Reviewerly/exHarmony huggingface dataset:
Progressive Network Grafting With Local Features Embedding for Few-Shot Knowledge Distillation

Compared with traditional knowledge distillation, which relies on a large amount of data, few-shot knowledge distillation can distill student networks with good performance using only a small number of samples. Some recent studies treat the network as a combination of a series of network blocks, adopt a progressive graft strategy, and use the output of the teacher network to distill the student network. However, this strategy ignores the importance of the local feature information generated by the teacher block, which indicates what features should be learned by the corresponding student block. In this paper, we argue that using the features output from the teacher block can guide the student block to further learn more useful information from the teacher block. Therefore, we propose a joint learning framework for few-shot knowledge distillation that exploits both the output of the teacher network and the local features generated by the teacher block to optimize the student network. The local features will guide the student block to learn the output of the teacher block, and the output of the teacher network will allow the student network to take its learned local features to better contribute to the classification. In addition, further model compression was carried out to design a series of student networks with fewer number of parameters by reducing the number of network channels. Finally, extensive experiments using the model on CIFAR10 and CIFAR100 datasets show that our method outperforms SOTA, and our method has considerable advantages even with a very small number of parameters in further model compression experiments.
Penerapan Model Pembelajaran Gagnon And Collay Berbantuan Games Dalam Meningkatkan Motivasi Dan Prestasi Belajar Siswa

Tujuan penelitian ini adalah mengidentifikasi penerapan model pembelajaran Gagnon and Collay berbantuan games dalam meningkatkan motivasi dan prestasi belajar siswa pada materi karakteristik makhluk hidup. Subyek penelitian ini adalah siswa kelas VII SMP Muhammadiyah Mataram. Jenis penelitian yang digunakan adalah Penelitian Tindakan Kelas (PTK) yang dilaksanakan dalam dalam dua siklus. Data motovasi belajar siswa dikumpulkan dengan quisioner dan data hasil belajar dikumpulkan dengan tes. Data yang diperoleh dianalisis mengunakan statistik deskriptif. Berdasarkan hasil analisis data menunjukan bahwa pada siklus I rata-rata motivasi belajar siswa sebesar 62,5% dengan kategori termotivaasi dan meningkat pada siklus II menjadi 75,6% dengan kategori sangat termotivasi. Kondisi tersebut memperlihatkan adanya peningkatan sebesar 13,18%. Sementara data rata-rata prestasi belajar siswa pada siklus 1 sebesar 74.2% dan meningkat pada siklus II sebesar 80,9% dengan nilai rata-rata peningkatan yaitu 14,9% setelah dilakukan tindakan pada setiap siklusnya. Berdasarkan analisis data penelitian dapat disimpulkan bahwa penerapan model pembelajaran Gagnon and Collay berbantuan games dapat meningkatkan motivasi dan prestasi belajar siswa kelas VII pada mata pembelajaran IPA-Biologi SMP Muhammadiyah Mataram. The Application Of The Gagnon And Collay Learning Model Helps Games In Increasing Student Motivation And Learning Achievement The purpose of this study is to identify the application of the Gagnon and Collay learning model assisted by games in increasing student motivation and learning achievement on material characteristics of living beings. The subject of this study was a grade VII student of Muhammadiyah Mataram Junior High School. The type of research used is Classroom Action Research (PTK) which is carried out in two cycles. Student learning data were collected with questionnaires and learning outcomes data were collected with tests. The data obtained were analyzed using descriptive statistics. Based on the results of data analysis, it shows that in cycle I the average student learning motivation was 62.5% with the category of termivation and increased in cycle II to 75.6% with the category of highly motivated. This condition showed an increase of 13.18%. Meanwhile, the average data on student learning achievement in cycle 1 was 74.2% and increased in cycle II by 80.9% with an average score of 14.9% after actions were taken in each cycle. Based on the analysis of research data, it can be concluded that the application of the Gagnon and Collay learning model assisted by games can increase the motivation and learning achievement of grade VII students in the science-biology learning course of Muhammadiyah Mataram Junior High School.
ENHANCING THE SECURITY OF AN ORGANIZATION FROM SHADOW IOT DEVICES USING BLOW-FISH ENCRYPTION STANDARD

Brilliant urban communities, gridlock, squander the board, primary well being, security, crisis administrations, coordination, retail, modern control, and medical care are only a couple of the applications that the Internet of Things can assist with. The Internet of Things is a super innovation that can associate with anything, anyone, whenever, spot, stage, and organization. It fundamentally affects the whole square chain of ventures, savvy items and gadgets, frameworks and administrations given by heterogeneous organization association, and is being created as a brilliant inescapable structure for shrewd gadgets. Since gadgets connect to confounded gear, collaborate with threatening environmental elements, and are sent on an assortment of unregulated stages, they defy a few security dangers and difficulties. Since the Internet of Things has the ability to coordinate any kind of organization or modern framework, it could be powerless against weaknesses innate in the different frameworks that make up the incorporated organization. The reason for this exploration paper is to examine the security gives that singular framework answerable for interconnection face, just as their effect on the in general framework.
COLLEGE/HIGH SCHOOL STUDENTS' CYBERSECURITY CAREER INTEREST

This study investigates college and high school students' career interest in cybersecurity. It intends to contribute to the research that explores millennials' and generation Z general lack of interest in the cybersecurity field. The dataset
أثر توفر الأدوات التقنية والمعرفة لدى الطالبة في رفع مستوى الاستيعاب والتحصيل الأكاديمي في بيئة التعليم الإلكتروني : دراسة حالة طالبات كلية المجتمع بخميس مشيط - جامعة الملك خالد = The Impact of the Availability of the Student’s Technical Tools and Knowledge and Raising the Level of Students’ Absorption and Attainment in the E-Learning Environment : Case Study Girls’ Community College, King Khalid University

التعليم الإلكتروني | التعليم التقليدي | نموذج بدر خان | استيعاب الطلاب | تحصيل الطلاب | جامعة الملك خالد | فايروس كورونا (كوفيد-19) | E-Learning | Traditional Learning | Badrul Khan Model | Students' Absorption | Students' Attainment | King Khalid University | Coronavirus disease (Covid-19)
Model Matematika Penyebaran Hoax COVID-19

In this article, the problem of spreading hoaxes during the corona-19 outbreak is studied using a mathematical model. Currently, we often see a lot of hoaxes that are very unsettling, for example the news that eggs are a corona drug. In addition, there have been denials of funerals for Covid victims in various regions. In this article, the impact of government education and outreach, decisive action against hoax spreaders and ignorance of people who understand the problem of Covid-19 regarding the spread of hoaxes. The model built using 4 compartments, equilibrium point, free hoax spreader, basic reproduction number and sensitivity analysis are discussed in this article. Several numerical simulations are provided to test the theoretical study of the model
A Multiscale Adaptive Rift Method for Low Coherence Sar Image Matching

In this paper, an improved Radiation-Variation Insensitive Feature Transform (RIFT) method is proposed for low co-herence SAR image matching. First, pyramid scale space is introduced in the period of feature transformation, which can extract features with different scales, increase the num-ber of feature points and achieve the scale invariance of the algorithm. Then, the key parameters of the RIFT algorithm are calculated and selected adaptively according to the sizes and statistical characteristics of the images, so as to acquire feature points with even distribution. Finally, we conduct experiments on SAR images obtained at different wavebands, platforms and times. The experimental results show that, the proposed method can obtain more feature points with more even distribution, and the matching accuracy is also improved.
Deeply Learned Invariant Features for Component-based Facial Recognition

Face recognition underage variation is a challenging problem.It is a difficult task because ageing is an intrinsic variation, not like pose and illumination, which can be controlled.We propose an approach to extract invariant features to improve facial recognition using facial components.Can facial recognition over age progression be improved by resizing independently each individual facial component?The individual facial components: eyes, mouth, and nose were extracted using the Viola-Jones algorithm.Then we utilize the eyes region rectangle with upper coordinates to detect the forehead and lower coordinates with the nose rectangle to detect the cheeks.The proposed work uses Convolutional Neural Network with an ideal input image size for each facial component according to many experiments.We sum up component scores by applying weighted fusion for a final decision.The experiments prove that the nose component provides the highest score contribution among other ones, and the cheeks are the lowest.The experiments were conducted on two different facial databases-MORPH, and FG-NET databases.The proposed work achieves a state-of-the-art accuracy that reaches 100% on the FG-NET dataset and the results obtained on the MORPH dataset outperform the accuracy results of the related works in the literature.
Maintenance and evolution

E-type open-source software inevitably grows in size and complexity over time, and without performing anti-regressive tasks this type of software has a limited lifespan. In this project, a case study of the effect of such anti-regressive tasks is conducted using Grimoire-Lab Graal as a subject. This process is guided by quality metrics and developer insights. The outcome of this work is a life-cycle of maintenance activities, ultimately resulting in a refactored version of GrimoireLab Graal. After applying anti-regressive actions, commonly used software quality metrics decreased (lower is better). Additionally, after performing an experiment to test the evolution readiness of the software, the complexity of the original software increased significantly, whilst no side effects were measured in the revised software.
Resource approach in diagnostics of pedagogical workers of educational organizations of Russia

Introduction. The introduction of a new model of professional development of education workers in Russia involves the technology of an individual development route developed on the basis of previously identified professional deficits. However, approaches to identifying deficits do not yet serve as a proper source of such routes, since they state the facts of subject or methodological difficulties of teachers in isolation from the context of the teacher's professional activity. As a possible solution to the current situation, it is proposed to approach the diagnosis of pedagogical workers in the logic of the resource approach, when a particular shortage of a pedagogical worker is interpreted in two aspects: as a consequence of the scarcity of other resources of an educational organization, for example, managerial or methodological, and as a cause of gaps in educational achievements of students. Materials and methods. The study was conducted on the material of a freely available regional report on the results of diagnostics of professional competencies of teachers. The key method is document analysis. The subject of the study were: the conclusions made in the report on the degree of reflection in diagnostic materials of the purpose and objectives of diagnostics; the structure and purpose of diagnostic data; the typology of tasks used to identify professional deficits of teachers; commentary on the methods of processing primary information. Results. The following costs of working with data based on the results of diagnostics of pedagogical workers of educational organizations are revealed: the predominance of stating facts over their comprehension; the lack of contextual assessment of the stated data; limitations in the use of data; the undetected connection of teacher deficits with the gaps of the students themselves. It is concluded that the work with data on teachers is built in the pledge of conditions, and not in the pledge of resources. That is, the data only indicate the existing level of pedagogical conditions, but do not explain the potential of professional skills of teachers, their resource capacity. Conclusion. The use of a resource approach in working with data on teachers requires updating the structure of analytical data, mandatory inclusion of contextual information; more attention to the analysis of the relationship between teacher deficits and student deficits. We consider promising further research approaches to the analysis of the results of diagnostics of professional competencies of teachers. We consider the resource approach proposed by us as one of the possible, but not exhaustive, arsenal of analytical practices that allow using data on teachers to manage the quality of general education.
Regime-change-induced modification of jet flow in flute-like instruments with a hysteresis region

Sound production in flue instruments (e.g., flutes and recorders) results from the interaction of an air jet with a strike edge that is coupled to a resonator. This paper presents evidence for a peculiar aspect of air jet behavior observed when a flute or recorder powered by an artificial blower is set up to play in a bistable hysteresis region where both an upper and lower tone are stable at the same blowing pressure. Under these conditions, the sounded tone is determined by the pressure history of the system, and a switch between the two tones can be accomplished by a momentary increase or decrease in the blowing pressure. An unexpected finding was that the jet pressure recorded by an external pressure probe positioned downstream of the strike edge was sensitive to the tone being played. With an optimally positioned probe, switching from one tone to the other typically produced a reproducible 20-30% change in jet pressure even though the blowing pressure remained fixed. Additional measurements suggest that this pressure change results from a macroscopic change in the steady-state angular width of the jet. It is hoped that these results will stimulate further measurements and modeling by others.
BiTMulV: Bidirectional-Decoding Based Transformer with Multi-view Visual Representation

The Transformer-based image captioning models have made significant progress on the generalization performance. However, most methods still have two kinds of limitations in practice: 1) Heavily rely on the single region-based visual feature representation. 2) Not effectively utilize the future semantic information during inference. To solve these issues, we introduce a novel bidirectional-decoding based Transformer with multi-view visual representation (BiTMulV) for image captioning. In the encoding stage, we adopt a modular cross-attention block to fuse both grid features and region features by virtue of multi-view visual feature representation, which realizes full exploitation of image context information and fine-grained information. In the decoding stage, we design the bidirectional decoding structure, which consists of two parallel and consistent forward and backward decoders, to promote the model to effectively combine the history with future semantics for inference. Experimental results on the MSCOCO dataset demonstrate that our proposal significantly outperforms the competitive models, improving by 1.5 points on the CIDEr metric.
Penerapan Metode Pembelajaran Show and Tell pada Materi Iklan untuk Meningkatkan Keterampilan Berbicara Siswa Kelas V UPTD SD Negeri 145 Barru

This study examines the application of the Show and Tell learning method on advertising materials to improve the speaking skills of students class V UPTD SD Negeri 145 Barru. The approach used is a qualitative approach and the type of research is Classroom Action Research (CAR). The implementation of this research action was carried out in 2 cycles starting with pre-action activities then in each cycle consisting of 4 stages which included planning, implementation, observation, and reflection. Meanwhile, to find out the process of applying and improving students' speaking skills, the researchers used observation sheets and performance tests. The subjects in this study were students of class V UPTD SD Negeri 145 Barru in the academic year 2020/2021, which amounted to 22 students consisting of 10 boys and 12 girls. Data analysis was carried out during and after data collection. The data analysis technique used is qualitative data analysis techniques. Based on the data obtained during the implementation of cycle I and Cycle II, it was found that the results of the research in the first cycle showed a sufficient category, and an increase in the second cycle which showed a good category and achieved the predetermined indicators. The conclusion of this study is that by applying the Show and tell learning method on advertising material, it can improve process of learning and speaking skills of the students class V of UPTD SD Negeri 145 Barru.
Research on fault simulation of atmospheric data measurement system based on AMESim

In order to quickly and accurately locate system faults, the AMESim simulation analysis method is applied for the fault diagnosis of atmospheric data measurement systems. The results show that by changing the model settings and parameters, simulating the failure mode, and comparing the failure phenomena for fault location, accurate fault diagnosis can be achieved, and most suspected causes of faults can be eliminated. At the same time, the fault simulation analysis significantly reduces the workload of experiments, saves resources, shortens the troubleshooting cycle, and saves time for the model development.
Organizing Committee

List of Organizers and Organizing Committee of the Musical Metacreation 2012 AIIDE Workshop
Modeling Unseen Entities from a Semantic Evidence View in Temporal Knowledge Graphs

Temporal Knowledge Graphs (TKGs), which model dynamic events along the timeline, have attracted much attention in recent years. For temporal knowledge graphs suffer from incompleteness, quite a lot of researches are devoted to TKG reasoning, attempting to predict missing temporal facts from known events. Depending on different tasks of TKG reasoning, existing work can be divided into two categories: interpolation and extrapolation. Compared with interpolation, extrapolation faces more challenges and difficulties, which is intended to forecast facts in the future. It’s worth noting that dealing with unseen entities effectively is one of important challenges, while only a few of prior work focuses on handling unseen data explicitly. To this end, we model unseen entities from semantic perspectives in this work, combining with temporal-path-based reinforcement learning, which guarantees the interpretability of reasoning. Through extensive experiments on standard TKG tasks, we verify that our model performs well on link prediction task at future timestamps, demonstrating better extrapolation capabilities.
A Process for Gathering Data to Train the NLU Module of a NLI System for ITV

The performance of the natural language understanding (NLU) module is a key issue when developing a natural language interaction (NLI) system. For the NLU to perform in an accurate way, it must be trained to correctly understand a wide scope of users’ intentions and give (correct) answers. The interaction context must also be considered, meaning that optimizing a NLI system for the TV domain, with a conversational dynamic close to that of humans, involves a complex process capable of gathering a relevant set of natural and diverse utterances for the television specific context. Considering this, the purpose of this paper is to report a process for gathering utterances, which was later be converted into selected data to train the NLU module of a NLI system developed to search a huge number of video content and to control the ever-increasing features of a Smart TV or a Set-Top Box (STB). The process involved real users’ data, close monitoring, and active intervention of a support team. The results of the study highlight that integrating easy-to-use feedback mechanisms (WhatsApp and “Flag Failure” buttons embedded in the mobile app), along with the creation of a continuous dynamic with the participants to stimulate their interactions, are paramount to improving the collection of natural and diverse utterances.
A New Chaotic Image Encryption Algorithm based on ILSS System and RAN Operation

For ensuring the secure transmission of image information, an encryption algorithm is proposed based on ILSS system and RAN operations. At first, A new chaotic system is designed, think it is an improvement to traditional logic and sinusoidal charts. Secondly, its SHA 512 hash function can be used in combination to generate chaotic sequence. In the third, on the basis of this novel chaotic system, image encryption is performed by the method of RAN encoding rules. Analyzing the security of encryption algorithm through key space, correlation, and information entropy method. Experimental results show that the encryption algorithm has high safety performance.
DOMA: Deep Smooth Trajectory Generation Learning for Real-Time UAV Motion Planning

In this paper, we present a Deep Reinforcement Learning (DRL) based real-time smooth UAV motion planning method for solving catastrophic flight trajectory oscillation issues. By formalizing the original problem as a linear mixture of dual-objective optimization, a novel Deep smOoth Motion plAnning (DOMA) algorithm is proposed, which adopts an alternative layer-by-layer gradient descending optimization approach with the major gradient and the DOMA gradient applied separately. Afterward, the mix weight coefficient between the two objectives is also optimized adaptively. Experimental result reveals that the proposed DOMA algorithm outperforms baseline DRL-based UAV motion planning algorithms in terms of both learning efficiency and flight motion smoothness. Furthermore, the UAV safety issue induced by trajectory oscillation is also addressed.
Association Analysis of Fault Measuring Points based on New Link Prediction

Many measuring points on airborne are important reasons for the redundancy of aircraft electromechanical data, which leads to the low quality of airborne data. This paper presents a kind of aircraft electromechanical fault measuring points correlation prediction analysis based on the new link prediction. This method combines higher-order network representation learning algorithm with link prediction to consider higher-order similarity relationships between nodes. Then, through the analysis of the observation points, the link prediction algorithm is used to obtain the relationship between the observation points. Finally, the optimization improves the quality of the airborne data. After simultaneous experimental simulations on four real datasets, the results show good link prediction performance based on high-order approximation.