Reviewerly/exHarmony huggingface dataset:
Meta-Learning with Adaptive Hyperparameters.

Despite its popularity, several recent works question the effectiveness of MAML when test tasks are different from training tasks, thus suggesting various task-conditioned methodology to improve the initialization. Instead of searching for better task-aware initialization, we focus on a complementary factor in MAML framework, inner-loop optimization (or fast adaptation). Consequently, we propose a new weight update rule that greatly enhances the fast adaptation process. Specifically, we introduce a small meta-network that can adaptively generate per-step hyperparameters: learning rate and weight decay coefficients. The experimental results validate that the Adaptive Learning of hyperparameters for Fast Adaptation (ALFA) is the equally important ingredient that was often neglected in the recent few-shot learning approaches. Surprisingly, fast adaptation from random initialization with ALFA can already outperform MAML.
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments

For mental disorders, patients' underlying mental states are non-observed latent constructs which have to be inferred from observed multi-domain measurements such as diagnostic symptoms and patient functioning scores. Additionally, substantial heterogeneity in the disease diagnosis between patients needs to be addressed for optimizing individualized treatment policy in order to achieve precision medicine. To address these challenges, we propose an integrated learning framework that can simultaneously learn patients' underlying mental states and recommend optimal treatments for each individual. This learning framework is based on the measurement theory in psychiatry for modeling multiple disease diagnostic measures as arising from the underlying causes (true mental states). It allows incorporation of the multivariate pre- and post-treatment outcomes as well as biological measures while preserving the invariant structure for representing patients' latent mental states. A multi-layer neural network is used to allow complex treatment effect heterogeneity. Optimal treatment policy can be inferred for future patients by comparing their potential mental states under different treatments given the observed multi-domain pre-treatment measurements. Experiments on simulated data and a real-world clinical trial data show that the learned treatment polices compare favorably to alternative methods on heterogeneous treatment effects, and have broad utilities which lead to better patient outcomes on multiple domains.
The Effect of e-Service Quality on e-Satisfaction: A Study in the Context of Online Learning during the Covid-19 Pandemic

During the Covid-19 pandemic, almost all universities conducted distance learning. Student responses to the implementation of online lectures need attention. Research on e-service quality on e-satisfaction in the context of pursuit is still very limited. The purpose of this study was to examine the effect of e-service quality on e-satisfaction in online learning during the Covid-19 pandemic. The study was conducted on 1,212 students who had participated in online learning by distributing online questionnaires. The analysis tools used are SPSS and WarpPLS. The results of this study indicate that e-service quality has a significant positive effect on e-satisfaction. This finding is useful for universities as evaluation material in order to improve the online learning process.
Statistical Error Analysis of Machine Translation: The Case of Arabic

In this paper, we present a study of an automatic error analysis in the context of machine translation into Arabic. We have created a pipeline tool allowing evaluation of machine translation outputs and identification of errors. A statistical analysis based on cumulative link models is performed also in order to have a global overview about errors of statistical machine translation from English to Arabic, and to investigate the relationship between encountered errors and the human perception of machine translation quality. As expected, this analysis demonstrates that the impact of lexical, semantic and reordering errors is more significant thanother errors related to the fluency of the machine translation outputs.
Machine Learning for Modal Analysis

We train autoencoder artificial neural networks on simulated multimodal images and their corresponding modal power coefficients to reconstruct the component modal profiles and obtain a network capable of modal decomposition.
Uncertainty Modeling of a Group Tourism Recommendation System Based on Pearson Similarity Criteria, Bayesian Network and Self-Organizing Map Clustering Algorithm

Uncertainty Modeling of a Group Tourism Recommendation System Based on Pearson Similarity Criteria, Bayesian Network and Self-Organizing Map Clustering Algorithm
Segmentation of Visual Images by Sequential Extracting Homogeneous Texture Areas

The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images.The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images.At first, an initial seed point is found for the largest and most homogeneous segment of the image.This initial seed point of the segment is expanded using a region growing method.Other texture segments of the image are extracted analogously in turn.At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed.Then, the detected texture segments are input to a neural network with competitive layers which accomplishes more accurate delineation of the shapes of the extracted texture segments.The proposed segmentation procedure is fully unsupervised, i.e., it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image.The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.
Blockchain-based Website Solution for Controlling the Authorized Sale of Drugs in Peru

Drug counterfeiting or adulteration is a worldwide concern due to the serious consequences they generate, especially in the health and economic sectors. This concern is greater in Peru, as it is among the top five countries with drug counterfeiting incidents in the Americas, according to a study carried out in 2018 by the Pan American Health Organization. In this paper, we present our project, which aims at implementing a technological solution that provides reliable information on the origin and authenticity of these products in Peru to the drug consumer user, preserving the security and integrity of the exposed information using Blockchain technology. Likewise, it allows showing detailed drug characteristics, such as: composition, pharmaceutical form, active ingredients, among other relevant information. The technological solution, proposed by our project, aims at publishing the commercial origin of drugs from their sale in laboratories and distributors to the sale to the public in pharmacies. In the development of this paper, a bibliographic review of research on the use of blockchain technology is presented, as well as its benefits in the health sector, the architecture used by the system and the conceptual commercialization chain that supports it, and the qualitative and quantitative validation for the drug query service is shown.
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation

Panoptic segmentation is posed as a new popular test-bed for the state-of-the-art holistic scene understanding methods with the requirement of simultaneously segmenting both foreground things and background stuff. The state-of-the-art panoptic segmentation network exhibits high structural complexity in different network components, i.e. backbone, proposal-based foreground branch, segmentation-based background branch, and feature fusion module across branches, which heavily relies on expert knowledge and tedious trials. In this work, we propose an efficient, cooperative and highly automated framework to simultaneously search for all main components including backbone, segmentation branches, and feature fusion module in a unified panoptic segmentation pipeline based on the prevailing one-shot Network Architecture Search (NAS) paradigm. Notably, we extend the common single-task NAS into the multi-component scenario by taking the advantage of the newly proposed intra-modular search space and problem-oriented inter-modular search space, which helps us to obtain an optimal network architecture that not only performs well in both instance segmentation and semantic segmentation tasks but also be aware of the reciprocal relations between foreground things and background stuff classes. To relieve the vast computation burden incurred by applying NAS to complicated network architectures, we present a novel path-priority greedy search policy to find a robust, transferrable architecture with significantly reduced searching overhead. Our searched architecture, namely Auto-Panoptic, achieves the new state-of-the-art on the challenging COCO and ADE20K benchmarks. Moreover, extensive experiments are conducted to demonstrate the effectiveness of path-priority policy and transferability of Auto-Panoptic across different datasets. Codes and models are available at: this https URL.
Kesalahan Berbahasa Pada Teks Berita Covid-19 Di Media Daring CNN Indonesia

Online news is the news that is widely accessed in this digital era, because it is faster, more accurate, and flexible. At the beginning of 2020, the whole world is facing the Corona or Covid-19 virus. In cyberspace itself, many sites have the Corona theme as their main news. However, it is not uncommon for language errors to be found in online news, both errors during the typing process, as well as pure errors from the personal error of the author who is not careful in using language rules. This study aims to determine how many types of language errors are in CNN Indonesia's online news about Covid-19 for the March 2020 period. This type of research is a qualitative descriptive study. Data collection techniques in this study used the observation and note technique. Data analysis techniques were carried out starting from the beginning in the form of collection, recording, and classification. Based on the results of this study, four areas of language error were found, namely: (1) spelling (writing italics), (2) morphology (affix me (N) and prefix meN-), (3) syntax (illogical sentences, and non-logical sentences. frugal), (4) diction (connotative meaning and non-standard words). With this research, so that in the future the news presented can provide knowledge to readers by writing well and correctly
Strong Stationary Times and its use in Cryptography

This paper presents applicability of Strong Stationary Times (SST) techniques in the area of cryptography. The applicability is in three areas: (1) Propositions of a new class of cryptographic algorithms (pseudo-random permutation generators) which do not run for the predefined number of steps. Instead, these algorithms stop according to a stopping rule defined as SST, for which one can obtain provable properties: a) results are perfect samples from uniform distribution, b) immunity to timing attacks (no information about the resulting permutation leaks through the information about the number of steps SST algorithm performed). (2) We show how one can leverage properties of SST-based algorithms to construct an implementation (of a symmetric encryption scheme) which is immune to the timing-attack by reusing implementations which are not secure against timing-attacks. In symmetric key cryptography researchers mainly focus on constant time (re)implementations. Our approach goes in a different direction and explores ideas of input masking. (3) Analysis of idealized (mathematical) models of existing cryptographic schemes-i.e., we improve a result by Mironov [21].
Students’ Perceptions on Collaborative Speaking Tasks in ESL Classrooms

Speaking is important in learning and teaching of a second language.This paper aims to investigate students' perceptions on collaborative speaking tasks in ESL classrooms by conducting pair work speaking activities.This survey was conducted with 100 Form Three students in a secondary school in Sentul Zone, Federal Territory of Kuala Lumpur.Pair work speaking activities were conducted for five weeks in English language classes in the school, to explore students' views on collaborative speaking tasks.All the students were asked to answer a five-point Likert Scale Questionnaire via Google Form, as the instrument to collect the data for this research.The data was analysed based on two research questions: RQ1: What are the students' perceptions on collaborative speaking tasks?And RQ2: Is there a significant difference between males and females with the respect to their perceptions on the collaborative speaking tasks?The data was analysed by frequency for RQ1 and t-test for RQ2.The findings revealed that the majority of the students prefer pair work speaking tasks as an effective technique to encourage them to speak confidently during their speaking lessons.The positive feedback of their perceptions showed that they can enhance their speaking skill with their peers and the role of their language teachers is only minimal.This study would provide some ideas for teachers to conduct speaking lessons referring to the findings obtained from the students' feedback.
Image and File System Support Framework for a Digital Mobile Forensics Software

Mobile applications are today ubiquitous, and everybody uses them on a daily basis. This applies also to security-critical mobile applications. In today's architectures, these mobile applications are usually used from the same source as mobile applications on smart phones. There are plenty of information inside those devices like call data records, SMS messages, web history and so on. This makes mobile digital forensics inevitable for them. To assess the strength of mobile digital forensics tools, there are many approaches based on acquiring lots of mobile artifacts. But there is almost no study that measures the ability for a digital forensic tool that gives insight about supported image types and file systems. In this study, we developed a framework that investigates the possible image and file system types that a forensic tool should have support and then we made a case study using two different open source forensic tools.
On Fermionic walkers interacting with a correlated structured environment

We study the large-time behaviour of a sample $$\mathcal {S}$$ consisting of an ensemble of fermionic walkers on a graph interacting with a structured infinite reservoir of fermions $$\mathcal {E}$$ through an exchange of particles in preferred states. We describe the asymptotic state of $$\mathcal {S}$$ in terms the initial state of $$\mathcal {E}$$, with especially simple formulae in the limit of small coupling strength. We also study the particle fluxes into the different parts of the reservoir.
Learning Spatial Relations with a Standard Convolutional Neural Network

This paper shows how a standard convolutional neural network (CNN) without recurrent connections is able to learn general spatial relationships between different objects in an image.A dataset was constructed by placing objects from the Fashion-MNIST dataset onto a larger canvas in various relational locations (for example, trousers left of a shirt, both above a bag).CNNs were trained to name the objects and their spatial relationship.Models were trained to perform two different types of task.The first was to name the objects and their relationships and the second was to answer relational questions such as "Where is the shoe in relation to the bag?".The models performed at above 80% accuracy on test data.The models were also capable of generalising to spatial combinations that had been intentionally excluded from the training data.
Performance of biofoam polyurethane-urea/rice straw waste (Oryza Sativa) as a noise reducer

Noise is one of the common problems found in the metal processing industry. The noise that continuously occurs can influence the health of workers. The large number of workers who do not use Personal Protective Equipment (PPE) and pay less attention to work safety, resulting in them being exposed to high noise, for example, parts of production units that have an average noise intensity of 102.9 dBA/8 hours. The need of noise reduction materials for the metal processing industry is still very potent. Utilization of eco-friendly noise reduction materials in a wide frequency range cannot be fulfilled by the existing materials. This research aims to synthesize polyurethane-urea/rice straw waste (PUU/RSW) foam and to measure its performance as a noise reducer. Rice straw waste is used as an additive in a mixture of polyurethane-urea foam. The one-step method is used for the synthesis of PUU/RSW biofoam using with the composition of maleic anhydric (MAH) 0.1% (w/w), ethylene diamine (EDA) 1% (w/w), variations of polyethylene glycol/methylene diamine (PEG/MDI) (1/1), and rice straw waste with the variations from 0, 1, 2, 3, 4, to 5% (w/w). The characterization of biofoam structures was carried out using FTIR. The effectiveness of the foam in sound absorption is tested by using the two-microphone technique according to the ASTM E-1050 procedure. The results show that physically biofoam products are medium-textured (soft to hard), porous and brown with chemical groups identified C-H, C-O, O-H. Noise absorption test results show that PUU/RSW biofoam is effective as a noise-reducing material as it has a high absorption coefficient in the wide frequency range.
Semantic Instance Segmentation in a 3D Traffic Scene Reconstruction task

We research into a 3D Traffic Scene Reconstruction (3DTSR) task. 3DTSR aims to reconstruct a 3D traffic scene from video footage captured from a car's dash-camera. The 3D traffic scene provides a new platform for various services to exploit, for example, self-driving cars, driving behavior analysis, and traffic accident analysis. In our approach, we resort to a passive sensing approach which detects objects and their positions based on visual information. Spatial positions of objects in a 2D scene are lifted into a 3D scene based on information from multi-sources: (i) semantic instance segmentation, (ii) spatial position and volume estimation through orthogonal images, and (iii) prior knowledge concerning shape and volume of objects. In this paper, we focus on semantic instance segmentation, the first phase of the proposed 3DTSR method. The semantic instance segmentation task is accomplished with the Mask R-CNN model pre-trained on COCO dataset. We report the performances of the semantic segmentation task from different Detectron2 models undergone the transfer learning process using information from various datasets. We show that it is feasible to obtain the shape and appearance of objects in the road scene using our proposed segmentation process.
DISTRIBUTION OF HEAVY METALS IN THE TOPSOIL OF AGRICULTURAL LAND IN NAM DINH PROVINCE, VIETNAM

Surface soil samples were collected from different land use types of the agricultural land in Nam Dinh province, Vietnam to determine the level of heavy metal contamination and identifying potential sources.The farming areas of rice and cereal show similar heavy metal contents, however, the index shows that the content of heavy metals in the aquacultural area is lower compared to the other types.The mean enrichment factor (EF) values of Cr, Cu, Pb, and Zn are less than 1.5, suggesting that these are not a major concern in the research area; whereas, the mean EF values for Cd is higher than 2 and As varies from 5-20 suggest that the pollution of these heavy metals is present in agricultural soil.The data analysis indicates that Cr mainly originates from a natural source; Cd and As have a significant anthropogenic input; Cd, Pb and Zn have a mixed source.
Exploration and Practice of C Programming Course Based on the Teaching Concept of Learning Community

In recent years, with the rapid development of science and technology, computer has become a hot industry, and C language, as an introductory language of computer, has also been highly valued.Based on the teaching concept of "learning community", this paper studies and practices the curriculum of "C Language Programming".It explores from the aspects of learning subjects, individual differences, group learning, self-evaluation, etc., with students as the main body, advocating students to study independently, and teachers to assist students to study, so as to solve the problems existing in the teaching mode, teaching method, cultivation method and other aspects in the course.Practical use of this teaching concept has proved that it can improve students' learning efficiency, the quality of teaching, and the effect of teaching.
Bridging the Performance Gap between FGSM and PGD Adversarial Training

Deep learning achieves state-of-the-art performance in many tasks but exposes to the underlying vulnerability against adversarial examples. Across existing defense techniques, adversarial training with the projected gradient decent attack (adv.PGD) is considered as one of the most effective ways to achieve moderate adversarial robustness. However, adv.PGD requires too much training time since the projected gradient attack (PGD) takes multiple iterations to generate perturbations. On the other hand, adversarial training with the fast gradient sign method (adv.FGSM) takes much less training time since the fast gradient sign method (FGSM) takes one step to generate perturbations but fails to increase adversarial robustness. In this work, we extend adv.FGSM to make it achieve the adversarial robustness of adv.PGD. We demonstrate that the large curvature along FGSM perturbed direction leads to a large difference in performance of adversarial robustness between adv.FGSM and adv.PGD, and therefore propose combining adv.FGSM with a curvature regularization (adv.FGSMR) in order to bridge the performance gap between adv.FGSM and adv.PGD. The experiments show that adv.FGSMR has higher training efficiency than adv.PGD. In addition, it achieves comparable performance of adversarial robustness on MNIST dataset under white-box attack, and it achieves better performance than adv.PGD under white-box attack and effectively defends the transferable adversarial attack on CIFAR-10 dataset.
Optimizing bid search in large outcome spaces for automated multi-issue negotiations using meta-heuristic methods

Bidding strategy is an important part of a negotiation strategy in automated multi-issue negotiations. In order to present good offers, which help maximize the agent’s utility, we need to search the outcome space and find appropriate bids. Bid search can become challenging in large outcome spaces with more than ten thousands of possible bids. The traditional search methods such as exhaustive or binary search are not efficient enough to find the right bids in a large space. This is mostly due to the high number of issues, high number of possible values for each issue, and increased time complexity of usual search methods. In this paper, we investigate the potential of using meta-heuristic methods for optimizing bid search in large outcome spaces. We apply some of the most popular meta-heuristic algorithms for bid search in bidding strategy of baseline negotiating agents and evaluate their impacts on negotiation performance in different negotiation domains. The evaluation results obtained through comprehensive experiments show how meta-heuristic algorithms can help improve bid search capability and consequently negotiation performance of the agents on different performance criteria. In addition, we show which search algorithm is most suitable for improving any particular performance criterion.