Our work stands between these two regimes: we introduce a general framework to uplift any MPNN to be more expressive, with limited scalability overhead and greatly improved practical performance. We achieve this by extending local aggregation in MPNNs from star patterns to general subgraph patterns (e.g.,k-egonets):in our framework, each node representation is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only (i.e. a star). We choose the subgraph encoder to be a GNN (mainly MPNNs, considering scalability) to design a general framework that serves as a wrapper to up-lift any GNN. We call our proposed method GNN-AK(GNN As Kernel), as the framework resembles a convolutional neural network by replacing the kernel with GNNs. Theoretically, we show that our framework is strictly more powerful than 1&2-WL, and is not less powerful than 3-WL. We also design subgraph sampling strategies which greatly reduce memory footprint and improve speed while maintaining performance. Our method sets new state-of-the-art performance by large margins for several well-known graph ML tasks; specifically, 0.08 MAE on ZINC,74.79% and 86.887% accuracy on CIFAR10 and PATTERN respectively.
Analysis of the Current High School 〈Korean History〉Textbooks Focusing on the History of Independence Movements in Korea

This paper analyzes descriptions of the history of the independence movement among high school 〈Korean History〉 textbooks under the 2015 revised curriculum. Until now, most of the analysis of textbook descriptions has been about whether facts are wrong or reflected in the latest results. Although the authors describe the textbook according to the curriculum, they reveal their perspectives everywhere. It is reflected not only in the textual description but also in the exploratory activities. There is a limit to finding the authors’ intentions by analyzing what is ostensibly revealed. Therefore, this writer conducted a written interview with the authors who described the history of the independence movement. The interview was divided into a textual description and curriculum. Through this, the intentions of textbook descriptions and exploration activities were more accurately identified. In particular, this writer focused on how the authors tried to escape the “dichotomy discourse of exploitation and resistance,” which was the biggest problem in the narrative of independence movement history. Each author of the textbook had a slightly different opinion on this, and accordingly, the description of the textbook was different. Through this paper, the present writer attempted to analyze the author’s intentions involved in the description of the textbook and seek alternatives to the description of the history of the independence movement in the future.
Modernization in Learning Practices &amp; Also Innovative Teaching

There is no doubt that online learning and the development of online courses is proliferating.The convenience of working online has proven to be very attractive to students and instructors alike.Despite its popularity, online learning is not without its challenges.As instructors are finding their way in the online environment and paying more attention to good course design and delivery, they are discovering that traditional forms of assessment of student work such as tests and quizzes that served them well in the face to face classroom may not work quite as well online.Milam, Voorhees, and Bedard -Voorhees ( 2004) note, "The online paradigm holds that learning itself may be different in the online environment and, if that is true, then the methodology for measuring it should also be different or should measure those things that are, in fact, different" effective assessment has come increased concern about academic honesty, raising questions like these:• How do I, as the instructor, know that students really understand and can apply the material I' m teaching if I can' t sees them?• How can I ensure that students won' t cheat on exams or other assessments in my online course?In these fields, assessment criteria cannot be too precise or rigid, as building arguments that provide new insights is the benchmark of good performance.Jones, Voorhees, and Paulson (2002) define competencies as "the combination of skills, abilities, and knowledge needed to perform a specific task".Competencies support the dynamic link between knowing and expressing.Once competencies are established, the next level of focus should be the development of course outcomes.Course outcomes are important in that they help students learn more effectively and make it clear what students can expect to gain from taking the course.Collaborative effort helps learners achieve a deeper level of knowledge generation while moving from independence to interdependence, thus strengthening the foundation of an online learning community, which we believe is the vehicle through which the course should be delivered.By learning together in a learning community, students have the opportunity to extend and deepen their learning experience, test out new ideas by sharing them with a supportive group, and receive critical and constructive feedback.The Teacher Accurately Demonstrates Knowledge of the Content Area and Approved Curriculum.
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain

Recently, the generalization behavior of Convolutional Neural Networks (CNN) is gradually transparent through explanation techniques with the frequency components decomposition. However, the importance of the phase spectrum of the image for a robust vision system is still ignored. In this paper, we notice that the CNN tends to converge at the local optimum which is closely related to the high-frequency components of the training images, while the amplitude spectrum is easily disturbed such as noises or common corruptions. In contrast, more empirical studies found that humans rely on more phase components to achieve robust recognition. This observation leads to more explanations of the CNN's generalization behaviors in both robustness to common perturbations and out-of-distribution detection, and motivates a new perspective on data augmentation designed by re-combing the phase spectrum of the current image and the amplitude spectrum of the distracter image. That is, the generated samples force the CNN to pay more attention to the structured information from phase components and keep robust to the variation of the amplitude. Experiments on several image datasets indicate that the proposed method achieves state-of-the-art performances on multiple generalizations and calibration tasks, including adaptability for common corruptions and surface variations, out-of-distribution detection, and adversarial attack.
E-Commerce Fraud: Effects of Online Selling

Internet technology has been adopted for multiple applications like e-government, e-learning, and e-commerce. It founds out that e-commerce provides advantages. For instance, flexibility and efficiency are some which can be achieved by e-commerce. However, this has disadvantages such as frauds that remains a serious threat globally. E-commerce or online selling is not only limited to the selling and purchasing of a physical product online but it also includes the sale and purchase of non-physical goods, such as services and digital products. It is during this transaction that the seller and customer experienced being victimized. The goal of this study is to determine the effects of online selling experiences to the victims either the customer or seller. The researchers have found that the effect of online selling experiences to the victim, aside from the stress that they suffer, is categorized into two: the financial and social impact. The financial impact refers to the money that is lost as a result of bad online selling experience. Conversely, the social impact talks about the behavior of the buyer that affects to buy products online. Through these bad experiences, the consumer becomes more vigilant as they thoroughly check the product first, while taking into consideration other consumers’ feedback before doing actual shopping.
A Review on Part-of-Speech Technologies

Developing an automatic part-of-speech (POS) tagging for any new language is considered a necessary step for further computational linguistics methodology beyond tagging, like chunking and parsing, to be fully applied to the language. Many POS disambiguation technologies have been developed for this type of research and there are factors that influence the choice of choosing one. This could be either corpus-based or non-corpus-based. In this paper, we present a review of POS tagging technologies.
I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selection

Most learners fail to develop deep text comprehension when reading textbooks passively. Posing questions about what learners have read is a well-established way of fostering their text comprehension. However, many textbooks lack self-assessment questions because authoring them is timeconsuming and expensive. Automatic question generators may alleviate this scarcity by generating sound pedagogical questions. However, generating questions automatically poses linguistic and pedagogical challenges. What should we ask? And, how do we phrase the question automatically? We address those challenges with an automatic question generator grounded in learning theory. The paper introduces a novel pedagogically meaningful content selection mechanism to find question-worthy sentences and answers in arbitrary textbook contents. We conducted an empirical evaluation study with educational experts, annotating 150 generated questions in six different domains. Results indicate a high linguistic quality of the generated questions. Furthermore, the evaluation results imply that the majority of the generated questions inquire central information related to the given text and may foster text comprehension in specific learning scenarios.
Decoupled IoU Regression for Object Detection

Non-maximum suppression (NMS) is widely used in object detection pipelines for removing duplicated bounding boxes. The inconsistency between the confidence for NMS and the real localization confidence seriously affects detection performance. Prior works propose to predict Intersection-over-Union (IoU) between bounding boxes and corresponding ground-truths to improve NMS, while accurately predicting IoU is still a challenging problem. We argue that the complex definition of IoU and feature misalignment make it difficult to predict IoU accurately. In this paper, we propose a novel Decoupled IoU Regression (DIR) model to handle these problems. The proposed DIR decouples the traditional localization confidence metric IoU into two new metrics, Purity and Integrity. Purity reflects the proportion of the object area in the detected bounding box, and Integrity refers to the completeness of the detected object area. Separately predicting Purity and Integrity can divide the complex mapping between the bounding box and its IoU into two clearer mappings and model them independently. In addition, a simple but effective feature realignment approach is also introduced to make the IoU regressor work in a hindsight manner, which can make the target mapping more stable. The proposed DIR can be conveniently integrated with existing two-stage detectors and significantly improve their performance. Through a simple implementation of DIR with HTC, we obtain 51.3% AP on MS COCO benchmark, which outperforms previous methods and achieves state-of-the-art.
Geometric Probing of Word Vectors

This paper studies the informativeness of linguistic properties such as part-of-speech and named entities encoded in word representations.First, we find directions that correspond to these properties using the method of Elazar et al. (2020).Then such directions are compared with the principal vectors obtained from application of PCA to word embeddings.As a result, we find that the part-of-speech information is more important for word embeddings than the named entity property.
Third Millennium Life Saving Smart Cyberspace Driven by AI & Robotics

The Journal issue discusses the current and future dynamic trends in research, innovation and developments of cutting-edge technologies, Humanoid Robotics, AI, and smart cyber systems that may contribute effectively to people saving lives, and decision makers in the nation and worldwide.
TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural Networks

Deep neural networks (DNNs) are now the de facto choice for computer vision tasks such as image classification. However, their complexity and "black box" nature often renders the systems they're deployed in vulnerable to a range of security threats. Successfully identifying such threats, especially in safety-critical real-world applications is thus of utmost importance, but still very much an open problem. We present TESDA, a low-overhead, flexible, and statistically grounded method for {online detection} of attacks by exploiting the discrepancies they cause in the distributions of intermediate layer features of DNNs. Unlike most prior work, we require neither dedicated hardware to run in real-time, nor the presence of a Trojan trigger to detect discrepancies in behavior. We empirically establish our method's usefulness and practicality across multiple architectures, datasets and diverse attacks, consistently achieving detection coverages of above 95% with operation count overheads as low as 1-2%.
Study on Design and Implementation of Distributed Multiple Camera Surveillance and Tracking System

This work proposes a system to successfully track, identify and tag target objects or individuals in a real time environment. Consider a sequence of cameras installed in an environment or a facility. Through these cameras user can track those intruders who are aware of how the surveillance system operates and are actively trying to avoid getting seen by the surveillance system. This system tracks the movement of any person’s movement and record and maintain that data throughout any facility in which such a solution is deployed. Movement logging of any individual person can also be done if it does not infringe his/her privacy. Design and analysis a distributed algorithm for the optimization of the recognition and mapping of the given subject/subjects on a User Interface. Finally, the performance and robustness of the distributed system is further analyzed through continuously training the algorithm or maybe real time demo.
Conditions for the formation of future primary teachers' self-expression culture in the educational process of the university

The article deals with the problem of the formation students' self-expression culture who are preparing to be primary school teachers. The author's view is presented on the conditions created in the educational process of the university, in which the future young specialists' self-expression culture acquires the characteristics necessary for successful professional activity. Such conditions include the development of basic educational programs of professional training, which are based on the ideas of developing a the future students' self-expression culture PEO, the introduction of subjects that characterize the self-expression culture of the teacher, peculiarities of its formation; organization of students' activities in the academic and extracurricular time with involvement in situations where they can express themselves according to their personal preferences and the requirements of their professional activities; methodological support of vocational training, containing materials that reveal questions about the teachers' self-expression culture and its formation. It is emphasized that the success of the students' education, the formation of their characteristics of the self-expression culture depends on what features of the self-expression culture are demonstrated by the primary teacher to them.
SIMD-Optimized Search Over Sorted Data

Abstract Applications often require a fast, single-threaded search algorithm over sorted data, typical in table-lookup operations. We explore various search algorithms for a large number of search candidates over a relatively small array of logarithmically-distributed sorted data. These include an innovative hash-based search that takes advantage of floating point representation to bin data by the exponent. Algorithms that can be optimized to take advantage of SIMD vector instructions are of particular interest. We then conduct a case study applying our results and analyzing algorithmic performance with the EOSPAC package. EOSPAC is a table look-up library for manipulation and interpolation of SESAME equation-of-state data. Our investigation results in a couple of algorithms with better performance with a best case eight times speedup over the original EOSPAC Hunt-and-Locate implementation. Our techniques should generalize to other instances of search algorithms seeking to get a performance boost from vectorization.
유산균 발효유가 복합레진 표면에 미치는 영향

Objectives: The present study was carried out to identify the effects of commercially available fermented milk on the surfaces of composite resins.Methods: The experiment included 6 groups: Fermented milk (Group 3), fermented milk supplemented with 3% calcium (Group 4), specimens coated with fluoride and then treated with fermented milk (Group 5), specimens coated with fluoride and then treated with fermented milk supplemented with calcium (Group 6), mineral water (Group 1), and carbonated beverage (Group 2). The prepared specimens were immersed in the experimental solution to analyze the effects of the test fluid on the surfaces of the composite resin specimens. The degree of microhardness on each specimen surface was subsequently measured. The results were obtained using scanning electron microscopy.Results: The pH values of the experimental solutions in an increasing order were: Group 2 (2.34 ±0.08), Group 3 and Group 5 (3.45±0.03), Group 4 and Group 6 (4.04±0.06), and Group 1 (7.72 ±0.17). Significant differences in the Vickers hardness number (△VHN) were found between the groups when measured before and after immersion in the experimental solution in the following order: -11.48±1.45 (Group 2), -9.54±1.15 (Group 3), -9.21±1.21 (Group 4), -8.14±0.84 (Group 6), -8.10±0.92 (Group 5), and -1.71±0.57 (Group 1) (P<0.05). Scanning electron microscopy findings of the composite resin surfaces in Groups 5 and 6 revealed smooth surfaces similar to those in Group 1 (negative control). In contrast, Groups 3 and 4 showed rough surfaces with severe cracks between the crystals, similar to those of Group 2 (positive control).Conclusions: The intake of fermented milk may attenuate the surface microhardness of composite resins. To slow down the reduction in microhardness following fermented milk consumption, coating the composite resin surfaces with fluoride and adding calcium supplement to the fermented milk could be considered.
PENERAPAN MODEL BERMAIN BERBASIS KONTEKSTUAL UNTUK MENINGKATKAN MINAT SAINS SISWA SEKOLAH DASAR

The research on the applicability of game-based Contextual Teaching and Learning (CTL) model on a simple instument problem aims to find out the increase of the interest and and achievement of learning science, the response of students and teachers to the application of the models. Classroom action research was conducted in three cycles. The data of the improvement of interest in learning science and the students' and teachers' responses were obtained through a questionnaire. The cognitive result of the study was obtained through an achievement test. The affective and psychomotor results of the study were obtained through an observation. The result can be concluded that the application of game-based Contextual Teaching and Learning (CTL) model increased the interest and learning outcomes.
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs

Model hubs with many pre-trained models (PTMs) have become a cornerstone of deep learning. Although built at a high cost, they remain \emph{under-exploited} -- practitioners usually pick one PTM from the provided model hub by popularity and then fine-tune the PTM to solve the target task. This na\"ive but common practice poses two obstacles to full exploitation of pre-trained model hubs: first, the PTM selection by popularity has no optimality guarantee, and second, only one PTM is used while the remaining PTMs are ignored. An alternative might be to consider all possible combinations of PTMs and extensively fine-tune each combination, but this would not only be prohibitive computationally but may also lead to statistical over-fitting. In this paper, we propose a new paradigm for exploiting model hubs that is intermediate between these extremes. The paradigm is characterized by two aspects: (1) We use an evidence maximization procedure to estimate the maximum value of label evidence given features extracted by pre-trained models. This procedure can rank all the PTMs in a model hub for various types of PTMs and tasks \emph{before fine-tuning}. (2) The best ranked PTM can either be fine-tuned and deployed if we have no preference for the model's architecture or the target PTM can be tuned by the top $K$ ranked PTMs via a Bayesian procedure that we propose. This procedure, which we refer to as \emph{B-Tuning}, not only improves upon specialized methods designed for tuning homogeneous PTMs, but also applies to the challenging problem of tuning heterogeneous PTMs where it yields a new level of benchmark performance.