Reviewerly/exHarmony huggingface dataset:
On the Cryptographic Hardness of Learning Single Periodic Neurons

Abstract We show a simple reduction which demonstrates the cryptographic hardness of learning a single periodic neuron over isotropic Gaussian distributions in the presence of noise. More precisely, our reduction shows that any polynomial-time algorithm (not necessarily gradientbased) for learning such functions under small noise implies a polynomial-time quantum algorithm for solving worst-case lattice problems, whose hardness form the foundation of lattice-based cryptography. Our core hard family of functions, which are well-approximated by one-layer neural networks, take the general form of a univariate periodic function applied to an affine projection of the data. These functions have appeared in previous seminal works which demonstrate their hardness against gradient-based (Shamir’18), and Statistical Query (SQ) algorithms (Song et al.’17). We show that if (polynomially) small noise is added to the labels, the intractability of learning these functions applies to all polynomial-time algorithms, beyond gradient-based and SQ algorithms, under the aforementioned cryptographic assumptions. Moreover, we demonstrate the necessity of noise in the hardness result by designing a polynomial-time algorithm for learning certain families of such functions under exponentially small adversarial noise. Our proposed algorithm is not a gradient-based or an SQ algorithm, but is rather based on the celebrated Lenstra-Lenstra-Lovasz (LLL) lattice basis reduction algorithm. Furthermore, in the absence of noise, this algorithm can be directly applied to solve CLWE detection (Bruna et al.’21) and phase retrieval with an optimal sample complexity of d + 1 samples. In the former case, this improves upon the quadratic-in-d sample complexity required in (Bruna et al.’21).
A mean-field approximation based BP algorithm for solving the stochastic block model

As a mainstream algorithm for inferring probabilistic graphical models, belief propagation (BP) algorithm is one of the most important methods to solve the joint probability distribution in the stochastic block model. However, existing methods either lead to low accuracy in dealing with the core-periphery structure problem, or the theoretical derivation is difficult to understand due to a large number of approximation, or both exist. Of course, the reason for low accuracy comes from too many approximations. The main reason for many approximations and complex theoretical derivation is that the joint probability distribution in the inference process of the stochastic block model is not directly solved by the BP algorithm, that is, the graph (network) being processed is not consistent with the graph considered in the probabilistic graph model. Therefore, in this paper, a mean-field approximation is developed to modify the joint probability distribution to make the BP algorithm match perfectly, which makes the theoretical derivation easy to understand. Finally, the effectiveness of the proposed method is validated by the experimental results.
Analysis of Grammar Part for Chinese Language Teaching: Focusing on the Existential Sentences and Investigation ‘le’ of Elementary Chinese Textbooks

Scholars who studied education have considered the three basic elements of education: the instructor, the learner, and the textbook. A successful education requires good teaching materials as well as good instructors. In other words, instructors and textbooks directly affect the effectiveness of education. Above all, textbooks are a main material that provides the basic framework of learning and are an important factor that determines the success of lesson. The textbooks on the market reflect the situation of Chinese education most realistically. So if we analyze it, we can grasp the reality of Chinese education. It can also be a starting point for discussing education. This thesis investigated the contents related to the Chinese elementary education used in Korean universities. As a result, 『Master Chinese』, 『Spoken Chinese』 and 『The Chinese』 were selected as the study subjects. Is the knowledge of Chinese textbooks which are widely used in universities effectively communicated to learners? Based on this problem, this paper comprehensively analyzed the selection, arrangement, and explanation of grammatical items. Based on the results, this thesis suggests how to carry out it in detail and an effective method of teaching Chinese grammar for learners. First, when selecting grammar items, the beginner level focuses on vocabulary and phrases so that learners can easily feel the language elements of grammar. Second, when arranging grammar items, the proportion and difficulty of grammar should be considered first If the proportion of grammar is not small, it should not be concentrated in one lesson. It should be designed in stages and sequentially by dividing it into several lessons. Less difficult sentences should be learned first so that efficient learning can be achieved. Third, when describing a grammar item, it should be formulated so that can be intuitively knew about the grammar structure. Along with sufficient examples, it shall be designed to accurately understand by the usage of each sentence with an appropriate explanation of Korean and Chinese sentences. I hope that this study will help to develop Chinese education in a more ideal and successful direction. And also hope to be a useful reference for the compilation of Chinese textbooks.
Teaching an Active Learner with Contrastive Examples

We study the problem of active learning with the added twist that the learner is assisted by a helpful teacher. We consider the following natural interaction protocol: At each round, the learner proposes a query asking for the label of an instance $x^q$, the teacher provides the requested label $\{x^q, y^q\}$ along with explanatory information to guide the learning process. In this paper, we view this information in the form of an additional contrastive example ($\{x^c, y^c\}$) where $x^c$ is picked from a set constrained by $x^q$ (e.g., dissimilar instances with the same label). Our focus is to design a teaching algorithm that can provide an informative sequence of contrastive examples to the learner to speed up the learning process. We show that this leads to a challenging sequence optimization problem where the algorithm's choices at a given round depend on the history of interactions. We investigate an efficient teaching algorithm that adaptively picks these contrastive examples. We derive strong performance guarantees for our algorithm based on two problem-dependent parameters and further show that for specific types of active learners (e.g., a generalized binary search learner), the proposed teaching algorithm exhibits strong approximation guarantees. Finally, we illustrate our bounds and demonstrate the effectiveness of our teaching framework via two numerical case studies.
RAANet: Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Density Level Estimation

3D object detection from LiDAR data for autonomous driving has been making remarkable strides in recent years. Among the state-of-the-art methodologies, encoding point clouds into a bird's-eye view (BEV) has been demonstrated to be both effective and efficient. Different from perspective views, BEV preserves rich spatial and distance information between objects; and while farther objects of the same type do not appear smaller in the BEV, they contain sparser point cloud features. This fact weakens BEV feature extraction using shared-weight convolutional neural networks. In order to address this challenge, we propose Range-Aware Attention Network (RAANet), which extracts more powerful BEV features and generates superior 3D object detections. The range-aware attention (RAA) convolutions significantly improve feature extraction for near as well as far objects. Moreover, we propose a novel auxiliary loss for density estimation to further enhance the detection accuracy of RAANet for occluded objects. It is worth to note that our proposed RAA convolution is lightweight and compatible to be integrated into any CNN architecture used for the BEV detection. Extensive experiments on the nuScenes dataset demonstrate that our proposed approach outperforms the state-of-the-art methods for LiDAR-based 3D object detection, with real-time inference speed of 16 Hz for the full version and 22 Hz for the lite version. The code is publicly available at an anonymous Github repository this https URL
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations

Contrastive self-supervised learning has outperformed supervised pretraining on many downstream tasks like segmentation and object detection. However, current methods are still primarily applied to curated datasets like ImageNet. In this paper, we first study how biases in the dataset affect existing methods. Our results show that current contrastive approaches work surprisingly well across: (i) object- versus scene-centric, (ii) uniform versus long-tailed and (iii) general versus domain-specific datasets. Second, given the generality of the approach, we try to realize further gains with minor modifications. We show that learning additional invariances -- through the use of multi-scale cropping, stronger augmentations and nearest neighbors -- improves the representations. Finally, we observe that MoCo learns spatially structured representations when trained with a multi-crop strategy. The representations can be used for semantic segment retrieval and video instance segmentation without finetuning. Moreover, the results are on par with specialized models. We hope this work will serve as a useful study for other researchers. The code and models will be available at this https URL.
Competition of Scientific Professional-Oriented Reports as a Tool For Development Motivation to Study the English Language Among Students of a Non-Linguistic University

The problem of holding scientific conferences on professional topics for non-language students is being updated in English for professional development and motivation to learn a foreign language. The purpose of the article is to analyze the achievements and difficulties of making a research and speaking at a scientific conference. Methodology. In the course of the study, methods such as analysis of domestic and foreign literature, the method of interviews, discussions for feedback, and generalization of pedagogical experience were used. Results. Having analyzed the literature on the topic of our research, as well as the experience of organizing and preparing students for a scientific conference, it can be stated that the scientific conference is important for students to create a more effective motivation for professional development and learning English, without which the professional outlook narrows. The role and importance of individual work, work in groups and consultations with the teacher are shown. Based on the results of scientific conferences, questionnaires and discussions with participants, the strengths of scientific conferences and the process of preparing for them are highlighted. The difficulties faced by students in research work are indicated and the conditions under which it is possible to overcome them for successful preparation are argued. Based on the results of the research, priorities are identified and proposed, which are determined by us as key for use in preparation for a scientific conference.
Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Motivated by the recent successes of neural networks that have the ability to fit the data perfectly and generalize well, we study the noiseless model in the fundamental least-squares setup. We assume that an optimum predictor fits perfectly inputs and outputs $\langle \theta_* , \phi(X) \rangle = Y$, where $\phi(X)$ stands for a possibly infinite dimensional non-linear feature map. To solve this problem, we consider the estimator given by the last iterate of stochastic gradient descent (SGD) with constant step-size. In this context, our contribution is two fold: (i) from a (stochastic) optimization perspective, we exhibit an archetypal problem where we can show explicitly the convergence of SGD final iterate for a non-strongly convex problem with constant step-size whereas usual results use some form of average and (ii) from a statistical perspective, we give explicit non-asymptotic convergence rates in the over-parameterized setting and leverage a fine-grained parameterization of the problem to exhibit polynomial rates that can be faster than $O(1/T)$. The link with reproducing kernel Hilbert spaces is established.
Avaliação da aplicabilidade de padrões para o desenvolvimento de aplicações móveis.

In the mobile application domain, the Android Platform does not impose an architecturalpattern. Hence, the choice of architecture and the quality of the application depend on thedeveloper’s experience. In this work an application for university restaurant managementwas developed using three architectural patterns: Passive Model-View-Controller (MVC),Model-View-Presenter (MVP) and Model-View-ViewModel (MVVM). In order to evaluatethe applicability of the patterns, we used the Chidamber e Kemerer (CK) metrics thatverified the superiority of the quality of the application developed in the Model-View-ViewModel (MVVM) architectural pattern.
Generalized Proximal Policy Optimization with Sample Reuse

In real-world decision making tasks, it is critical for data-driven reinforcement learning methods to be both stable and sample efficient. On-policy methods typically generate reliable policy improvement throughout training, while off-policy methods make more efficient use of data through sample reuse. In this work, we combine the theoretically supported stability benefits of on-policy algorithms with the sample efficiency of off-policy algorithms. We develop policy improvement guarantees that are suitable for the off-policy setting, and connect these bounds to the clipping mechanism used in Proximal Policy Optimization. This motivates an off-policy version of the popular algorithm that we call Generalized Proximal Policy Optimization with Sample Reuse. We demonstrate both theoretically and empirically that our algorithm delivers improved performance by effectively balancing the competing goals of stability and sample efficiency.
Image classification optimization models using the convolutional neural network ( CNN ) approach and embedded deep learning system

Deep learning has progressed rapidly in recent years and has been applied in many fields, which are the main fields of artificial intelligence. Traditional methods of machine learning most use shallow structures to deal with a limited number of samples and computational units. When the target objects have rich meanings, the performance and ability to generalize complex classification problems will be quite inadequate. The convolutional neural network (CNN), which has been developed in recent years, widely used in image processing; because it has high skills in dealing with image classification and image recognition issues and it has led to great care in many machine learning tasks and it has become a powerful and universal model of deep learning. The combination of deep learning and embedded systems has created good technical dimensions. In this paper, several useful models in the field of image classification optimization, based on convolutional neural network and embedded systems, are discussed. Since this paper focuses on usable models on the FPGA board, models known for embedded systems such as MobileNet, ResNet, ResNeXt and ShuffNet have been studied.
Integración de la computación física bajo el aprendizaje construccionista para el fortalecimiento de las competencias tecnológicas de los docentes del TecNM campus Minatitlán

espanolEl presente trabajo de investigacion fue antes de la pandemia COVID-19 tuvo como objetivo valorar las posibilidades de aplicacion y de integracion de la computacion fisica, mediante la programacion de sensores y actuadores para contribuir con el fortalecimiento de conocimientos, competencias y uso de nuevas herramientas tecnologicas para los docentes de la carrera de Ingenieria Electronica del Tecnologico Nacional de Mexico campus Minatitlan, aplicando la teoria construccionista y la herramienta tecnologica de Arduino Uno, se disenaron practicas, y se aplico la prueba de conceptos mediante un taller presencial. Aplicando al final dos encuesta. En esta investigacion, se concluye que la Computacion fisica con Arduino Uno bajo el enfoque construccionista, fortalece diversas competencias y conocimientos relacionados a la programacion, pero tambien se desarrollan habilidades tangibles como las de disenar, conectar, experimentar y construir, asi como se favorecen habilidades suaves tal como la colaboracion, el pensamiento computacional, la creatividad, comunicacion y la resolucion de problemas. EnglishThe present research work was carried out before the COVID-19 pandemic and its objective was to assess the possibilities of application and integration of physical computing, through the programming of sensors and actuators to contribute to the strengthening of knowledge, skills and use of new tools. technologies for teachers of the Electronic Engineering career of the National Technology of Mexico Minatitlan campus, applying the constructionist theory and the Arduino Uno technological tool, practices were designed, and the proof of concepts was applied through a face-to-face workshop. Applying at the end two surveys. From this research, it is concluded that Physical Computing with Arduino Uno under the constructionist approach, strengthens various skills and knowledge related to programming, but also develops tangible skills such as designing, connecting, experimenting and building, as well as skills are favored such as collaboration, computational thinking, creativity, communication, and problem solving.
Controlled Text Generation as Continuous Optimization with Multiple Constraints

As large-scale language model pretraining pushes the state-of-the-art in text generation, recent work has turned to controlling attributes of the text such models generate. While modifying the pretrained models via fine-tuning remains the popular approach, it incurs a significant computational cost and can be infeasible due to lack of appropriate data. As an alternative, we propose MuCoCO -- a flexible and modular algorithm for controllable inference from pretrained models. We formulate the decoding process as an optimization problem which allows for multiple attributes we aim to control to be easily incorporated as differentiable constraints to the optimization. By relaxing this discrete optimization to a continuous one, we make use of Lagrangian multipliers and gradient-descent based techniques to generate the desired text. We evaluate our approach on controllable machine translation and style transfer with multiple sentence-level attributes and observe significant improvements over baselines.
АВТОМАТИЧЕСКОЕ РАСПОЗНАВАНИЕ КАЗАХСКОЙ РЕЧИ С ИСПОЛЬЗОВАНИЕМ DNN

This paper describes one of the areas in the field of artificial intelligence speech recognition systems. Comparing the speeches o f Kazakh and other languages, they identified the main problems of automatic recognition of this language. One of the main problems is the lack of speech data, for which work was carried out to collect acoustic data of the Kazakh language. In order to continue the research work related to the Kazakh language, the personal data of the announcers were identified. Algorithms for processing speech signals, learning acoustic and language modeling are described and research and practical work is carried out. Test results of speech recognition using deep neural networks were obtained. Comparisons with the results of traditional models and the best DNN (Deep Neural Network) aspects.
ProTo: Program-Guided Transformer for Program-Guided Tasks

Programs, consisting of semantic and structural information, play an important role in the communication between humans and agents. Towards learning general program executors to unify perception, reasoning, and decision making, we formulate program-guided tasks which require learning to execute a given program on the observed task specification. Furthermore, we propose the Program-guided Transformer (ProTo), which integrates both semantic and structural guidance of a program by leveraging cross-attention and masked self-attention to pass messages between the specification and routines in the program. ProTo executes a program in a learned latent space and enjoys stronger representation ability than previous neural-symbolic approaches. We demonstrate that ProTo significantly outperforms the previous state-of-the-art methods on GQA visual reasoning and 2D Minecraft policy learning datasets. Additionally, ProTo demonstrates better generalization to unseen, complex, and human-written programs.
IMU-Assisted Learning of Single-View Rolling Shutter Correction

Rolling shutter distortion is highly undesirable for photography and computer vision algorithms (e.g., visual SLAM) because pixels can be potentially captured at different times and poses. In this paper, we propose a deep neural network to predict depth and row-wise pose from a single image for rolling shutter correction. Our contribution in this work is to incorporate inertial measurement unit (IMU) data into the pose refinement process, which, compared to the state-of-the-art, greatly enhances the pose prediction. The improved accuracy and robustness make it possible for numerous vision algorithms to use imagery captured by rolling shutter cameras and produce highly accurate results. We also extend a dataset to have real rolling shutter images, IMU data, depth maps, camera poses, and corresponding global shutter images for rolling shutter correction training. We demonstrate the efficacy of the proposed method by evaluating the performance of Direct Sparse Odometry (DSO) algorithm on rolling shutter imagery corrected using the proposed approach. Results show marked improvements of the DSO algorithm over using uncorrected imagery, validating the proposed approach.
A Security Model for Cloud-computing-based E-governance Applications

New communication trends can make a noteworthy contribution in achieving the goal of good governance. E-governance-based applications require computing models for maintaining and executing the data generated from applications. High performance computation technologies like cluster computing, grid computing, and cloud computing can provide effective services to the user at a reduced on cost. For providing the services to users, advanced security features need to be incorporated in these technologies. Therefore, it is necessary to focus on parameters that affect the security of data. In the cloud environment, the real-time output of the execution of e-governance applications depends on various factors such as the time of applying security measures, firewalls, intrusion detection and intrusion prevention systems, encryption, deciding the priority of jobs (filtering high priority jobs), and the execution time on virtual machines (VMs). This chapter addresses the problem of the secured execution of e-governance jobs in the cloud computing environment along with various factors concerning the security of the cloud and also proposes a security architecture. This architecture involves three layers, where the first layer describes the security aspects of user jobs, the second layer decides the priority of user jobs (filtering out e-governance jobs from other jobs in the cloud), and a third layer is responsible for the execution of sorted jobs on VMs. In this study, the overall execution time of the model is determined. The execution time for the first layer depends upon the processing time of the firewall; the time for the second layer depends upon the number of parameters taken for comparing the tasks; in the third layer the three shortest job first and the first come first serve policy is applied and the time of execution is obtained.