Reviewerly/exHarmony huggingface dataset:
Robustness Analysis of Gaussian Process Convolutional Neural Network with Uncertainty Quantification

This paper presents a novel framework for image classification which comprises a convolutional neural network (CNN) feature map extractor combined with a Gaussian process (GP) classifier.Learning within the CNN-GP involves forward propagating the predicted class labels, then followed by backpropagation of the maximum likelihood function of the GP with a regularization term added.The regularization term takes the form of one of the three loss functions: the Kullback-Leibler divergence, Wasserstein distance, and maximum correntropy.The training and testing are performed in mini batches of images.The forward step (before the regularization) involves replacing the original images in the mini batch with their close neighboring images and then providing these to the CNN-GP to get the new predictive labels.The network performance is evaluated on MNIST, Fashion-MNIST, CIFAR10, and CIFAR100 datasets.Precision-recall and receiver operating characteristics curves are used to evaluate the performance of the GP classifier.The proposed CNN-GP performance is validated with different levels of noise, motion blur, and adversarial attacks.Results are explained using uncertainty analysis and further tests on quantifying the impact on uncertainty with attack strength are carried out.The results show that the testing accuracy improves for networks that backpropagate the maximum likelihood with regularized losses when compared with methods that do not.Moreover, a comparison with a state-of-art CNN Monte Carlo dropout method is presented.The outperformance of the CNN-GP framework with respect to reliability and computational efficiency is demonstrated.
Security against Collective Attacks for a Continuous-Variable Quantum Key Distribution Protocol Using Homodyne Detection and Postselection

We analyze the security against collective attacks for a homodyne-based continuous-variable quantum key distribution protocol using binary coherent states and postselection. We derive a lower bound of the secret key rate in an asymptotic scenario, and numerically optimize it for the case of a symmetric Gaussian channel.
Charon: a FrameNet Annotation Tool for Multimodal Corpora

This paper presents Charon, a web tool for annotating multimodal corpora with FrameNet categories. Annotation can be made for corpora containing both static images and video sequences paired - or not - with text sequences. The pipeline features, besides the annotation interface, corpus import and pre-processing tools.
Constrained Monotonic Neural Networks

Wider adoption of neural networks in many critical domains such as finance and healthcare is being hindered by the need to explain their predictions and to impose additional constraints on them. Monotonicity constraint is one of the most requested properties in real-world scenarios and is the focus of this paper. One of the oldest ways to construct a monotonic fully connected neural network is to constrain signs on its weights. Unfortunately, this construction does not work with popular non-saturated activation functions as it can only approximate convex functions. We show this shortcoming can be fixed by constructing two additional activation functions from a typical unsaturated monotonic activation function and employing each of them on the part of neurons. Our experiments show this approach of building monotonic neural networks has better accuracy when compared to other state-of-the-art methods, while being the simplest one in the sense of having the least number of parameters, and not requiring any modifications to the learning procedure or post-learning steps. Finally, we prove it can approximate any continuous monotone function on a compact subset of $\mathbb{R}^n$.
THE STATE OF THE PROBLEM OF COGNITIVE FORMATION ACTIVITIES IN THE PRACTICE OF WORK SECONDARY SCHOOLS

The article is dedicated to the problem of forming of the activity ofpupils cognitive work in the practice of secondary schools.
Accounts Receivable Information System at PT. Latinusa, Tbk

The purpose of this research is to determine the suitability of accounts receivable accounting information at PT. Latinusa Tbk. The method used in writing this final assignment is descriptive method. This writing uses primary data and secondary data obtained from PT. Latinusa Tbk. Data sources come from field studies and literature. How to collect data through observation, interviews and documentation collected from PT. Latinusa Tbk. Based on the results of industry internships obtained in the field, account receivable accounting information system at PT. Latinusa Tbk, is in accordance with the theory.
Identification of digital modulation based on information entropy

The classification of modulated signals under a low signal-to-noise ratio (SNR) environment has become a hot topic due to the complexity of the communication environment. Many relevant publications deal with signal recognition with stable SNR but are not applicable in time-varying SNR scenarios. To solve this problem, we propose a new method for determining the types of modulation based on entropy analysis. The proposed algorithm first extracts characteristics using different types of entropy and can separate the types of phase modulation (PSK): BPSK, QPSK, 8PSK, 16PSK, 32PSK, and 64PSK. In comparison with traditional feature extraction methods, the proposed algorithm increases the efficiency of signal classification. The results show that the algorithm can achieve better signal classification effects, even if SNR reaches -4 dB.
Lutma: a Frame-Making Tool for Collaborative FrameNet Development

This paper presents Lutma, a collaborative, semi-constrained, tutorial-based tool for contributing frames and lexical units to the Global FrameNet initiative. The tool parameterizes the process of frame creation, avoiding consistency violations and promoting the integration of frames contributed by the community with existing frames. Lutma is structured in a wizard-like fashion so as to provide users with text and video tutorials relevant for each step in the frame creation process. We argue that this tool will allow for a sensible expansion of FrameNet coverage in terms of both languages and cultural perspectives encoded by them, positioning frames as a viable alternative for representing perspective in language models.
Video Summarization Using Deep Learning for Cricket Highlights Generation

Recently, video surveillance technology has grown pervasive in many aspects of our lives. Automatic video monitoring produces massive amounts of data that need human examination at some point. The primary emphasis is on reducing storage usage by compressing or eliminating superfluous frames without sacrificing real information. The current effort seeks to close the growing gap between the amounts of real data and the volume. Searching through key events in large video collections is time-consuming and tedious. In this paper, smart surveillance for various applications by using video summarization has been presented. A method for generating highlights has presented which pre-processes extracted Video Frames. Convolutional Neural Networks are then used to evaluate these highlighted frames. The proposed technique extracts and calculates characteristics utilized to generate summary movies. For training deep neural networks, cricket datasets have been used. Experimental results show that the proposed solution attains improved results than other advanced summarization methodologies. Experimental results show that the proposed video summarization method consistently generates high-quality reviews for all types of videos. The proposed video summarization method is easy to use, and it can also help extract highlights of cricket games with high accuracy.
Utilization of the SIMPACA Application to improve the quality of nursing care documentation for nurses

The phenomenon of nurses in hospitals being preoccupied with recording documentation activities manually compared to providing independent nursing care to patients. One of the reasons for improving the behavior of nurses in documenting nursing care is increasing the ability of nurses to do documentation using application assistance to facilitate the work of nurses. This community service activity aims to improve the ability of nurses to document effectively and efficiently. The activity was carried out for two days through a zoom meeting. Previously, at the beginning of the activity, a nurse's ability pre-test was conducted and at the end of the session, another measurement was made for the nurse's ability. Community service activities were carried out with 45 nurses in South Sumatra. During the activity, the participants were very enthusiastic in listening, discussing with resource persons, and doing simulations well. There is an increase in documentation ability before and after the documentation training using SIMPACA is carried out. SIMPACA is proven to be effective in improving nurses' documentation skills and reducing nurses' time in documenting their nursing care to patients. There is a need for further development of SIMPACA because it must adapt to the needs of the hospital.
CONNECTING THE MASTERY LEVEL IN TRANSLATING VERBAL STATEMENTS TO MATHEMATICAL SYMBOLS WITH VOCABULARY ENHANCEMENT

Translation from words to symbols is undeniably one of the solution processes in solving word problems that can be considered critical (Bardillion, Jr. 2004) cited by Dela Cruz, et.al (2014).Failure to translate the verbal statement to mathematical symbols can lead to incorrect solution.This study aimed at giving remedy to the least mastered competency in translating verbal phrases to mathematical symbols and improving the academic performance of the Grade 7 students at Tabuk City National High School, Tabuk City Division, School Year 2017-2018 using the Strategic Intervention Material developed by Ms. Apple Jean G. Yecyec (n.d.).This study focused on the fifteen (15) identified students having a score of 1-5 on a given formative test.A teacher-made pretest and posttest was utilized.The participants had a "Very Low" performance on the pre-test at 24.65% level of mastery with regards to translating verbal phrases to mathematical symbols.Results showed there was a significant difference between the pretest and posttest of the participants.It implies that the strategic intervention material by Yecyec on translating verbal phrase to mathematical symbols is effective in improving the mastery level on the least learning competency.Thus, the Strategic Intervention Material (SIM) by Yecyec on translating verbal phrases to mathematical symbols is strongly recommended for dissemination and utilization.
Building a Cumulative Science of Memory Development

Cumulative science hinges on consolidating empirical evidence. However, both narrative reviews and meta-analyses often restrict integration by using construct labels in searches, which vary greatly across traditions and eras in psychology. An alternative is the mapping review, more common in other disciplines, which focuses on operational definitions and experimental design. Here, we pioneered using this approach in psychology, applying it to memory development. We classified 506 publications in the last 5 decades using 16 design features and identified methodologically convergent work that originated from different theoretical frameworks. New insights emerge from this approach. First, characterizing the relative data density along each dimension of experimental design identifies where future research is most necessary. Second, we linked relevant evidence previously separated by nonoverlapping construct labels. Third, we illustrated the value of this technique with an analysis of pattern separation (also called mnemonic discrimination). In order to facilitate the process of literature integration and identification of methodological overlap, we created a freely available interactive web application using the current database.
Masked Bayesian Neural Networks : Computation and Optimality

As data size and computing power increase, the architectures of deep neural networks (DNNs) have been getting more complex and huge, and thus there is a growing need to simplify such complex and huge DNNs. In this paper, we propose a novel sparse Bayesian neural network (BNN) which searches a good DNN with an appropriate complexity. We employ the masking variables at each node which can turn off some nodes according to the posterior distribution to yield a nodewise sparse DNN. We devise a prior distribution such that the posterior distribution has theoretical optimalities (i.e. minimax optimality and adaptiveness), and develop an efficient MCMC algorithm. By analyzing several benchmark datasets, we illustrate that the proposed BNN performs well compared to other existing methods in the sense that it discovers well condensed DNN architectures with similar prediction accuracy and uncertainty quantification compared to large DNNs.
Extreme Compression for Pre-trained Transformers Made Simple and Efficient

Extreme compression, particularly ultra-low bit precision (binary/ternary) quantization, has been proposed to fit large NLP models on resource-constraint devices. However, to preserve the accuracy for such aggressive compression schemes, cutting-edge methods usually introduce complicated compression pipelines, e.g., multi-stage expensive knowledge distillation with extensive hyperparameter tuning. Also, they oftentimes focus less on smaller transformer models that have already been heavily compressed via knowledge distillation and lack a systematic study to show the effectiveness of their methods. In this paper, we perform a very comprehensive systematic study to measure the impact of many key hyperparameters and training strategies from previous works. As a result, we find out that previous baselines for ultra-low bit precision quantization are significantly under-trained. Based on our study, we propose a simple yet effective compression pipeline for extreme compression, named XTC. XTC demonstrates that (1) we can skip the pre-training knowledge distillation to obtain a 5-layer BERT while achieving better performance than previous state-of-the-art methods, e.g., the 6-layer TinyBERT; (2) extreme quantization plus layer reduction is able to reduce the model size by 50x, resulting in new state-of-the-art results on GLUE tasks.
Exploring the System of Punitive Damages for Trademark Malicious Prosecution

The phenomenon of trademark malicious prosecution is common, endangering individual private rights and public interests, and the existing legal system is difficult to effectively regulate and remedy the rights of the defendant. In response to this dilemma, it is necessary to establish a punitive damages system to regulate the nature of trademark malicious prosecution based on the "abuse of rights". It is necessary to clarify the criteria of "malicious prosecution" and "seriousness of the circumstances", as well as to formulate the scope of compensation in line with the "optimal deterrence" and balance of interests, to protect the interests of the relevant rights holders, maintain market order and judicial authority.
Graph Backup: Data Efficient Backup Exploiting Markovian Transitions

The successes of deep Reinforcement Learning (RL) are limited to settings where we have a large stream of online experiences, but applying RL in the data-efficient setting with limited access to online interactions is still challenging. A key to data-efficient RL is good value estimation, but current methods in this space fail to fully utilise the structure of the trajectory data gathered from the environment. In this paper, we treat the transition data of the MDP as a graph, and define a novel backup operator, Graph Backup, which exploits this graph structure for better value estimation. Compared to multi-step backup methods such as $n$-step $Q$-Learning and TD($\lambda$), Graph Backup can perform counterfactual credit assignment and gives stable value estimates for a state regardless of which trajectory the state is sampled from. Our method, when combined with popular value-based methods, provides improved performance over one-step and multi-step methods on a suite of data-efficient RL benchmarks including MiniGrid, Minatar and Atari100K. We further analyse the reasons for this performance boost through a novel visualisation of the transition graphs of Atari games.
RESEARCH OF VECTOR TILE CONSTRUCTION TECHNOLOGY BASED ON APACHE SEDONA

Abstract. With the development of spatial information technology, the amount of geographic information data shows an explosive growth, which puts forward higher demands on the time efficiency and visualization effect of geographic information data release. vector tile maps have become the main map service mode in the fields of Internet maps and GIS industries because of the advantages of its powerful interactive capabilities, efficient data transmission and lower storage costs. In order to construct massive vector tiles quickly and enhance the scalability of vector tiles application, this paper researched the vector tiles construction technology based on the distributed computing framework. Firstly, we introduced the construction process of vector tiles such as pyramid model, data organization and data generalization, Specifically, the data generalization methods of different geometric types were discussed. Second, Apache Sedona, the distributed computing framework were researched and the advantages for vector data processing is introduced. Then, the Sedona based parallel construction technology is proposed, and pipeline optimization of Spark is applied in this process. Third, a comparative experiment to evaluate the performance were conducted, the result showed that the parallel construction technology had obvious performance advantages, the greater the volume and extent of vector data, the greater the advantage.
FEATURES OF THE DEVELOPMENT OF ECOLOGICAL CONSCIOUSNESS OF STUDENTS DURING THEIR STUDIES AT THE UNIVERSITY

The article discusses the importance of the development of students’ ecological consciousness during the period of vocational training. Ecological consciousness is considered as an integrative formation of a personality, the structural components of which are motivational-value, cognitive, reflective, emotional and activity-practical. It is concluded that the motivational-value component of ecological consciousness is central and backbone. It is noted that the development of the ecological consciousness of students in the educational system of the university should be provided with a holistic complex of psychological and pedagogical conditions using various methods. It is concluded that for the effective development of the ecological consciousness of a future specialist, a specially organized environmentally-oriented educational environment is important.
Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions

Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely unstudied for image-based 6D object pose estimation. In this work, we introduce the first knowledge distillation method driven by the 6D pose estimation task. To this end, we observe that most modern 6D pose estimation frameworks output local predictions, such as sparse 2D keypoints or dense representations, and that the compact student network typically struggles to predict such local quantities precisely. Therefore, instead of imposing prediction-to-prediction supervision from the teacher to the student, we propose to distill the teacher's \emph{distribution} of local predictions into the student network, facilitating its training. Our experiments on several benchmarks show that our distillation method yields state-of-the-art results with different compact student models and for both keypoint-based and dense prediction-based architectures.
Evaluasi Penggunaan Media Pembelajaran E-Learning pada Mata Pelajaran IPS Era Pandemi Kelas IX di SMP Islam Al Azhar 38 Wonosari Kabupaten Gunungkidul

This study aims to describe the evaluation of the use of e-learning-based learning media in social studies subjects in the era of the epidemic of class IX at Al Azhar 38 Islamic Junior High School, Wonosari, Gunungkidul Regency. This study uses a qualitative method with a case study approach. The sources of data in this study were teachers, parents and students of class IX Islamic Junior High School Al Azhar 38 Wonosari, Gunungkidul Regency. Several data collection techniques used in this study were interviews, observations and online documentation. The results of this study are (1) to describe various types of e-learning learning media that are relevant for learning social studies subjects. (2) The advantages and disadvantages of e-learning-based evaluation media. (3) Other findings in this study indicate an evaluation model of the use of e-learning media in social studies learning that has been implemented including portfolio-based and project-based evaluations. However, it is undeniable that in the application of the alternative evaluation model there are several constraining factors faced, namely the lack of enthusiasm of parents and students, knowledge of parents in the application of the evaluation model, and the lack of cooperation between teachers and parents.
STRUCTURING PEDAGOGICAL CONTENT PRACTICES OF STUDENTS BY SPECIALTY "PRESCHOOL EDUCATION"

The article deals with the structure of the content of the pedagogical the practice o f the student’s — of future tutors I-II courses pedagogical university, direct on the formation of the readiness for the upbringing of responsibility in the preschool children’s.
Semiconductor quantum dot based single-photon sources as resources for quantum communication and cryptography applications

We present the latest developments carried on in Quandela, where solid-state based quantum light sources with increased performances and usability were realized, allowing for plug-and-play operation, portability and integration in commercial infrastructure.
Software Mitigation of RISC-V Spectre Attacks

Speculative attacks are still an active threat today that, even if initially focused on the x86 platform, reach across all modern hardware architectures. RISC-V is a newly proposed open instruction set architecture that has seen traction from both the industry and academia in recent years. In this paper we focus on the RISC-V cores where speculation is enabled and, as we show, where Spectre attacks are as effective as on x86. Even though RISC-V hardware mitigations were proposed in the past, they have not yet passed the prototype phase. Instead, we propose low-overhead software mitigations for Spectre-BTI, inspired from those used on the x86 architecture, and for Spectre-RSB, to our knowledge the first such mitigation to be proposed. We show that these mitigations work in practice and that they can be integrated in the LLVM toolchain. For transparency and reproducibility, all our programs and data are made publicly available online.