Reviewerly/exHarmony huggingface dataset:
LapNet : Automatic Balanced Loss and Optimal Assignment for Real-Time Dense Object Detection

Real-time single-stage object detectors based on deep learning still remain less accurate than more complex ones. The trade-off between model performance and computational speed is a major challenge. In this paper, we propose a new way to efficiently learn a single-shot detector which offers a very good compromise between these two objectives. To this end, we introduce LapNet, an anchor based detector, trained end-to-end without any sampling strategy. Our approach aims to overcome two important problems encountered in training an anchor based detector: (1) ambiguity in the assignment of anchor to ground truth and (2) class and object size imbalance. To address the first limitation, we propose a soft positive/negative anchor assignment procedure based on a new overlapping function called "Per-Object Normalized Overlap" (PONO). This soft assignment can be self-corrected by the network itself to avoid ambiguity between close objects. To cope with the second limitation, we propose to learn additional weights, that are not used at inference, to efficiently manage sample imbalance. These two contributions make the detector learning more generic whatever the training dataset. Various experiments show the effectiveness of the proposed approach.
High quality monocular depth estimation with parallel decoder

Monocular depth estimation aims to recover the depth information in three-dimensional (3D) space from a single image efficiently, but it is an ill-posed problem. Recently, Transformer-based architectures have achieved excellent accuracy in monocular depth estimation. However, due to the characteristics of Transformer, the model parameters are huge and the inference speed is slow. In traditional convolutional neural network-based architectures, many encoder-decoders perform serial fusion of the multi-scale features of each stage of the encoder and then output predictions. However, in these approaches it may be difficult to recover the spatial information lost by the encoder during pooling and convolution. To enhance this serial structure, we propose a structure from the decoder perspective, which first predicts global and local depth information in parallel and then fuses them. Results show that this structure is an effective improvement over traditional methods and has accuracy comparable with that of state-of-the-art methods in both indoor and outdoor scenes, but with fewer parameters and computations. Moreover, results of ablation studies verify the effectiveness of the proposed decoder.
Artificial intelligence enabled conversational agent for mental healthcare

Conversational agents are a software program that can converse with users in the manner of a real-world conversation. Artificial intelligence (AI) is not complete without conversation modeling. The most difficult artificial intelligence endeavor since its start has been developing an effective chatbot application. Despite chatbots may do a variety of tasks, their main duty is to accurately understand human speech and respond appropriately. Previously, manual patterns and instructions or simple statistical methods were used to create chatbots architectures. Due to its improved capacity for training, end-to-end AI has replaced these models since 2015. The most popular technique for conversation simulation at the moment is the encoder-decoder recurrent neural network (RNN). The realm of language comprehension served as inspiration for this design. Until recently, a number of additions and changes dramatically enhanced chatbot conversational abilities. In this paper, we outline our research results into creating an interactive digital chatbot that may provide patients with psychological assistance. To build and train the chatbot, we used resources such Rasa Natural Language Processing (NLU) technology, which employs natural language processing (NLP) methods. The results of the investigation showed that selecting proper responses while conversing with patients had a more than 70% predictive performance.
Robustness analytics to data heterogeneity in edge computing

Federated Learning is a framework that jointly trains a model \textit{with} complete knowledge on a remotely placed centralized server, but \textit{without} the requirement of accessing the data stored in distributed machines. Some work assumes that the data generated from edge devices are identically and independently sampled from a common population distribution. However, such ideal sampling may not be realistic in many contexts. Also, models based on intrinsic agency, such as active sampling schemes, may lead to highly biased sampling. So an imminent question is how robust Federated Learning is to biased sampling? In this work\footnote{\url{https://github.com/jiaqian/robustness_of_FL}}, we experimentally investigate two such scenarios. First, we study a centralized classifier aggregated from a collection of local classifiers trained with data having categorical heterogeneity. Second, we study a classifier aggregated from a collection of local classifiers trained by data through active sampling at the edge. We present evidence in both scenarios that Federated Learning is robust to data heterogeneity when local training iterations and communication frequency are appropriately chosen.
ADC: Adversarial attacks against object Detection that evade Context consistency checks

Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples, which are slightly perturbed input images which lead DNNs to make wrong predictions. To protect from such examples, various defense strategies have been proposed. A very recent defense strategy for detecting adversarial examples, that has been shown to be robust to current attacks, is to check for intrinsic context consistencies in the input data, where context refers to various relationships (e.g., object-to-object co-occurrence relationships) in images. In this paper, we show that even context consistency checks can be brittle to properly crafted adversarial examples and to the best of our knowledge, we are the first to do so. Specifically, we propose an adaptive framework to generate examples that subvert such defenses, namely, Adversarial attacks against object Detection that evade Context consistency checks (ADC). In ADC, we formulate a joint optimization problem which has two attack goals, viz., (i) fooling the object detector and (ii) evading the context consistency check system, at the same time. Experiments on both PASCAL VOC and MS COCO datasets show that examples generated with ADC fool the object detector with a success rate of over 85% in most cases, and at the same time evade the recently proposed context consistency checks, with a bypassing rate of over 80% in most cases. Our results suggest that how to robustly model context and check its consistency, is still an open problem.
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects. But, discrete domains are (1) not naturally amenable to gradient-based optimization, and (2) incompatible with deep learning architectures that rely on representations in high-dimensional vector spaces. In this work, we address both difficulties for set functions, which capture many important discrete problems. First, we develop a framework for extending set functions onto low-dimensional continuous domains, where many extensions are naturally defined. Our framework subsumes many well-known extensions as special cases. Second, to avoid undesirable low-dimensional neural network bottlenecks, we convert low-dimensional extensions into representations in high-dimensional spaces, taking inspiration from the success of semidefinite programs for combinatorial optimization. Empirically, we observe benefits of our extensions for unsupervised neural combinatorial optimization, in particular with high-dimensional representations.
STUDENTS’ PERCEPTIONS OF THE ELIMINATION OF ENGLISH SUBJECT IN ELEMENTARY SCHOOLS

&#x0D; Abstract. This study aims to determine the perceptions of students and teachers on the elimination of English subjects at the elementary schools level. This research was conducted because there was still a pros and cons of the placement of English subjects in the 2013 curriculum should be taught at which level. The approach used in this study is descriptive qualitative. The research subjects with purposive sampling were eighth grade students and English teachers. The research on the study was collected through interviews. Research data were analysed using data reduction, data display, and conclusion /verification. The results of this research indicate that students find it difficult to learn English material at the next level. While from the teacher also did not agree that the English language subjects at the elementary school level were abolished because students did not have the basis and complicate students in understanding the material at the next level
Marjinalization Information Literacy of Community Nusa Penida, Klungkung Regency, Bali

The purpose of this paper was to investigate the marginalization of information literacy among the people of Nusa Penida, Klungkung, and Bali.As one of the main tourist destinations that has become increasingly popular in Bali lately, Nusa Penida is required to implement sustainable tourism.To support sustainable tourism in Nusa Penida, a good and healthy level and understanding of information literacy is needed.There has been no research or research conducted before to see and measure how well the information literacy mapping of the community is.This type of research is qualitative research using descriptive method.Mapping of information literacy using Empowering Model 8 (E8).In this study, data was gathered through interviews and literature reviews.The results of the data analysis are expected to provide an overview of the mapping that shows the marginalization of information literacy for the people of Nusa Penida using the Empowering 8 standard reference, which has the ability to identify, explore, select, organize, and create information.(create), presentation ability (present), assessment ability (assess) and application ability (apply) in utilizing information.
Arithmetical completeness theorems for monotonic modal logics

We investigate modal logical aspects of provability predicates $\mathrm{Pr}_T(x)$ satisfying the following condition: $\mathbf{M}$: If $T \vdash \varphi \to \psi$, then $T \vdash \mathrm{Pr}_T(\ulcorner \varphi \urcorner) \to \mathrm{Pr}_T(\ulcorner \psi \urcorner)$. We prove the arithmetical completeness theorems for monotonic modal logics $\mathsf{MN}$, $\mathsf{MN4}$, $\mathsf{MNP}$, $\mathsf{MNP4}$, and $\mathsf{MND}$ with respect to provability predicates satisfying the condition $\mathbf{M}$. That is, we prove that for each logic $L$ of them, there exists a $\Sigma_1$ provability predicate $\mathrm{Pr}_T(x)$ satisfying $\mathbf{M}$ such that the provability logic of $\mathrm{Pr}_T(x)$ is exactly $L$. In particular, the modal formulas $\mathrm{P}$: $\neg \Box \bot$ and $\mathrm{D}$: $\neg (\Box A \land \Box \neg A)$ are not equivalent over non-normal modal logic and correspond to two different formalizations $\neg \mathrm{Pr}_T(\ulcorner 0=1 \urcorner)$ and $\neg \big(\mathrm{Pr}_T(\ulcorner \varphi \urcorner) \land \mathrm{Pr}_T(\ulcorner \neg \varphi \urcorner) \bigr)$ of consistency statements, respectively. Our results separate these formalizations in terms of modal logic.
DAMPAK GAME ONLINE TERHADAP HASIL BELAJAR PADA MATA PELAJARAN SEJARAH DI SMA N 4 KOTA JAMBI TAHUN AJARAN 2021-2022

This study aims to see how big the impact of online games on student learning outcomes in history subjects at SMA N 4 Jambi City Academic Year 2021-2022. In this study using descriptive qualitative research methods with percentage analysis, data collection researchers used interview questionnaires to collect student data and for data collection of students' academic scores using the goggle form through random sampling techniques, by obtaining value data for playing online games by not playing online games, data processing techniques using world percentages, based on the results of research that has been researched for approximately two month, That the number of students who play online games is 80% of the 60 students as respondents and above the KKM as many as 85% of students from 48 respondents who play online games, while those who do not play online games are 15% of 60 respondents . The researcher concludes that students who play online games can be said to be low, that online games have no impact on students' history lessons at SMAN 4 Jambi City and only have an impact of 7% of students on learning outcomes in history subjects.
A General Framework for Evaluating the Role of Electronic Banks in Achieving Sustainable Development According to 2030 Vision

After the United Nations adopted in 2015 the 2030 Agenda to achieve sustainable development, which included 17 goals, which made achieving these goals one of the great challenges facing societies, reducing poverty, protecting the environment, preserving natural resources and achieving a just peace. Before 2030 Based on the foregoing, the purpose of the research was to determine the requirements necessary to enable electronic banks to achieve sustainable development goals in accordance with Egypt’s Vision 2030 and in line with the United Nations 2030 Agenda. The research was applied on the National Bank of Egypt, which includes 500 branches. A questionnaire form was used as a tool to apply the research and delivered to the main bank branch, which in turn distributed it to the bank’s branches spread throughout the country. The research studies the compatibility of the design of the electronic banking system and its applications with the sustainable development goals approved by the United Nations, and determine what are the requirements, whether marketing, technological or operational, to follow the technological development in the field of electronic banking so that the National Bank is a pioneer in this field among its competitors. The banking sector includes 39 banks, and each bank has many branches. 600 questionnaire forms were distributed to the bank's branches by the main branch, and 78 items were excluded, so the return forms became 522 forms. The results of the data obtained through the questionnaire form were analysed and the required requirements were collected and divided into marketing, technological or operational requirements to include the 17 goals in the United Nations 2030 Agenda, taking into account the requirements of customers and employees, which included 17 elements of the system characteristics in order to achieve the 17 SDG goals. In the end, the research recommended re-designing the electronic banking system according to the results obtained from the research and after excluding the features that are not required by customers and employees and replacing them with other characteristics required by them.
Telecom Quantum Network Node via Atom-Nanophotonic Coupling

We propose neutral atoms coupled to telecom nanophotonic cavities as quantum network nodes. Our novel protocol for generating atom-telecom photon entanglement enables a scalable network architecture featuring identical qubits and direct telecom operation.
Penerapan Pola Pengajaran Bahasa dan Sastra Daerah Bali sebagai Penguatan Kecerdasan Afektif, Kognitif dan Psikomotorik Peserta Didik

Application of pattern language teaching and literature area of Bali is seen very important applied in of all potential and existing intelligence in learners. As it known, that every participant has a range of student potential and intelligence who strived to be developed early on so that they can grow and develop optimally. As Bloom's taxonomy refers to, that there are three aspects that should be in students in learning, i.e. aspects of apectif-related behavior, cognitive-related knowledge, intellect and related psychomotor skills. So, these three aspects that influence the development of maximum mental attitude, behavior and abilities of learners, so that the purpose of education can be achieved. Pattern language learning and literature area of Bali is pattern learning based on the application of language and literature area of Bali as the local value and charge implemented in learning. The pattern became a cornerstone of learning with student-centered approach as learners and teachers as educators.
Use of Set Shaping theory in the development of locally testable codes

In developing locally testable codes, information is added to the coded message by creating redundancy in the codewords. In this article, we propose an alternative method in which redundancy is introduced on the message that must be transmitted before its encoding and not in the codewords. This approach exploits the Set Shaping Theory whose goal is the study the bijection functions f(X)=Y that transform a set of strings into a set of equal size made up of strings of greater length. In this way, this type of function transforms the independent variable x into the dependent variable y whose emission probability is conditioned by the previously emitted variables. Thus, if the decoder decodes a symbol associated with a conditional probability equal to zero, we detect an error in the message. If the function f used is the one that minimizes the average information content, we develop a code that can be tested efficiently. In fact, it is observed, in terms of compression, that the greater length of the strings is compensated by the fact of having chosen the strings with less entropy.
Aggregated Pyramid Vision Transformer: Split-transform-merge Strategy for Image Recognition without Convolutions

With the achievements of Transformer in the field of natural language processing, the encoder-decoder and the attention mechanism in Transformer have been applied to computer vision. Recently, in multiple tasks of computer vision (image classification, object detection, semantic segmentation, etc.), state-of-the-art convolutional neural networks have introduced some concepts of Transformer. This proves that Transformer has a good prospect in the field of image recognition. After Vision Transformer was proposed, more and more works began to use self-attention to completely replace the convolutional layer. This work is based on Vision Transformer, combined with the pyramid architecture, using Split-transform-merge to propose the group encoder and name the network architecture Aggregated Pyramid Vision Transformer (APVT). We perform image classification tasks on the CIFAR-10 dataset and object detection tasks on the COCO 2017 dataset. Compared with other network architectures that use Transformer as the backbone, APVT has excellent results while reducing the computational cost. We hope this improved strategy can provide a reference for future Transformer research in computer vision.
Understanding Refactoring Tasks over Time: A Study Using Refactoring Graphs

Refactoring is a fundamental practice in modern software development. Therefore, it is essential that practitioners have a solid understanding of refactoring, both in theory and in practice. In this paper, we rely on a graph-based abstraction — called refactoring graphs — to visualize and analyze refactorings performed by students in a canonical refactoring application: the Video Store System, proposed by Fowler. We asked 46 students to perform the refactorings included in this example under two scenarios: according to a list of explicit guidelines, where most students performed the tasks successfully; and by using flexible ones, in which a part of the students faced problems identifying the appropriate operations. We conclude by presenting discussions and implications.
The punctured codes of two classes of cyclic codes with few weights

<p style='text-indent:20px;'>The puncturing technique is one of important tools for constructing new codes from old ones. In recent years, many classes of linear codes with interesting parameters have been obtained with this technique. Based on quadratic Gauss sums, the puncturing technique and cyclotomic classes, we investigate two classes of punctured codes of cyclic codes over finite fields. The weight distribution and the parameters of the duals of these codes are studied. The parameters of these codes are new.
On identifying significant edges for structure learning in Bayesian networks

Bayesian networks (BNs) are widely used as a data analysis tool in diverse areas, including finance, medicine, and sports.A standard data analysis methodology is to use the well-known score-and-search approach to learn a set of possible Bayesian networks and then to perform model averaging with thresholding to identify features such as edges between variables with high confidence.A fundamental step in the methodology is to select the threshold, as the value selected has broad implications for the success of the analysis.However, the problem of selecting a good threshold in Bayesian network structure learning has received limited attention in the literature.In this paper, we identifying an important shortcoming in a widely used threshold selection method.We then propose a simple transfer learning approach for maximizing target metrics and selecting a threshold that can be generalized from proxy datasets to the target dataset and show on an extensive set of benchmarks that it can perform significantly better than previous approaches.
Efficient Runtime Profiling for Black-box Machine Learning Services on Sensor Streams

In highly distributed environments such as cloud, edge and fog computing, the application of machine learning for automating and optimizing processes is on the rise. Machine learning jobs are frequently applied in streaming conditions, where models are used to analyze data streams originating from e.g. video streams or sensory data. Often the results for particular data samples need to be provided in time before the arrival of next data. Thus, enough resources must be provided to ensure the just-in-time processing for the specific data stream. This paper focuses on proposing a runtime modeling strategy for containerized machine learning jobs, which enables the optimization and adaptive adjustment of resources per job and component. Our black-box approach assembles multiple techniques into an efficient runtime profiling method, while making no assumptions about underlying hardware, data streams, or applied machine learning jobs. The results show that our method is able to capture the general runtime behaviour of different machine learning jobs already after a short profiling phase.