Reviewerly/exHarmony huggingface dataset:
Data-driven Targeted Advertising Recommendation System for Outdoor Billboard

In this article, we propose and study a novel data-driven framework for Targeted Outdoor Advertising Recommendation (TOAR) with a special consideration of user profiles and advertisement topics. Given an advertisement query and a set of outdoor billboards with different spatial locations and rental prices, our goal is to find a subset of billboards, such that the total targeted influence is maximum under a limited budget constraint. To achieve this goal, we are facing two challenges: (1) it is difficult to estimate targeted advertising influence in physical world; (2) due to NP hardness, many common search techniques fail to provide a satisfied solution with an acceptable time, especially for large-scale problem settings. Taking into account the exposure strength, advertisement matching degree, and advertising repetition effect, we first build a targeted influence model that can characterize that the advertising influence spreads along with users mobility. Subsequently, based on a divide-and-conquer strategy, we develop two effective approaches, i.e., a master–slave-based sequential optimization method, TOAR-MSS, and a cooperative co-evolution-based optimization method, TOAR-CC, to solve our studied problem. Extensive experiments on two real-world datasets clearly validate the effectiveness and efficiency of our proposed approaches.
AMANet: Adaptive Multi-Path Aggregation for Learning Human 2D-3D Correspondences

Learning human 2D-3D correspondences aims to map all human 2D pixels to a 3D human template, namely human densepose estimation, involving surface patch recognition (i.e., Index-to-Patch (I)) and regression of patch-specific UV coordinates. Despite recent progress, it remains challenging especially under the condition of “in the wild”, where RGB images capture real-world scenes with backgrounds, occlusions, scale variations, and postural diversity. In this paper, we address three vital problems in this task: 1) how to perceive multi-scale visual information for instances “in the wild”; 2) how to design learning objectives to address the precise instance representation harassed by “multiple instances in one bounding box” phenomenon; and 3) how to boost the performance of index-to-patch prediction faced by limited supervision. To tackle problems above, we propose an end-to-end deep Adaptive Multi-path Aggregation network (AMA-net) for Human DensePose Estimation. First, we introduce an adaptive multi-path aggregation algorithm to extract varying-sized instance-level features, which capture multi-scale information of a bounding-box and are then utilized for parsing different instances. Second, we adopt an instance augmentation learning objective to further distinguish the target instance from other interference instances. Third, taking advantage of 2D human parsers that are trained from sufficient annotations, we introduce a task transformer that bridges the “gap” between 2D human parsing and densepose estimation, thus benefiting the performance of densepose estimator. Experimental results on the challenging DensePose-COCO dataset demonstrate that our approach sets a new record, and it significantly outperforms the state-of-the-art methods. Codes and models are publicly available.
Iterative Named Entity Recognition with Conditional Random Fields

Named entity recognition (NER) constitutes an important step in the processing of unstructured text content for the extraction of information as well as for the computer-supported analysis of large amounts of digital data via machine learning methods. However, NER often relies on domain-specific knowledge, being conducted manually in a time- and human-resource-intensive process. These can be reduced with statistical models performing NER automatically. The current work investigates whether Conditional Random Fields (CRF) can be efficiently trained for NER in German texts, by means of an iterative procedure combining self-learning with a manual annotation–active learning–component. The training dataset increases continuously with the iterative procedure. Whilst self-learning did not markedly improve the performance of the CRF for NER, the manual annotation of sentences with the lowest probability of correct prediction clearly improved the model F1-score and simultaneously reduced the amount of manual annotation required to train the model. A model with an F1-score of 0.885 was able to be trained in 11.4 h.
Blind Spots of Security Monitoring in Enterprise Infrastructures: A Survey

Cybersecurity monitoring today is laborious but straightforward: dump network traces at chokepoints in a network, collect log files from services, feed both to a proper security information and event management solution, and get alerted if something suspicious happens, right? Wrong!
Building an Accessible, Usable, Scalable, and Sustainable Service for Scholarly Big Data

Since the emergence of scholarly big data, there have been several efforts for web-based services such as digital library search engines (DLSEs). However, much of the design and specifications of an accessible, usable, scalable, and sustainable DLSE have not been well represented and discussed in the literature. We argue that these four characteristics are essential to providing a high-quality service for scholarly big data from both the user and developer’s perspectives. This paper reviews the design, implementation, and operation experiences, and lessons of CiteSeerX, a real-world digital library search engine. We analyze the strengths and weaknesses of the current design, and proposed a new design with a revised architecture, enhanced hardware, and software infrastructure. The Alpha version of the new design has been implemented and tested. The new system replaces MySQL and Apache Solr with a single instance of Elasticsearch, which plays a dual role of data storage and search. Another major improvement is the integration of extraction and ingestion, which significantly boosts document ingestion speed. The web application is re-engineered to enhance the user experience by applying a learning-to-rank model and offering more refined search tools. The system is also improved in many other aspects. We believe the design considerations and experience can benefit researchers and engineers who plan, design, and upgrade future systems with comparable scales and functionalities.
Machine Learning Based and Reconfigurable Architecture with a Countermeasure for Side Channel Attacks

Cryptographic devices, which are embedded in SoC systems and mathematically secured algorithms, are used for operating it. But a side channel leakage may cause the secret data in these systems to be at high risk. Particularly, cryptography circuits like ECC algorithm are prone to power attacks like correlation power analysis (CPA) and differential power analysis (DPA). Hence, it is necessary to safeguard the sensitive information using proposed method having ECC with countermeasure. This paper focuses on protecting sensitive information using chaotic countermeasure as reconfigurable architecture by adopting ARTIX-7 board and offline ELM algorithm to detect the attack. In this, an effective countermeasure is designed and implemented using the FPGA for attack detection. Moreover, the proposed chaotic architecture has been integrated with ECC bit key mounted on ARTIX-7 board and tested nearly with 250 power traces recorded from the architecture. We have compared the proposed chaotic maps with the other current countermeasure technique, such as WDDL, IVR, and inductive ECC, in which the proposed design outperforms the above described existing structures in terms of area usage, power overhead, and frequency overhead. And to verify the strength of the encryption algorithm by using NIST to ensure that the given random number can be used for cryptographic purposes.
Expressive Logics for Coinductive Predicates

The classical Hennessy-Milner theorem says that two states of an image-finite transition system are bisimilar if and only if they satisfy the same formulas in a certain modal logic. In this paper we study this type of result in a general context, moving from transition systems to coalgebras and from bisimilarity to coinductive predicates. We formulate when a logic fully characterises a coinductive predicate on coalgebras, by providing suitable notions of adequacy and expressivity, and give sufficient conditions on the semantics. The approach is illustrated with logics characterising similarity, divergence and a behavioural metric on automata.
Lithological Mapping Using Landsat 8 OLI in the Meso-Cenozoic Tarfaya Laayoune Basin (South of Morocco): Comparison between ANN and SID Classification

In the Saharian domain, the Tarfaya-Laayoune coastal basin developed in a stable passive margin, where asymmetrical sedimentation increase from East to West and reach a sediment stack of about 14 kilometers.However, the morphology of the studied area corresponds to a vast plateau (hamada) presenting occasional major reliefs.For this purpose, remote sensing approach has been applied to find the best approaches for truthful lithological mapping.The two supervised classification methods by machine learning (Artificial Neural Network and Spectral Information Divergence) have been evaluated for a most accurate classification to be used for our lithofacies mapping.The latest geological maps and RGB images were used for pseudo-color groups to identify important areas and collect the ROIs that will serve as facilities samples for the classifications.The results obtained showed a clear distinction between the various formation units, and very close results to the field reality in the ANN classification of the studied area.Thus, the ANN method is more accurate with an overall accuracy of 92.56% and a Kappa coefficient is 0.9143.
Comparative analysis of U-Net and TLMDB GAN for the cardiovascular segmentation of the ventricles in the heart

Magnetic Resonance Image (MRI) is an important imaging modality for diagnosing heart disease and analyzing heart function. The size and shape of the ventricle are important parameters for judging whether the heart is normal, and the ventricles in the MRI image is effectively segmented It is the key to obtain the ventricle size, shape and other parameters. Accurate segmentation of the entricle is the fundamental guarantee for the evaluation of cardiac function. However, in the heart image, the contrast between the ventricle area and the background area is not obvious, the boundary is blurred, and there is noise in most of the images. The accurate segmentation of the ventricle becomes a challenging problem.We performed scanning of short-axis cardiac MR image sequences based on 33 subjects. Each subject has 8 to 15 sequences, each pertaining to a 20-frame sequence. Based on the U-Net neural network structure, the high-resolution information directly transferred from the encoder to the same-height decoder through the connection operation can provide more refined features for segmentation, such as gradients. The MRI left ventricular image segmentation method based on transfer learning and multi-scale discriminant Generative Adversarial Network (TLMDB GAN) solves the problem of insufficient ventricular image data.According to the experimental results of TLMDB GAN and U-Net network on the data set, the Dice coefficients of TLMDB GAN segmentation of the inner cardiac wall and outer cardiac wall of the ventricle are 0.9399 and 0.9697, respectively, which are 0.01 higher than other methods. The Dice coefficients of U-Net segmentation of the inner cardiac wall and outer cardiac wall of the ventricle are 0.8829 and 0.9292, respectively; CONCLUSION: The experimental results show that the TLMDB GAN based on transfer learning and multi-scale discrimination significantly improves the segmentation accuracy when compared with the U-Net segmentation model.
Digital Currency Price Analysis Via Deep Forecasting Approaches for Business Risk Mitigation

Bitcoin, the most well-known of all the cryptocurrencies, have attracted a lot of attention thus far, and their prices have been quite volatile. While some research employ traditional statistical and econometric methods to discover the factors that drive Bitcoin prices, experimenting on the development of prediction models to be utilized as decision support aids in investment approaches is uncommon. The sudden rise and fall of cryptocurrency rates affects the economies and future perspectives of various businesses. In order to minimize business risks, to track the differences and avoid serious economic loss, prediction of daily digital currency rates becomes a crucial task. Our study performs a comparative analysis of Bitcoin price prediction utilizing efficient neural network techniques such as LSTM and GRU. A better RNN-based approach is derived as a result of the study. This approach will assist to facilitate a secure environment for businesses and to alarms to carryout risk management tasks for business risk mitigation purposes.
Query-Augmented Active Metric Learning

In this article, we propose an active metric learning method for clustering with pairwise constraints. The proposed method actively queries the label of informative instance pairs, while estimating underlying metrics by incorporating unlabeled instance pairs, which leads to a more accurate and efficient clustering process. In particular, we augment the queried constraints by generating more pairwise labels to provide additional information in learning a metric to enhance clustering performance. Furthermore, we increase the robustness of metric learning by updating the learned metric sequentially and penalizing the irrelevant features adaptively. In addition, we propose a novel active query strategy that evaluates the information gain of instance pairs more accurately by incorporating the neighborhood structure, which improves clustering efficiency without extra labeling cost. In theory, we provide a tighter error bound of the proposed metric learning method using augmented queries compared with methods using existing constraints only. Furthermore, we also investigate the improvement using the active query strategy instead of random selection. Numerical studies on simulation settings and real datasets indicate that the proposed method is especially advantageous when the signal-to-noise ratio between significant features and irrelevant features is low.
A Novel Class Imbalance-oriented Polynomial Neural Network Algorithm for Disease Diagnosis

The class imbalance problem is common in disease diagnosis, which significantly damages the artificial intelligence-based diagnostic models and causes enormous cost about disease misclassification. To alleviate the class imbalance problem, we propose a novel class imbalance-oriented polynomial neural network (CIPNN) algorithm, which incorporates the data sampling and the classifier ensemble. Specifically, we utilize the nearest neighbor algorithm to identify a critical area from the given medical dataset (original dataset), which forms a critical area dataset. Then, we apply the synthetic minority over-sampling technique to generate a balanced dataset based on the original medical dataset. Further, we obtain the ensemble by combining multiple polynomial neural network classifiers, which are learned from the bootstrap samples sampled from the balanced dataset, the critical area dataset, and the original medical dataset, respectively. The experiments conducted on nine imbalanced medical datasets demonstrate that the proposed method can effectively alleviate the class imbalance problem.
Reputation-Based Defense Scheme Against Backdoor Attacks on Federated Learning

Federated Learning enables thousands of participants to train a deep learning model together without divulging their local data. However, each participant may be hijacked by the attacker. Then those attacked clients themselves will produce the normal data set and the tampered data set embedded by the backdoor. A tampered data set embedded in a backdoor intentionally and incorrectly classifies, a sort of feature. Exception clients usually send large model weights to have a lasting impact. We propose a new method to detect abnormal clients, Mainly by setting up a high-quality data set on the central server-side, which we also call a test set, the parameters sent by the client compute losses on the test set, as well as losses on the local data set of the client. The total loss value on the server and client-side is added together, and then the credit value is calculated. In addition, we evaluate the outliers of the client through the calculation of the reputation value. For the client with low reputation value, we will reduce its weight, to reduce the influence of the abnormal client in the global model.
PaZoe: classifying time series with few labels

Semi-Supervised Learning (SSL) on graph-based datasets is a rapidly growing area of research, but its application to time series is difficult due to the time dimension. We propose a flexible SSL framework based on the stacking of PageRank, PCA and Zoetrope Genetic Programming algorithms into a novel framework: PaZoe. This self-labelling framework shows that graph-based and non-graph based algorithms jointly improve the quality of predictions and outperform each component taken alone. We also show that PaZoe outperforms state-of-the-art SSL algorithms on three time series datasets close to real world conditions. A first set was generated in house, taking data from industrial graded equipment in order to mimick DC motors during operation. Two other datasets, which include the recording of gestures, were taken from the public domain.
Hierarchical Services of Convolutional Neural Networks via Probabilistic Selective Encryption

Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them. In this work, we propose a selective encryption (SE) algorithm to protect CNN models from unauthorized access, with a unique feature of providing hierarchical services to users. Our algorithm firstly selects important model parameters via the proposed Probabilistic Selection Strategy (PSS). It then encrypts the most important parameters with the designed encryption method called Distribution Preserving Random Mask (DPRM), so as to maximize the performance degradation by encrypting only a very small portion of model parameters. We also design a set of access permissions, using which different amount of most important model parameters can be decrypted. Hence, different levels of model performance can be naturally provided for users. Experimental results demonstrate that the proposed scheme could effectively protect the classification model VGG19 by merely encrypting 8% parameters of convolutional layers. We also implement the proposed model protection scheme in the denoising model DnCNN, showcasing the hierarchical denoising services.
Unsupervised Context-Driven Question Answering Based on Link Grammar

While general conversational intelligence (GCI) can be considered one of the core aspects of artificial general intelligence (AGI), there currently exists minimal overlap between the disciplines of AGI and natural language processing (NLP). Only a few AGI architectures can comprehend and generate natural language, and most NLP systems rely either on hardcoded, specialized rules and frameworks that cannot generalize to the various complex domains of human language or on heavily trained deep neural network models that cannot be interpreted, controlled, or made sense of. In this paper, we propose an interpretable “Contextual Generator” architecture for question answering (QA), built as an extension of the recently published “Generator” algorithm for sentence generation, that produces grammatically valid answers to queries structured as lists of seed words. We demonstrate the potential for this architecture to perform automated, closed-domain QA by detailing results on queries from SingularityNET’s “small world” POC-English corpus and from the Stanford Question Answering Dataset. Overall, our work may bring a greater degree of GCI to proto-AGI NLP pipelines. The proposed QA architecture is open-source and can be found on GitHub under the MIT License at https://github.com/aigents/aigents-java-nlp .
Application of computer graphics and image processing technology in media in the new media era

With the continuous development and progress of modern science and technology, the application level of computer graphics and image processing technology in media has also been significantly improved in the new media era, and has played its due value in many industries. Now, the world is slowly entering the digital era. All walks of life are inseparable from the application of computers. The application of digital image processing technology is also widely used, such as segmentation technology, digital image processing technology and enhancement and restoration technology. This paper mainly describes the key technologies and related theories of digital image processing through in-depth exploration in all aspects, so as to provide a strong basis for future development.
Refined UNet V4: End-to-End Patch-Wise Network for Cloud and Shadow Segmentation with Bilateral Grid

Remote sensing images are usually contaminated by cloud and corresponding shadow regions, making cloud and shadow detection one of the essential prerequisites for processing and translation of remote sensing images. Edge-precise cloud and shadow segmentation remains challenging due to the inherent high-level semantic acquisition of current neural segmentation fashions. We, therefore, introduce the Refined UNet series to partially achieve edge-precise cloud and shadow detection, including two-stage Refined UNet, v2 with a potentially efficient gray-scale guided Gaussian filter-based CRF, and v3 with an efficient multi-channel guided Gaussian filter-based CRF. However, it is visually demonstrated that the locally linear kernel used in v2 and v3 is not sufficiently sensitive to potential edges in comparison with Refined UNet. Accordingly, we turn back to the investigation of an end-to-end UNet-CRF architecture with a Gaussian-form bilateral kernel and its relatively efficient approximation. In this paper, we present Refined UNet v4, an end-to-end edge-precise segmentation network for cloud and shadow detection, which is capable of retrieving regions of interest with relatively tight edges and potential shadow regions with ambiguous edges. Specifically, we inherit the UNet-CRF architecture exploited in the Refined UNet series, which concatenates a UNet backbone of coarsely locating cloud and shadow regions and an embedded CRF layer of refining edges. In particular, the bilateral grid-based approximation to the Gaussian-form bilateral kernel is applied to the bilateral message-passing step, in order to ensure the delineation of sufficiently tight edges and the retrieval of shadow regions with ambiguous edges. Our TensorFlow implementation of the bilateral approximation is relatively computationally efficient in comparison with Refined UNet, attributed to the straightforward GPU acceleration. Extensive experiments on Landsat 8 OLI dataset illustrate that our v4 can achieve edge-precise cloud and shadow segmentation and improve the retrieval of shadow regions, and also confirm its computational efficiency.