Reviewerly/exHarmony huggingface dataset:
Vietnamese Short Text Classification via Distributed Computation

Social networking has been growing rapidly in Vietnam.The sharing information is diverse and circulates in many forms.It requires user-friendly solutions such as topic sorting and perspectives analysis in analyzing community trends, advertisements or anticipating and monitoring the spread of bad news.Unfortunately, Vietnamese is highly different from other languages and little research has been conducted in the literature on messages classification.The implementation of machine learning models on Vietnamese has not been thoroughly investigated and these models' performance is unknown when applying in a different language.Vietnamese text is a serialization of syllables, hence, word boundary identification is not trivial.This research portrays our endeavor to construct an effective distributed framework for addressing the task of classification of short Vietnamese texts on social networks using the idea of probability categorization.The authors argue that addressing the task sharps the successful combination of machine learning, natural language processing, and ambient intelligence.The proposed framework is effective and enables fast calculation, suitable for implementation in Apache Spark, meeting the demand for dealing with large amounts of textual data on the current social networks.Our data has been collected from several online text sources of 12412 short messages classified into five different topics.The evaluation shows that our approach has achieved an average of 82.73% classification accuracy.Thoughtfully learning the literature, we could state that this is the first attempt to classify short Vietnamese messages under a distributed computation framework.
The Successful Ingredients of Policy Gradient Algorithms

Despite the sublime success in recent years, the underlying mechanisms powering the advances of reinforcement learning are yet poorly understood. In this paper, we identify these mechanisms - which we call ingredients - in on-policy policy gradient methods and empirically determine their impact on the learning. To allow an equitable assessment, we conduct our experiments based on a unified and modular implementation. Our results underline the significance of recent algorithmic advances and demonstrate that reaching state-of-the-art performance may not need sophisticated algorithms but can also be accomplished by the combination of a few simple ingredients.
Research on Remote Sensing Image Matching with Special Texture Background

The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.
The Oldest Ceramics of East Asia: Current Research Questions (Materials for the Educational Course “Ceramics as an Archaeological Source”)

Purpose. The article presents a historiographical review of the problems of research of ancient ceramics from archaeological sites of East Asia in the chronological interval from 20,000–18,000 to 9,000 years ago. Results. The subject of discussion is the periodization of monuments with early ceramics, the problems of socio-economic conditions of the emergence of pottery technology, its functional role in ancient societies, reconstruction of technological skills and technical levels of pottery, morphology and decor. There are a lot of controversial issues and “blind spots” in this direction. However, the opening of sites with ancient ceramics in East Asia showed that here, on the Pacific (eastern) outskirts of the Eurasian continent, pottery making technology first appeared about 10,000 years earlier than in the Middle East. The invention of ceramics in this region of the world at the turn of the Pleistocene and Holocene should be considered as a result of a combination of natural and social factors. The example of East Asia shows that the relationship between the appearance of ceramic vessels and the development of agriculture, as a technology for food production, is universally, not a mandatory factor. Conclusion. The article discusses certain regional differences in the formation of skills in making the most ancient ceramic vessels of East Asia. It is assumed that the development of ceramic technology in the Japanese archipelago and in the mainland areas of East Asia took place independently. For the Amur region, there are two local cultural traditions – Osipovskaya and Gromatukhinskaya. According to materials from Northern China, there is a version of the existence of a common line in the development of ancient ceramics in the Valley of the Nonny River. There are similarities between the early ceramics of Northeast China, and the Gromatukhinskaya and Osipovskaya cultures of Amur. For the ceramics of South Korea also note similarities with the materials of the Russian Far East, Japanese and Chinese dishes.
Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder

Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be identically and independently distributed, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Autoencoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.
Skills2Graph: Processing million Job Ads to face the Job Skill Mismatch Problem

In this paper, we present Skills2Graph, a tool that, starting from a set of users’ professional skills, identifies the most suitable jobs as they emerge from a large corpus of 2.5M+ Online Job Vacancies (OJVs) posted in three different countries (the United Kingdom, France, and Germany). To this aim, we rely both on co-occurrence statistics - computing a count-based measure of skill-relevance named Revealed Comparative Advantage (rca) - and distributional semantics - generating several embeddings on the OJVs corpus and performing an intrinsic evaluation of their quality. Results, evaluated through a user study of 10 labor market experts, show a high P@3 for the recommendations provided by Skills2Graph, and a high nDCG (0.985 and 0.984 in a [0,1] range), that indicates a strong correlation between the experts’ scores and the rankings generated by Skills2Graph.
Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems

The growing elderly population living independently demands remote systems for health monitoring. Falls are considered recurring fatal events and therefore have become a global health problem. Fall detection systems based on WiFi radio frequency signals still have limitations due to the difficulty of differentiating the features of a fall from other similar activities. Additionally, the antenna orientation has not been taking into account as an influencing factor of classification performance. Therefore, we present in this paper an analysis of the classification performance in relation to the antenna orientation and the effects related to polarization and radiation pattern. Furthermore, the implementation of a device-free fall detection platform to collect empirical data on falls is shown. The platform measures the Doppler spectrum of a probe signal to extract the Doppler signatures generated by human movement and whose features can be used to identify falling events. The system explores two antenna polarization: horizontal and vertical. The accuracy reached by horizontal polarization is 92% with a false negative rate of 8%. Vertical polarization achieved 50% accuracy and false negatives rate.
Characteristic Examples: High-Robustness, Low-Transferability Fingerprinting of Neural Networks

This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-transferability to unassociated models. This is the first work taking both robustness and transferability into consideration for generating realistic fingerprints, whereas current methods lack practical assumptions and may incur large false positive rates. To achieve better trade-off between robustness and transferability, we propose three kinds of characteristic examples: vanilla C-examples, RC-examples, and LTRC-example, to derive fingerprints from the original base model. To fairly characterize the trade-off between robustness and transferability, we propose Uniqueness Score, a comprehensive metric that measures the difference between robustness and transferability, which also serves as an indicator to the false alarm problem. Extensive experiments demonstrate that the proposed characteristic examples can achieve superior performance when compared with existing fingerprinting methods. In particular, for VGG ImageNet models, using LTRC-examples gives 4X higher uniqueness score than the baseline method and does not incur any false positives.
Rescuing Deep Hashing from Dead Bits Problem

Deep hashing methods have shown great retrieval accuracy and efficiency in large-scale image retrieval. How to optimize discrete hash bits is always the focus in deep hashing methods. A common strategy in these methods is to adopt an activation function, e.g. sigmoid() or tanh(), and minimize a quantization loss to approximate discrete values. However, this paradigm may make more and more hash bits stuck into the wrong saturated area of the activation functions and never escaped. We call this problem "Dead Bits Problem (DBP)". Besides, the existing quantization loss will aggravate DBP as well. In this paper, we propose a simple but effective gradient amplifier which acts before activation functions to alleviate DBP. Moreover, we devise an error-aware quantization loss to further alleviate DBP. It avoids the negative effect of quantization loss based on the similarity between two images. The proposed gradient amplifier and error-aware quantization loss are compatible with a variety of deep hashing methods. Experimental results on three datasets demonstrate the efficiency of the proposed gradient amplifier and the error-aware quantization loss.
Veiling glare removal: synthetic dataset generation, metrics and neural network architecture

In photography, the presence of a bright light source often reduces the quality and readability of the resulting image. Light rays reflect and bounce off camera elements, sensor or diaphragm causing unwanted artifacts. These artifacts are generally known as "lens flare" and may have different influences on the photo: reduce contrast of the image (veiling glare), add circular or circular-like effects (ghosting flare), appear as bright rays spreading from light source (starburst pattern), or cause aberrations. All these effects are generally undesirable, as they reduce legibility and aesthetics of the image. In this paper we address the problem of removing or reducing the effect of veiling glare on the image. There are no available large-scale datasets for this problem and no established metrics, so we start by (i) proposing a simple and fast algorithm of generating synthetic veiling glare images necessary for training and (ii) studying metrics used in related image enhancement tasks (dehazing and underwater image enhancement). We select three such no-reference metrics (UCIQE, UIQM and CCF) and show that their improvement indicates better veil removal. Finally, we experiment on neural network architectures and propose a two-branched architecture and a training procedure utilizing structural similarity measure.
Causal Learning for Socially Responsible AI

There have been increasing concerns about Artificial Intelligence (AI) due to its unfathomable potential power. To make AI address ethical challenges and shun undesirable outcomes, researchers proposed to develop socially responsible AI (SRAI). One of these approaches is causal learning (CL). We survey state-of-the-art methods of CL for SRAI. We begin by examining the seven CL tools to enhance the social responsibility of AI, then review how existing works have succeeded using these tools to tackle issues in developing SRAI such as fairness. The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.
On Cycles, Attackers and Supporters --- A Contribution to The Investigation of Dynamics in Abstract Argumentation

Abstract argumentation as defined by Dung in his seminal 1995 paper is by now a major research area in knowledge representation and reasoning. Dynamics of abstract argumentation frameworks (AFs) as well as syntactical consequences of semantical facts of them are the central issues of this paper. The first main part is engaged with the systematical study of the influence of attackers and supporters regarding the acceptability status of whole sets and/or single arguments. In particular, we investigate the impact of addition or removal of arguments, a line of research that has been around for more than a decade. Apart from entirely new results, we revisit, generalize and sum up similar results from the literature. To gain a comprehensive formal and intuitive understanding of the behavior of AFs we put special effort in comparing different kind of semantics. We concentrate on classical admissibility-based semantics and also give pointers to semantics based on naivity and weak admissibility, a recently introduced mediating approach. In the second main part we show how to infer syntactical information from semantical one. For instance, it is well-known that if a finite AF possesses no stable extension, then it has to contain an odd-cycle. In this paper, we even present a characterization of this issue. Moreover, we show that the change of the number of extensions if adding or removing an argument allows to conclude the existence of certain even or odd cycles in the considered AF without having further information.
Digital man as a philosophical problem

It is proposed to consider a digital person as the latest stage of human development as a major object and subject of information relations in the information society at the latest stages of its development; as a postmodern type of a homo sapiens capable of processing information, creating new information phenomena, interconnections and structures. It is substantiated that the transition to a digital person occurred due to convergence of technologies of artificial intelligence, machine training and powerful databases capable of using an unlimited amount of information for its processing, classification and multiple use. The attention is drawn to the fact that a digital person is, first of all, a person of new moral values, which immerses in the virtual reality of simulations and increasingly perceives the world as a digital game environment, realizing its conventionality, controllability of its parameters and the possibility of exiting it.
Research on Query Optimization of Classic Art Database Based on Artificial Intelligence and Edge Computing

With the changes and development of the social era, my country’s classic art is slowly being lost. In order to more effectively inherit and preserve classic art, the collection and sorting of classic art data through modern information technology has become a top priority. Database storage is a good way. However, as the amount of data grows, the requirements for computing processing power and query speed for massive amounts of data and information are also increasing day by day. Faced with this problem, this article is aimed at studying the optimization of database queries through effective algorithms to improve the efficiency of data query. Based on the traditional database query optimization algorithm, this article improves on the traditional algorithm and proposes a semi-join query optimization algorithm, which reduces the number of connection cards and the number of columns and uses the number of blocks that participate in the semi-link algorithm connection and preconnection preview and selection. And other functions reduce the size of the participating block, and the connection sent between sites reduces the cost of sending between networks. The graph data query optimization algorithm is used to optimize the graph data query in the database to reduce the extra task overhead and improve the system performance. The experimental results of this paper show that through the data query optimization algorithm of this paper, the additional task overhead is reduced by 19%, the system performance is increased by 22%, and the data query efficiency is increased by 31%.
What’s the Context? Implicit and Explicit Assumptions in Model-Based Goal Recognition

Every model involves assumptions. While some are standard to all models that simulate intelligent decision-making (e.g., discrete/continuous, static/dynamic), goal recognition is well known also to involve choices about the observed agent: is it aware of being observed? cooperative or adversarial? In this paper, we examine not only these but the many other assumptions made in the context of model-based goal recognition. By exploring their meaning, the relationships between them and the confusions that can arise, we demonstrate their importance, shed light on the way trends emerge in AI, and suggest a novel means for researchers to uncover suitable avenues for future work.
NIR-MID Reflectance and Emissivity Study at Different Temperatures of Sodium Carbonate Minerals: Spectra Characterization and Implication for Remote Sensing Identification

Recent satellite observations and the deconvolution of remote sensing data have shown the existence of various carbonate minerals in different solar system bodies. Emissivity, from 403 to 803 K, and reflectance spectra at 300 and 193 K of selected carbonates minerals with different ratios of Na:Ca and water amounts were respectively collected at 3–20 μm and at 1–16 μm. All reflectance spectra show absorption features in the 1.9–2.5 and 3.4–4.0 μm areas due to overtone and a combination of CO32− and fundamental vibrational models at ≈9.09, 11.35, 7.06, and 14.7 μm. The increase of the Na:Ca ratio in anhydrous samples produces a shift of the absorption features in the 3.4–4.0 μm area toward shorter wavelengths, and the peak at 3.9 μm doubles in the presence of a CO32− oxygen group shared with two cations in minerals having more complex structures. The comparison of the bands at ≈669 and ≈794 cm−1 in the emissivity spectra collected at high temperatures indicates that around 600 K, phase transitions occurred in natrite and thermonatrite. The reflectance spectra measured at 193 K reveal a fine structure compared to spectra collected at room temperature. The comparison of laboratory results with the spectrum of Ceres’s brightest crater Occator from the Dawn mission, taken as a case study, showed how the anhydrous samples, shortite and nyerereite, studied in this work can also be hypothesized for Ceres’s surface beyond that already suggested (trona, natrite, thermonatrite).
Neural Architecture Search of SPD Manifold Networks

In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks, aiming to automate the design of SPD neural architectures. To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design. Further, we model our new NAS problem with a one-shot training process of a single supernet. Based on the supernet modeling, we exploit a differentiable NAS algorithm on our relaxed continuous search space for SPD neural architecture search. Statistical evaluation of our method on drone, action, and emotion recognition tasks mostly provides better results than the state-of-the-art SPD networks and traditional NAS algorithms. Empirical results show that our algorithm excels in discovering better performing SPD network design and provides models that are more than three times lighter than searched by the state-of-the-art NAS algorithms.
Aspect-Based Sentiment Analysis on Indonesian Restaurant Review Using a Combination of Convolutional Neural Network and Contextualized Word Embedding

Someone's opinion on a product or service that is poured through a review is something that is quite important for the owner or potential customer. However, the large number of reviews makes it difficult for them to analyze the information contained in the reviews. Aspect-based sentiment analysis is the process of determining the sentiment polarity of a sentence based on predetermined aspects.This study aims to analyze an Indonesian restaurant review using a combination of Convolutional Neural Network and Contextualized Word Embedding models. Then it will be compared with a combination of Convolutional Neural Network and Traditional Word Embedding models. The result of aspect-classification on three models; BERT-CNN, ELMo-CNN, and Word2vec-CNN give the best results on the ELMo-CNN model with micro-average precision of 0.88, micro-average recall of 0.84, and micro-average f1-score of 0.86. Meanwhile, the sentiment-classification gives the best results on the BERT-CNN model with a precision value of 0.89, a recall of 0.89, and an f1-score of 0.91. Classification using data without stemming have almost similar results, even better than using data with stemming.