Reviewerly/exHarmony huggingface dataset:
Learning to Infer 3D Shape Programs with Differentiable Renderer

Given everyday artifacts, such as tables and chairs, humans recognize high-level regularities within them, such as the symmetries of a table, the repetition of its legs, while possessing low-level priors of their geometries, e.g., surfaces are smooth and edges are sharp. This kind of knowledge constitutes an important part of human perceptual understanding and reasoning. Representations of and how to reason in such knowledge, and the acquisition thereof, are still open questions in artificial intelligence (AI) and cognitive science. Building on the previous proposal of the \emph{3D shape programs} representation alone with the accompanying neural generator and executor from \citet{tian2019learning}, we propose an analytical yet differentiable executor that is more faithful and controllable in interpreting shape programs (particularly in extrapolation) and more sample efficient (requires no training). These facilitate the generator's learning when ground truth programs are not available, and should be especially useful when new shape-program components are enrolled either by human designers or -- in the context of library learning -- algorithms themselves. Preliminary experiments on using it for adaptation illustrate the aforesaid advantages of the proposed module, encouraging similar methods being explored in building machines that learn to reason with the kind of knowledge described above, and even learn this knowledge itself.
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning

Cutting planes are essential for solving mixed-integer linear problems (MILPs), because they facilitate bound improvements on the optimal solution value. For selecting cuts, modern solvers rely on manually designed heuristics that are tuned to gauge the potential effectiveness of cuts. We show that a greedy selection rule explicitly looking ahead to select cuts that yield the best bound improvement delivers strong decisions for cut selection - but is too expensive to be deployed in practice. In response, we propose a new neural architecture (NeuralCut) for imitation learning on the lookahead expert. Our model outperforms standard baselines for cut selection on several synthetic MILP benchmarks. Experiments with a B&C solver for neural network verification further validate our approach, and exhibit the potential of learning methods in this setting.
A Study on the Real-Time Visual Debugging of Hierarchically Designed Logic Circuits

Rapid changes have been taking place throughout society due to the development and deployment of various personal mobile portable terminals and communication networks such as 5G wireless communication and high-speed Internet.In the case of education field, learning through online virtual experiment is also being attempted in experimental subjects, which were representative offline education subjects.In order to increase the educational effect in such online education, it is essential to develop various contents and check visually the results of virtual experiments.This paper describes the implementation of a circuit simulator that enables real time visual confirmation of the circuit operation of the desired layer when designing a hierarchical digital logic circuit.The developed simulator was designed to visually confirm the operation of the circuit by operating circuit elements of each layer in real time using object-oriented and event-driven programming techniques and displaying the results on the screen, and its usefulness was verified by applying various hierarchical design circuits.
Research on Cloud Data Storage Security

Abstract: Cloud computing is a revolutionary mechanism that changing way to enterprise hardware and software design and procurements. Because of cloud simplicity everyone is moving data and application software to cloud data centers there still exist significant issues that need to be considered before shifting into cloud. Security stands as major obstacle in cloud computing. This paper gives an overview of the security issues on data storage along with its possible solutions many companies are not ready to implement cloud computing technology due to lack of proper security control policy and weakness in protection which lead to many challenge in cloud computing. An independent mechanism is required to make sure that data is correctly hosted in to the cloud storage server. In this paper, we will discuss the different techniques that are used for secure data storage on cloud. This paper also provides a process to avoid Collusion attacks of server modification by unauthorized users.
A General Recipe for Likelihood-free Bayesian Optimization

The acquisition function, a critical component in Bayesian optimization (BO), can often be written as the expectation of a utility function under a surrogate model. However, to ensure that acquisition functions are tractable to optimize, restrictions must be placed on the surrogate model and utility function. To extend BO to a broader class of models and utilities, we propose likelihood-free BO (LFBO), an approach based on likelihood-free inference. LFBO directly models the acquisition function without having to separately perform inference with a probabilistic surrogate model. We show that computing the acquisition function in LFBO can be reduced to optimizing a weighted classification problem, where the weights correspond to the utility being chosen. By choosing the utility function for expected improvement (EI), LFBO outperforms various state-of-the-art black-box optimization methods on several real-world optimization problems. LFBO can also effectively leverage composite structures of the objective function, which further improves its regret by several orders of magnitude.
Implementation of The Advanced Encryption Standard (AES) Algorithm for Digital Image Security

Nowadays, technological advances have made it increasingly easy to obtain information, especially image data (digital images). Digital image is an interesting thing to look for information. So that misuse of data can be done for personal or public interests. Misuse of data can be avoided by adding data security systems. Cryptography is the science of securing data. Cryptography can be done using the AES (Advanced Encryption Standard) algorithm, which is an algorithm that utilizes symmetric keys. Testing is done by entering the same key in the encryption and decryption process. Encryption is the process of encoding plaintext (original text) into ciphertext (text that has been encoded). While decryption is the process of recovering the plaintext from the ciphertext. Therefore, data security is an important thing to do. This study aims to find out how encryption and decryption on the AES algorithm can be used to secure digital data. The results of this study indicate that the encryption and decryption process on the AES algorithm was successfully carried out so that it can be used to secure data on digital images.
Dealing with Unwanted Donations: A Content Analysis of Small Academic Canadian Library Webpages

While archives and special collections continue to welcome unique and valuable resources, small academic libraries can struggle with how to manage donation offers intended for their main collections. There is a need to be selective considering falling print circulation, workload pressures on library personnel, and space restrictions. Additionally, limited collections funds needed for more current and higher-demand resources can be strained by the higher processing costs of donated materials. These pressures are compounded by prospective donors seeking a home for items they no longer want, a perception that small academic libraries need all donations, and a lack of understanding about the qualifications and expertise of academic library workers. Clearly communicated and regularly reviewed guidelines can help discourage unwanted donations in ways that lessen alienating our patrons. This article provides a content analysis of donations webpages from small academic libraries in Canada to identify trends and provide support for libraries reviewing their own policies and procedures in an effort to manage donor expectations.
A Simple Approach for Visual Rearrangement: 3D Mapping and Semantic Search

Physically rearranging objects is an important capability for embodied agents. Visual room rearrangement evaluates an agent's ability to rearrange objects in a room to a desired goal based solely on visual input. We propose a simple yet effective method for this problem: (1) search for and map which objects need to be rearranged, and (2) rearrange each object until the task is complete. Our approach consists of an off-the-shelf semantic segmentation model, voxel-based semantic map, and semantic search policy to efficiently find objects that need to be rearranged. On the AI2-THOR Rearrangement Challenge, our method improves on current state-of-the-art end-to-end reinforcement learning-based methods that learn visual rearrangement policies from 0.53% correct rearrangement to 16.56%, using only 2.7% as many samples from the environment.
Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding

Multi-modal document pre-trained models have proven to be very effective in a variety of visually-rich document understanding (VrDU) tasks. Though existing document pre-trained models have achieved excellent performance on standard benchmarks for VrDU, the way they model and exploit the interactions between vision and language on documents has hindered them from better generalization ability and higher accuracy. In this work, we investigate the problem of vision-language joint representation learning for VrDU mainly from the perspective of supervisory signals. Specifically, a pre-training paradigm called Bi-VLDoc is proposed, in which a bidirectional vision-language supervision strategy and a vision-language hybrid-attention mechanism are devised to fully explore and utilize the interactions between these two modalities, to learn stronger cross-modal document representations with richer semantics. Benefiting from the learned informative cross-modal document representations, Bi-VLDoc significantly advances the state-of-the-art performance on three widely-used document understanding benchmarks, including Form Understanding (from 85.14% to 93.44%), Receipt Information Extraction (from 96.01% to 97.84%), and Document Classification (from 96.08% to 97.12%). On Document Visual QA, Bi-VLDoc achieves the state-of-the-art performance compared to previous single model methods.
Investigating Metacognitive Reading Strategy Awareness of Saudi Tertiary Students: Comparisons of Gender, Reading Ability and Year Level

Students entering higher education are required to have higher order reading skills in order to succeed at the tertiary level. Comprehending complex texts requires them to possess certain strategies to compensate for any pitfalls of not grasping unfamiliar words or semantic structures especially in first-time read texts. The study, therefore, investigated the metacognitive reading strategy awareness of EFL Saudi students at the tertiary level in Saudi Arabia and its relationship with other variables such as year level and gender. A total of 355 students completed the Metacognitive Awareness of Reading Strategies Inventory (MARSI-R) (Mokhtari et al., 2018) . The data analysis delineated metacognitive awareness of EFL Saudi students of reading strategies and variations in awareness and use between different levels and genders. The most and least frequently used strategies among the students and correlation between different strategies and year and ability perception levels were also analysed. The findings indicated that the participants in this study had a medium level of awareness in all metacognitive reading strategies. The analysis also showed that the most frequently used strategy amongst Saudi students was problem-solving strategies (PSS) followed by support reading strategies (SRS) and the least frequently used strategy was global reading strategies (GRS). Awareness among students according to their perceived levels varied but only result of poor readers was statistically lower compared to other levels. Gender differences were all statistically insignificant across all subscales of reading strategies. The study concludes with a number of suggested pedagogical implications and future research recommendations. Keywords: Metacognitive awareness of reading strategies; EFL Reading; Reading comprehension; Reading strategies
ANALISIS FAKTOR KESULITAN BELAJAR MATEMATIKA MASA PANDEMI COVID-19 PADA PESERTA DIDIK KELAS IV SDS TRI SUKSES

The problem in this study is that students have difficulty learning mathematics during the Covid-19 pandemic. This study is aims to determine the factors of learning difficulties in mathematics in fourth grade students of SDS Tri Sukses, with the limitation of the problem of factors of learning difficulties in mathematics and efforts to overcome difficulties in learning mathematics in grade IV students of SDS Tri Sukses. The research method in this study is a qualitative method with a descriptive approach. Data collection techniques with interviews, observation and documentation. The data in the analysis refers to the data of Miles and Hubberman with the following steps: data collection, data reduction, data presentation, and conclusion drawing. The results of this study indicate that the factors that cause difficulty in learning mathematics come from internal factors, namely the health of students who are not optimal, negative attitudes in learning mathematics, student interest, and student motivation is still low. External factors include the use of teaching methods that are less varied, the lack of use of learning media, the relationship between educators and students who are less close and the family environment. Efforts made in overcoming difficulties in learning mathematics are teachers always provide motivation, provide contextual or real learning, increase interest in learning and help and guide students who have difficulty learning mathematics
CopyCat2: A Single Model for Multi-Speaker TTS and Many-to-Many Fine-Grained Prosody Transfer

In this paper, we present CopyCat2 (CC2), a novel model capable of: a) synthesizing speech with different speaker identities, b) generating speech with expressive and contextually appropriate prosody, and c) transferring prosody at fine-grained level between any pair of seen speakers. We do this by activating distinct parts of the network for different tasks. We train our model using a novel approach to two-stage training. In Stage I, the model learns speaker-independent word-level prosody representations from speech which it uses for many-to-many fine-grained prosody transfer. In Stage II, we learn to predict these prosody representations using the contextual information available in text, thereby, enabling multi-speaker TTS with contextually appropriate prosody. We compare CC2 to two strong baselines, one in TTS with contextually appropriate prosody, and one in fine-grained prosody transfer. CC2 reduces the gap in naturalness between our baseline and copy-synthesised speech by $22.79\%$. In fine-grained prosody transfer evaluations, it obtains a relative improvement of $33.15\%$ in target speaker similarity.
Preemptive text warping to prevent appearance of motion blur

We present a preemptive image-based method to reduce motion blurring. Motion blur appears when there is a relative motion between the scene and the viewer/camera. A preemptive method pre-filters the content before being displayed in order to mitigate the occurrence of motion blur. Our experiments and user study have shown that such preemptive methods are fundamentally subject to producing visual artifacts as a consequence of unavoidable ringing or intensity oscillations. Frequency-domain analysis shows that the energy weakened at certain frequencies leads to those artifacts. We present a method to process alphanumeric content so that it has lower energy on frequencies eliminated by a given motion blur kernel. Our processed image, when motion blurred, will have a sharper appearance and less artifacts as compared to various alternative approaches. We demonstrate the effectiveness of the proposed technique with simulated and real-world experiments as well as user feedback. Our results show that our approach yields content robust to motion blur while still being perceptually similar to the original text.
The effectiveness of egra technique to improve students' writing skills

This study aims to discover the increase in students' ability to write essays in English. It is a class action research that administered the EGRA technique in the learning process. This study involved two classes, namely the control class and the experimental class as the research sample. The qualitative descriptive method was used in analyzing the data. The data analyzed are presented in graphs and tables and then described qualitatively. Several theories are used in this study, such as error analysis, writing descriptive text, and EGRA techniques. The pre-test results showed the highest grammatical error score of control class results was 5.1, while the lowest score of students' grammatical errors was 2.8. After getting learning actions with conventional methods, there was a decrease in grammatical errors, the highest score was 3.4, while the lowest was 1.8. Meanwhile, the pre-test result of the experiment class showed the highest error score was 6.6, and the lowest score was 1.2. After the treatment administered with the EGRA technique, there is a decrease in grammatical errors, the highest score is 3.3, and the lowest score is 1.6. With the application of the EGRA technique in classroom learning, the student's grammatical ability increases, and students are more active and enjoyable in the learning process.
Analisis Biplot Robust dengan Metode Minimum Covariance Determinant dalam Mendeskripsikan Provinsi Sumatera Selatan Berdasarkan Karakteristik Angkatan Kerja Menganggur Dari Aspek Gender

This study describes the unemployed workforce from the gender aspect in each district in South Sumatra Province (Sumsel), using secondary data taken from the Central Statistics Agency (BPS) in 2020. This study was analyzed using the statistical data description method, namely Robust Biplot Analysis, to data that contains outliers. The results obtained are the Biplot Robust graphic display which can explain the diversity of the data by more than 70%. The districts of Muara Enim, Lahat, Musi Banyuasin, Ogan Ilir, Prabumulih, Lubuk Lingau, and Palembang have the characteristics of the male Open Unemployment Rate (TPT) and the female TPT which is higher than the average for other districts. The districts of North Musi Rawas, Palembang, and Lubuk Lingau have the characteristics of high educated male and female unemployment. The districts/cities of Ogan Komering Ulu, Ogan Komering Ilir, and Musi Rawas Utara need attention because they have a higher percentage of unemployment in the desperate category than other districts, both male and female.
Research Progress in Digital Pathology: a bibliometric and visual analysis based on Web of Science

Abstract Background: The development of Whole Slide Image (WSI) and deep neural network technologies has contributed to the paradigm shift in diagnostic pathology and has received a lot of attention from researchers, with related publications increasing year by year and "exploding" in recent years. However, few studies have systematically reviewed "digital pathology" using bibliometric tools. In this study, we will use multiple approaches to visualize and analyze "digital pathology" in order to provide a comprehensive and objective picture of the historical evolution and future development of the field. Methods: We use the term "subject=((“Artificial Intelligence” OR AI OR “Computational Intelligence” OR “Machine Intelligence” OR “Computer Vision” OR “Machine Learning” OR “Deep Learning” OR “Neural Network*” OR “Neural Net*” OR “Computer Assisted” OR “Artificial Intelligence Assisted” OR ”AI Assisted” OR “Digital Microscopy”) AND (*patholog* OR Pathomics OR“Whole Slide Imag*” OR WSI*)) OR “Digital *patholog*” OR “Computational *patholog*”, document type = ARTICLE OR PROCEEDINGS PAPER OR REVIEW, LANGUAGE=English, YEAR=2001 to 2021" was used to search the Web of Science core database and download the results. VOSviewer, CiteSpace, Gephi, and R were used to analyze the authors, institutional and national collaboration networks, keyword co-occurrence, and co-citation analysis to visualize the current status of global digital pathology (DP) research. Results: DP-related research is active in molecular, biological, and immunology journal groups, pharmaceutical, medical, and clinical journal groups, and psychology, education, and health journal groups, and other citation groups; in addition to "digital pathology," "diagnosis," "deep learning," "histopathology," and "surgical pathology" are also active research topics. In addition to "digital pathology," "diagnosis," "deep learning," "histopathology," and "surgical pathology" are also active research topics; the U.S. has significant research results in the field of DP, with the top 10 publishing institutions all coming from the U.S. In the past two decades, global DP-related research can be divided into two major research areas. One is about the system verification and optimization of WSI, and the other is about the application and development of artificial intelligence technology in the field of DP. Among them, based on the development of computer technology and the update of machine learning concept, the research results for deep neural network technology have been more concentrated in recent years. The powerful performance of deep neural networks in feature extraction and image analysis provides a new research direction for the improvement of digital pathology-aided diagnosis system, which is where the research hotspots are in recent years. Conclusion: The bibliometric analysis may help to better understand the current status of research within the field of DP and provide references and lessons for future related research.