Reviewerly/exHarmony huggingface dataset:
Particle Swarm Optimization Setup for Hyperparameter Selection in Support Vector Machines

Abstract Selecting hyperparameters for support vector machines is an optimization task that affects their classification performance. In particular, particle swarm optimization has been successfully applied for this purpose; however, the literature in the field has not addressed the effect of the population topology and the method for handling constraints in the search space. In this paper, we assessed two population types (von Neumann and fully connected) and four methods for handling constraints in the search space (penalization, dynamic penalization, reflect, and border), for a total of eight different PSO setups. These PSO setups helped us to optimize an SVM using a radial basis function kernel, which means that two hyperparameters were selected: cost parameter ( C ) and kernel width ( γ ). We conducted the experiments over 10 benchmark classification problems and for atrial fibrillation detection. Our findings suggest that using a von Neumann population together with the reflect method provides the SVM with the best classification capabilities and shortest execution time.
Donation of School Supplies as an Effort to Increase the Spirit of Learning for Students Affected by Flood Disasters in Garut City

Disasters are a series of events that can threaten human life in various places, one of which is in Garut Regency which is an area with the Indonesia Disaster Prone Index ranked third in West Java Province, and one of the school facilities affected by the flood disaster in July 2022 is SDN 1 Sukaratu. This community service activity was carried out in July 2022 by providing school supplies for high school students whose homes were affected by flooding. The results of the evaluation, both questions and answers and observations of affected students, can carry out learning activities with their new equipment.
Application of Financial Literature through the Love of Saving Early at Elementary School, Oenesu Village

Socialization activities like saving are carried out by students participating in real work colleges thematic Universitas Katolik Widya Mandira aims to educate and provide an understanding of financial literacy through saving from an early age while increasing participants ' awareness and interest learn to save. Saving means setting aside some of the money you have to save and is one way to manage money, and familiarize participants educate to buy school necessities such as uniforms, stationery, and textbooks, and can help the family's economy from savings. This activity is one of the applications of knowledge carried out at the Nait State Elementary School and the GMIT Oenesu Elementary School which is carried out face-to-face while still adhering to health protocols. The methods used are lectures with extension techniques, using language and fun ways according to their age level, asking questions, and being creative in their work. practice make a piggy bank using used bottles and origami paper. The results of this activity, participants students gain knowledge about the benefits of saving and caring for the environment by utilizing household waste. This activity is expected to be continued by the school and also in the environment community so that these good habits can have a lasting impact for a better future
Technical Assistance in Making Instructional Learning Media for Teachers and Students Mas Dr. H. Abdullah Ahmad PGAI Padang

Utilization of relevant learning media in the classroom can optimize the learning process. For lecturers, media helps concrete concepts or ideas and helps motivate active learning participants. For students, the media can be a bridge for critical thinking and action. Thus the media can help the task of lecturers and students achieve the specified basic competencies. So that learning media can be used properly, teachers need to know their learning needs and the problems faced by students regarding the material to be taught. To improve the teaching and learning process, a teacher should use instructional media as a channel. Through this channel, the stimulus is delivered to students to motivate, attract attention, stimulate student responses and others. For this purpose, it is necessary for the teacher to know the types of media besides being able to choose, it is also possible to combine several media to convey his meaning.
MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation

Large language models have demonstrated the ability to generate both natural language and programming language text. Such models open up the possibility of multi-language code generation: could code generation models generalize knowledge from one language to another? Although contemporary code generation models can generate semantically correct Python code, little is known about their abilities with other languages. We propose MultiPL-E, a system for translating unit test-driven code generation benchmarks to new languages. We create the first massively multilingual code generation benchmark by using MultiPL-E to translate two popular Python code generation benchmarks to 18 additional programming languages. We use MultiPL-E to extend the HumanEval benchmark and MBPP benchmark to 18 languages that encompass a range of programming paradigms and popularity. Using these new parallel benchmarks, we evaluate the multi-language performance of three state-of-the-art code generation models: Codex, CodeGen, and InCoder. We find that Codex matches or even exceeds its performance on Python for several other languages. The range of programming languages represented in MultiPL-E allow us to explore the impact of language frequency and language features on model performance. Finally, the MultiPL-E approach of compiling code generation benchmarks to new programming languages is both scalable and extensible, making it straightforward to evaluate new models, benchmarks, and languages.
Adaptive Recursive Circle Framework for Fine-grained Action Recognition

How to model fine-grained spatial-temporal dynamics in videos has been a challenging problem for action recognition. It requires learning deep and rich features with superior distinctiveness for the subtle and abstract motions. Most existing methods generate features of a layer in a pure feedforward manner, where the information moves in one direction from inputs to outputs. And they rely on stacking more layers to obtain more powerful features, bringing extra non-negligible overheads. In this paper, we propose an Adaptive Recursive Circle (ARC) framework, a fine-grained decorator for pure feedforward layers. It inherits the operators and parameters of the original layer but is slightly different in the use of those operators and parameters. Specifically, the input of the layer is treated as an evolving state, and its update is alternated with the feature generation. At each recursive step, the input state is enriched by the previously generated features and the feature generation is made with the newly updated input state. We hope the ARC framework can facilitate fine-grained action recognition by introducing deeply refined features and multi-scale receptive fields at a low cost. Significant improvements over feedforward baselines are observed on several benchmarks. For example, an ARC-equipped TSM-ResNet18 outperforms TSM-ResNet50 with 48% fewer FLOPs and 52% model parameters on Something-Something V1 and Diving48.
The Polynomial Learning With Errors Problem and the Smearing Condition

As quantum computing advances rapidly, guaranteeing the security of cryptographic protocols resistant to quantum attacks is paramount. Some leading candidate cryptosystems use the Learning with Errors (LWE) problem, attractive for its simplicity and hardness guaranteed by reductions from hard computational lattice problems. Its algebraic variants, Ring-Learning with Errors (RLWE) and Polynomial Learning with Errors (PLWE), gain in efficiency over standard LWE, but their security remains to be thoroughly investigated. In this work, we consider the "smearing" condition, a condition for attacks on PLWE and RLWE introduced in [6]. We expand upon some questions about smearing posed by Elias et al. in [6] and show how smearing is related to the Coupon Collector's Problem Furthermore, we develop some practical algorithms for calculating probabilities related to smearing. Finally, we present a smearing-based attack on PLWE, and demonstrate its effectiveness.
A Fast Text-Driven Approach for Generating Artistic Content

In this work, we propose a complete framework that generates visual art. Unlike previous stylization methods that are not flexible with style parameters (i.e., they allow stylization with only one style image, a single stylization text or stylization of a content image from a certain domain), our method has no such restriction. In addition, we implement an improved version that can generate a wide range of results with varying degrees of detail, style and structure, with a boost in generation speed. To further enhance the results, we insert an artistic super-resolution module in the generative pipeline. This module will bring additional details such as patterns specific to painters, slight brush marks, and so on.
Building Analyses from Syntactic Inference in Local Languages: An HPSG Grammar Inference System

We present a grammar inference system that leverages linguistic knowledge recorded in the form of annotations in interlinear glossed text (IGT) and in a meta-grammar engineering system (the LinGO Grammar Matrix customization system) to automatically produce machine-readable HPSG grammars. Building on prior work to handle the inference of lexical classes, stems, affixes and position classes, and preliminary work on inferring case systems and word order, we introduce an integrated grammar inference system that covers a wide range of fundamental linguistic phenomena. System development was guided by 27 geneologically and geographically diverse languages, and we test the system's cross-linguistic generalizability on an additional 5 held-out languages, using datasets provided by field linguists. Our system out-performs three baseline systems in increasing coverage while limiting ambiguity and producing richer semantic representations, while also producing richer representations than previous work in grammar inference.
Bilevel hyperparameter optimization for support vector classification: theoretical analysis and a solution method

Support vector classification (SVC) is a classical and well-performed learning method for classification problems. A regularization parameter, which significantly affects the classification performance, has to be chosen and this is usually done by the cross-validation procedure. In this paper, we reformulate the hyperparameter selection problem for support vector classification as a bilevel optimization problem in which the upper-level problem minimizes the average number of misclassified data points over all the cross-validation folds, and the lower-level problems are the l1-loss SVC problems, with each one for each fold in T-fold cross-validation. The resulting bilevel optimization model is then converted to a mathematical program with equilibrium constraints (MPEC). To solve this MPEC, we propose a global relaxation cross-validation algorithm (GR-CV) based on the well-know Sholtes-type global relaxation method (GRM). It is proven to converge to a C-stationary point. Moreover, we prove that the MPEC-tailored version of the Mangasarian-Fromovitz constraint qualification (MFCQ), which is a key property to guarantee the convergence of the GRM, automatically holds at each feasible point of this MPEC. Extensive numerical results verify the efficiency of the proposed approach. In particular, compared with other methods, our algorithm enjoys superior generalization performance over almost all the data sets used in this paper.
WRITING SKILLS: CHALLENGES AND SOLUTIONS

Ability to communicate in writing in a foreign language is required for any educated professional in our modern globalized world. It allows people to get a prestigious job, to participate in international projects and exchange programmes, to get education, etc. For that foreign language proficiency level should be higher than the Intermediate one (B2+), including writing skills. It is this level that allows people to live, study or work independently in a country where and when this particular foreign language is used for communication However, the results of examinations aimed at assessing the writing proficiency of Ukrainian school and university students demonstrate that their writing skills are not developed enough and leave much to be desired. Both teachers and learners consider writing to be the most difficult skill of the four main basic skills as it requires control over a number of variables simultaneously, over the content and the form, i.e., to know what to write about and how to write about it. The present article focuses on the specific features of writing which make it challenging, makes an attempt to analyze the writing challenges Ukrainian students face and to present some recommendations about what may be done by teachers of foreign languages to improve the situation.
Spatiotemporal quantum parametric mode sorter

We demonstrate a spatio-temporal quantum parametric mode sorter that can achieve high extinction by selectively upconverting the signal modes with an optimized pump. This could serve as a viable resource for photon-efficient quantum communication and computation.
On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel

Recent Deep Learning (DL) advancements in solving complex real-world tasks have led to its widespread adoption in practical applications. However, this opportunity comes with significant underlying risks, as many of these models rely on privacy-sensitive data for training in a variety of applications, making them an overly-exposed threat surface for privacy violations. Furthermore, the widespread use of cloud-based Machine-Learning-as-a-Service (MLaaS) for its robust infrastructure support has broadened the threat surface to include a variety of remote side-channel attacks. In this paper, we first identify and report a novel data-dependent timing side-channel leakage (termed Class Leakage) in DL implementations originating from non-constant time branching operation in a widely used DL framework PyTorch. We further demonstrate a practical inference-time attack where an adversary with user privilege and hard-label black-box access to an MLaaS can exploit Class Leakage to compromise the privacy of MLaaS users. DL models are vulnerable to Membership Inference Attack (MIA), where an adversary's objective is to deduce whether any particular data has been used while training the model. In this paper, as a separate case study, we demonstrate that a DL model secured with differential privacy (a popular countermeasure against MIA) is still vulnerable to MIA against an adversary exploiting Class Leakage. We develop an easy-to-implement countermeasure by making a constant-time branching operation that alleviates the Class Leakage and also aids in mitigating MIA. We have chosen two standard benchmarking image classification datasets, CIFAR-10 and CIFAR-100 to train five state-of-the-art pre-trained DL models, over two different computing environments having Intel Xeon and Intel i7 processors to validate our approach.
Long-term Causal Effects Estimation via Latent Surrogates Representation Learning

Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications, e.g., marketing and medicine. Despite its success in certain domains, most existing methods estimate causal effects in an idealistic and simplistic way - ignoring the causal structure among short-term outcomes and treating all of them as surrogates. However, such methods cannot be well applied to real-world scenarios, in which the partially observed surrogates are mixed with their proxies among short-term outcomes. To this end, we develop our flexible method, Laser, to estimate long-term causal effects in the more realistic situation that the surrogates are observed or have observed proxies.Given the indistinguishability between the surrogates and proxies, we utilize identifiable variational auto-encoder (iVAE) to recover the whole valid surrogates on all the surrogates candidates without the need of distinguishing the observed surrogates or the proxies of latent surrogates. With the help of the recovered surrogates, we further devise an unbiased estimation of long-term causal effects. Extensive experimental results on the real-world and semi-synthetic datasets demonstrate the effectiveness of our proposed method.
Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms

Central venous catheters (CVC) are commonly inserted into the large veins of the neck, e.g. the internal jugu- lar vein (IJV). CVC insertion may cause serious complications like misplacement into an artery or perforation of cervical vessels. Placing a CVC under sonographic guidance is an appropriate method to reduce such adverse events, if anatomical landmarks like venous and arterial vessels can be detected reliably. This task shall be solved by registration of patient individual images vs. an anatomically labelled reference image. In this work, a linear, affine transformation is performed on cervical sonograms, followed by a non-linear transformation to achieve a more precise registration. Voxelmorph (VM), a learning-based library for deformable image registration using a convolutional neural network (CNN) with U-Net structure was used for non-linear transformation. The impact of principal component analysis (PCA)-based pre-denoising of patient individual images, as well as the impact of modified net structures with differing complexities on registration results were examined visually and quan- titatively, the latter using metrics for deformation and image similarity. Using the PCA-approximated cervical sonograms resulted in decreased mean deformation lengths between 18% and 66% compared to their original image counterparts, depending on net structure. In addition, reducing the number of convolutional layers led to improved image similarity with PCA images, while worsening in original images. Despite a large reduction of network parameters, no overall decrease in registration quality was observed, leading to the conclusion that the original net structure is oversized for the task at hand.
PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images

We present Progressively Deblurring Radiance Field (PDRF), a novel approach to efficiently reconstruct high quality radiance fields from blurry images. While current State-of-The-Art (SoTA) scene reconstruction methods achieve photo-realistic rendering results from clean source views, their performances suffer when the source views are affected by blur, which is commonly observed for images in the wild. Previous deblurring methods either do not account for 3D geometry, or are computationally intense. To addresses these issues, PDRF, a progressively deblurring scheme in radiance field modeling, accurately models blur by incorporating 3D scene context. PDRF further uses an efficient importance sampling scheme, which results in fast scene optimization. Specifically, PDRF proposes a Coarse Ray Renderer to quickly estimate voxel density and feature; a Fine Voxel Renderer is then used to achieve high quality ray tracing. We perform extensive experiments and show that PDRF is 15X faster than previous SoTA while achieving better performance on both synthetic and real scenes.
A Game-Theoretic Perspective of Generalization in Reinforcement Learning

Generalization in reinforcement learning (RL) is of importance for real deployment of RL algorithms. Various schemes are proposed to address the generalization issues, including transfer learning, multi-task learning and meta learning, as well as the robust and adversarial reinforcement learning. However, there is not a unified formulation of the various schemes, as well as the comprehensive comparisons of methods across different schemes. In this work, we propose a game-theoretic framework for the generalization in reinforcement learning, named GiRL, where an RL agent is trained against an adversary over a set of tasks, where the adversary can manipulate the distributions over tasks within a given threshold. With different configurations, GiRL can reduce the various schemes mentioned above. To solve GiRL, we adapt the widely-used method in game theory, policy space response oracle (PSRO) with the following three important modifications: i) we use model-agnostic meta learning (MAML) as the best-response oracle, ii) we propose a modified projected replicated dynamics, i.e., R-PRD, which ensures the computed meta-strategy of the adversary fall in the threshold, and iii) we also propose a protocol for the few-shot learning of the multiple strategies during testing. Extensive experiments on MuJoCo environments demonstrate that our proposed methods can outperform existing baselines, e.g., MAML.
AnonySIGN: Novel Human Appearance Synthesis for Sign Language Video Anonymisation

The visual anonymisation of sign language data is an essential task to address privacy concerns raised by large-scale dataset collection. Previous anonymisation techniques have either significantly affected sign comprehension or required manual, labour-intensive work. In this paper, we formally introduce the task of Sign Language Video Anonymisation (SLVA) as an automatic method to anonymise the visual appearance of a sign language video whilst retaining the meaning of the original sign language sequence. To tackle SLVA, we propose AnonySign, a novel automatic approach for visual anonymisation of sign language data. We first extract pose information from the source video to remove the original signer appearance. We next generate a photo-realistic sign language video of a novel appearance from the pose sequence, using image-to-image translation methods in a conditional variational autoencoder framework. An approximate posterior style distribution is learnt, which can be sampled from to synthesise novel human appearances. In addition, we propose a novel \textit{style loss} that ensures style consistency in the anonymised sign language videos. We evaluate AnonySign for the SLVA task with extensive quantitative and qualitative experiments highlighting both realism and anonymity of our novel human appearance synthesis. In addition, we formalise an anonymity perceptual study as an evaluation criteria for the SLVA task and showcase that video anonymisation using AnonySign retains the original sign language content.
A Generalization of Array Codes with Local Properties and Efficient Encoding/Decoding

A maximum distance separable (MDS) array code is composed of $m\times (k+r)$ arrays such that any $k$ out of $k+r$ columns suffice to retrieve all the information symbols. Expanded-Blaum-Roth (EBR) codes and Expanded-Independent-Parity (EIP) codes are two classes of MDS array codes that can repair any one symbol in a column by locally accessing some other symbols within the column, where the number of symbols $m$ in a column is a prime number. By generalizing the constructions of EBR and EIP codes, we propose new MDS array codes, such that any one symbol can be locally recovered and the number of symbols in a column can be not only a prime number but also a power of an odd prime number. Also, we present an efficient encoding/decoding method for the proposed generalized EBR (GEBR) and generalized EIP (GEIP) codes based on the LU factorization of a Vandermonde matrix. We show that the proposed decoding method has less computational complexity than existing methods. Furthermore, we show that the proposed GEBR codes have both a larger minimum symbol distance and a larger recovery ability of erased lines for some parameters when compared to EBR codes. We show that EBR codes can recover any $r$ erased lines of a slope for any parameter $r$, which was an open problem in [2].