Reviewerly/exHarmony huggingface dataset:
Score to Learn: A Comparative Analysis of Scoring Functions for Active Learning in Robotics

Accurately detecting objects in unconstrained settings is crucial for robotic agents, such as humanoids, that function in ever-changing environments. Current deep learning based methods achieve remarkable performance on this task on general purpose benchmarks and they are therefore appealing for robotics. However, their high accuracy comes at the price of computationally expensive off-line training and extensive human labeling. These aspects make their adoption in robotics challenging, since they prevent rapid model adaptation and re-training to novel tasks and conditions. Nonetheless, robots, and especially humanoids, being embodied in the surrounding environment, have access to streams of data from their sensors that, even though without supervision, might contain information of the objects of interest. The Weakly-supervised Learning (WSL) framework offers a set of tools to tackle these problems in general-purpose Computer Vision. In this work, we aim at investigating their adoption in the robotics domain which is still at a preliminary stage. We build on previous work, studying the impact of different, so called, scoring functions, which are at the core of WSL methods, on Pascal VOC, a general purpose dataset, and a prototypical robotic setting, i.e. the iCubWorld-Transformations dataset.
Locally Weighted Mean Phase Angle (LWMPA) Based Tone Mapping Quality Index (TMQI-3)

High Dynamic Range (HDR) images are the ones that contain a greater range of luminosity as compared to the standard images. HDR images have a higher detail and clarity of structure, objects, and color, which the standard images lack. HDR images are useful in capturing scenes that pose high brightness, darker areas, and shadows, etc. An HDR image comprises multiple narrow-range-exposure images combined into one high-quality image. As these HDR images cannot be displayed on standard display devices, the real challenge comes while converting these HDR images to Low dynamic range (LDR) images. The conversion of HDR image to LDR image is performed using Tone-mapped operators (TMOs). This conversion results in the loss of much valuable information in structure, color, naturalness, and exposures. The loss of information in the LDR image may not directly be visible to the human eye. To calculate how good an LDR image is after conversion, various metrics have been proposed previously. Some are not noise resilient, some work on color channels separately (Red, Green, and Blue one by one), and some lack capacity to identify the structure. To deal with this problem, we propose a metric in this paper called the Tone Mapping Quality Index (TMQI-3), which evaluates the quality of the LDR image based on its objective score. TMQI-3 is noise resilient, takes account of structure and naturalness, and works on all three color channels combined into one luminosity component. This eliminates the need to use multiple metrics at the same time. We compute results for several HDR and LDR images from the literature and show that our quality index metric performs better than the baseline models.
Facial Expression Synthesis with Synchronous Editing of Face Organs

Facial expression synthesis (FES) is to generate a new face with a desired expression domain. However, in FES field, many current methods fail to enable relevant facial components to transform simultaneously, which makes the target expression look unnatural. Therefore, we are purposed to develop a method which can edit main organs synchronously when generating a new expression. Based on this, the global spatial interaction mechanism is introduced by us which can capture the long-range dependency between distant positions. Besides, current methods usually suffer from blurs and artifacts around key regions. From the point of frequency domain, it is probably caused by the distortion of high frequency information. After this we add a spectrum restriction loss to original training losses in order to improve the fidelity of generated faces. Extensive experiments prove our model a great success on two widely-used datasets: MUG and CASIA-Oulu.
Codebook-Based Near-Duplicate Video Detection

In the current context of monetization of multimedia content, it is common to see the appearance of edited replicas of popular videos to take advantage of the momentum of those. In this work, several parameters of near-duplicate video detection systems based on codebooks are studied using techniques from the field of information retrieval. As a result, a system with high average precision, usually higher than 85%, is obtained. Several hyperparameters of the system, such as the aggregation mechanisms and the retrieval model, are analyzed, thus adjusting the system for optimal performance.
An information security model of industrial control network in the coal industry

The coal mining industry is taking the development path of information industrialization and promoting the “deep integration of the two technologies”, which involves the construction and citation of many new technologies and new business models. With the introduction of cloud computing and big data technologies, data sharing is becoming more and more important. The industrial control system becomes more and more open, and the safety protection measures for the industrial control system are seriously lagging behind, leading to the extremely prominent information security problems of the industrial control system of the coal industry. This paper analyzes the industrial control system of the coal mine industry, researches the specificity of the coal mine industry and its applicable protection technology, and combines IEC62443, GB/T30976 and other industrial control system safety general technical standards, and proposes a coal mine industry industrial control network information security model.
High Performance Convolutional Neural Network Accelerator Based on Design Space Exploration

Convolutional neural networks (CNNs) have achieved a wide range of applications in the field of computer vision. However, its large number of parameters and computation limits its deployment in embedded terminals. Designing dedicated CNN accelerators becomes an efficient way to solve the situation. In this paper, a basic access model of systolic accelerator is established to find the optimal design parameters for specific CNN structure. The experiment result shows that $14 \times16$ is the optimum size of processing element array for AlexNet and VGG16, which matches with the design parameters of accelerators with the same architecture.
A Unified Binary Embedding Framework for Image Retrieval

Hashing, as an efficient retrieval strategy, has been extensively studied in the image retrieval community. It aims to map the original high-dimensional features into compact binary codes, which dramatically reduces the cost of computing similarity with economic memory consumption. In general, most existing algorithms contain two stages: projecting high-dimensional descriptors into low-dimensional features, and encoding the low-dimensional features as binary strings. Although these two-stage approaches are intuitive and effective, certain beneficial information to binary encoding is inevitably discarded in the dimensionality reduction, which tends to lossy encoding. In this paper, we propose a novel hash algorithm where integrate subspace reconstruction and binary quantization into a unified framework and compact hash codes can be generated directly from high-dimensional features. More specifically, the proposed method discovers a latent subspace from the original feature space which is suitable for hash learning, thus alleviating the heavy information loss caused by traditional dimensionality reduction strategy. Meanwhile, thanks to the excellent property of rotational invariance in Euclidean space, the proposed method avoid the challenge of directly optimizing the binary matrix. Specially, our approach essentially is built on l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> estimator obeying the unsupervised paradigm, without involving any labeled data. Furthermore, we design an effective iterative alternating strategy to optimize the proposed model, so as to generate compact binary codes for the subsequent image matching. Extensive experiments on public benchmarks demonstrate that our method outperforms the compared method by a significant margin.
Isolation and Comparison of Company Names in the News Text with Tickers of Companies Traded on Stock Exchanges

Determining the names of companies in the news text is an important task in predicting changes in the prices of companies’ shares. There is a large number of works devoted to this problem, a large number of algorithms have been created. On the other hand, in order to understand which stocks of which company may be affected by this or that news, it is necessary to understand exactly which ticker corresponds to the name of the company extracted from the text of the news.Behind the seeming simplicity of the question, there is the difficulty of comparing the descriptions of tickers that exchanges provide with incomplete company names that are indicated in ordinary, non-financial news. Often in the news, companies are written in abbreviated form or abbreviations. At the same time, the description of tickers contains deliberately redundant information, which also needs to be isolated and removed to improve the results of matching names.In this article, we will analyze several possible approaches to solving this problem and present an algorithm developed by us for comparing company names found in news texts with stock tickers.
Understanding the Access and Benefit-Sharing of Genetic Resources for Microbiology Researchers

Following enforcement of the Nagoya Protocol (NP), in which the sovereign rights to genetic resources of countries are recognized, new legal obligations regarding access and benefit-sharing (ABS) that did not exist before have now been imposed on researchers.To implement the NP, many countries are introducing new procedures and regulations when a researcher wants to obtain genetic resources for commercial or noncommercial uses.It is therefore expected that resource-rich countries will adopt strong regulations to protect their genetic resources.In this regard, Korean microbiologists will need to respond to these changes to minimize the potential damages caused by the ABS.This paper reviews the key contents of the NP to raise its awareness among scientific researchers and further presents specific measures to meet the ABS obligations accordingly.For example, Korean researchers, in principle, do not need to acquire Prior Informed Consents (PICs) when they access Korean microbial resources for both commercial or research purposes.Nevertheless, when a foreign culture collection agency such as DSMZ requests a confirmation of compliance with the NP to deposit genetic resources, Korean researchers can also apply for a PIC with the Korean government as an exception.By referring to this article, microbiologists will be able to conduct their research in compliance with the NP while respecting the legal ABS obligations of each resource-providing country.
Real Sample Consistency Regularization for GANs

Mode collapse has always been a fundamental problem in generative adversarial networks. The recently proposed Zero Gradient Penalty (0GP) regularization can alleviate the mode collapse, but it will exacerbate a discriminator's misjudgment problem, that is the discriminator judges that some generated samples are more real than real samples. In actual training, the discriminator will direct the generated samples to point to samples with higher discriminator outputs. The serious misjudgment problem of the discriminator will cause the generator to generate unnatural images and reduce the quality of the generation. This paper proposes Real Sample Consistency (RSC) regularization. In the training process, we randomly divided the samples into two parts and minimized the loss of the discriminator's outputs corresponding to these two parts, forcing the discriminator to output the same value for all real samples. We analyzed the effectiveness of our method. The experimental results showed that our method can alleviate the discriminator's misjudgment and perform better with a more stable training process than 0GP regularization. Our real sample consistency regularization improved the FID score for the conditional generation of Fake-As-Real GAN (FARGAN) from 14.28 to 9.8 on CIFAR-10. Our RSC regularization improved the FID score from 23.42 to 17.14 on CIFAR-100 and from 53.79 to 46.92 on ImageNet2012. Our RSC regularization improved the average distance between the generated and real samples from 0.028 to 0.025 on synthetic data. The loss of the generator and discriminator in standard GAN with our regularization was close to the theoretical loss and kept stable during the training process.
Research on Signal Modulation Type Recognition Based on Cyclic Spectrum in Internet of Things

The identification of signal modulation type in the perception supervision mechanism in the power Internet of Things environment is the primary content. Because the wireless communication channel environment is complex and difficult to predict, especially in the case of strong noise interference, many parameter estimation and modulation type identification methods are difficult to achieve the desired accuracy. In this paper, based on the obvious differences between the cyclic spectrum of different modulation types of signals, related features are extracted, and some recognition techniques are used to distinguish these features. The identification of signal modulation based on cyclic spectrum has the characteristics of strong anti-interference ability, and can also be applied well in the case of low signal-to-noise ratio.
Design of a Knowledge-Based Recommender System for Recipes From an End-User Perspective

Nowadays, recommender systems are a fundamental part of several online services. However, most of these systems rely on collective user data and ratings or a preselection of parameters to derive appropriate recommendations. Within this paper, we examine recommendations without previous user data. We therefore designed and evaluated a knowledge-based recommender system by turning to recipe recommendations that offer alternatives for favorite recipes. We introduce and compare three versions of a given algorithm. Our evaluation shows that the knowledge-based approach may serve as a good start for deriving appropriate recommendations without prior user data. Moreover, we show that end-users’ assumptions about decisive criteria of a recommender system do not necessarily match the later actual decisive criteria.
Automatic slides generation in the absence of training data

Disseminating the main research findings is one of the main requirements to become a successful researcher. Presentation slides are the most common way to present paper content. To support researchers in slide preparation, the NLP research community has explored the use of summarization techniques to automatically generate a draft of the slides consisting of the most salient sentences or phrases. State-of-the-art methods adopt a supervised approach, which first estimates global content relevance using a set of training papers and slides, then performs content selection by optimizing also section-level coverage. However, in several domains and contexts there is a lack of training data, which hinders the use of supervised models. This paper focuses on addressing the above issue by applying unsupervised summarization methods. They are exploited to generate sentence-level summaries of the paper sections, which are then refined by applying an optimization step. Furthermore, it evaluates the quality of the output slides by taking into account the original paper structure as well. The results, achieved on a benchmark collection of papers and slides, show that unsupervised models performed better than supervised ones on specific paper facets, whereas they were competitive in terms of overall quality score.
Cautious Classification with Data Missing Not at Random Using Generative Random Forests

Missing data present a challenge for most machine learning approaches. When a generative probabilistic model of the data is available, an effective approach is to marginalize missing values out. Probabilistic circuits are expressive generative models that allow for efficient exact inference. However, data is often missing not at random, and marginalization can lead to overconfident and wrong conclusions. In this work, we develop an efficient algorithm for assessing the robustness of classifications made by probabilistic circuits to imputations of the non-ignorable portion of missing data at prediction time. We show that our algorithm is exact when the model satisfies certain constraints, which is the case for the recent proposed Generative Random Forests, that equip Random Forest Classifiers with a full probabilistic model of the data. We also show how to extend our approach to handle non-ignorable missing data at training time.
Potential Security Risks of Internationalized Domain Name Processing for Hyperlink

Domain names and URLs are essential technologies in the current Internet. Thus, a failure in URL processing not only gives an inconvenience to users but also causes serious security vulnerability. If URLs are still organized with only ASCII characters, there may be no problem on URL processing. However, current URLs are further extended and complicated. One of the complexity is coming from Internationalized Domain Name (IDN) related extensions. Thus, there are no simple ways to process URLs due to their characteristics and historical extensions. In this paper, firstly, we introduce possible threats due to wrong IDN processing. Then, we present potential threats due to URL extraction operations in applications with classifying attack surfaces. We examined the above problems with various programming languages and web browsers and confirmed many issues in different environments. Furthermore, we confirmed and demonstrated that the failure pattern are not identical because the issues that come from IDN processing varies. Finally, we conclude the experimental result and propose ways to a comprehensive solution.
Optimize quantum simulation using a force-gradient integrator

Quantum simulation has shown great potential in many fields due to its powerful computational capabilities. However, the limited fidelity can lead to a severe limitation on the number of gate operations, which requires us to find optimized algorithms. Trotter decomposition and high order Trotter decompositions are widely used in quantum simulations. We find that they can be significantly improved by force-gradient integrator in lattice QCD. By using two applications as examples, we show that the force-gradient decomposition can reduce the number of gate operations up to about a third of those using high order Trotter decompositions. Therefore, force-gradient decomposition shows a great prospective in future applications of quantum simulation.
Comparing Deep Learning-based Architectures for Logo Recognition

Logo recognition is a subset of image recognition and has attracted attentions of many researchers due to its specific problem. That is, logo recognition has a wide intra-class and inter-class variability. For example, distinguishing a new edition of a company’s logo and the old one falls into a specific problem that is tailored to logo recognition problem. In this paper, we compare three deep learning-based logo recognition architectures, namely Bianco’s architecture, AlexNet, and Xception. Bianco’s architecture is chosen as a sample of an architecture that includes many preprocessing pipelines including a logo region proposal. Therefore, in this paper we want to investigate whether Bianco’s architecture performs significantly better compared to the others if a logo region proposal is removed. We compare it with other typical deep convolutional neural network architectures such as AlexNet and Xception. Experiments are carried out on the FlickrLogo-32plus, FlickrLogos 27, BrandLogo, and LogoDet-3K. In addition, we also add the curated dataset with hundreds of logo by using Selenium WebDriver. We found out that Bianco’s architecture does not significantly perform better compared to AlexNet, and performs worse compared to Xception. There, we conclude that a logo region proposal is an important preprocessing step in logo recognition.
Whale Optimisation Freeman Chain Code (WO-FCC) Extraction Algorithm for Handwritten Character Recognition

In order to improve the classification accuracy in the field of handwriting character recognition (HCR), the number of derivative algorithms has improved and the interest in feature extraction has increased. In this paper, we propose a metaheuristic method for feature extraction algorithm with Whale Optimisation Algorithm (WOA) based HCR. WOA is a swarm-based techniques that mimic the social behavior of groups of animals, which mimics the social behavior of humpback whales. Freeman chaincode (FCC) is utilised as a data representations of handwritten text images. Nevertheless, the representations of FCC depends on the length of the path and the branching of the character’s nodes. To solve this problem, we propose a metaheuristic approach through WOA to find the shortest path length and minimum computational time for handwriting recognition. Finally, the results were compared with the existing proposed Flower Pollination Algorithm (FPA) at the time of FCC extraction. The results show that WOA is a bit better at getting shorter path lengths than FPA in terms of path lengths. In terms of calculation time, WOA calculates faster calculation time by feature extraction than FPA.
Deep hierarchical reinforcement learning to manage the trade-off between sustainability and profitability in common pool resources systems

Natural resource management refers, in theory, to a sustainable utilization of fundamental natural resources. For the different stakeholders, there is a trade-off between profitability and a sustainable exploitation that allows ecosystems and resources to be preserved. These issues can be managed mathematically or with optimization via simulation, but it requires perfect knowledge and robust data to be valid, which often represents a lack in marine sciences, for various reasons (lack of sampling, local policies, difficulties in accessing quantitative data etc.). In this paper, we focus on deep hierarchical reinforcement learning based approach to help decision making in a common pool resource system. This approach allows us to i/ model decisionmakers behavior, ii/ determine sustainable exploitation quotas, iii/ model owners wishing to maximize their profitability on an individual scale, taking into account the uncertainties and partial knowledge concerning the resources, in a partially observable Markov game. By comparing two types of fishing gears, selective and non-selective, we show the limits of the trade-off between profitability and sustainability. Indeed, the latter depends on the amount of greedy behavior, the power of exploitation and the need for awareness and a stricter regulation of the exploitation of the most vulnerable resources.