Reviewerly/exHarmony huggingface dataset:
Doc2KG

Document Management Systems (DMS) are used for decades to store large amounts of information in textual form. Their technology paradigm is based on storing vast quantities of textual information enriched with metadata to support searchability. However, this exhibits limitations as it treats textual information as black box and is based exclusively on user-created metadata, a process that suffers from quality and completeness shortcomings. The use of knowledge graphs in DMS can substantially improve searchability, providing the ability to link data and enabling semantic searching. Recent approaches focus on either creating knowledge graphs from document collections or updating existing ones. In this paper, we introduce Doc2KG (Document-to-Knowledge-Graph), an intelligent framework that handles both creation and real-time updating of a knowledge graph, while also exploiting domain-specific ontology standards. We use DIAVGEIA (clarity), an award winning Greek open government portal, as our case-study and discuss new capabilities for the portal by implementing Doc2KG.
DeepPatent: Large scale patent drawing recognition and retrieval

We tackle the problem of analyzing and retrieving technical drawings. First, we introduce DeepPatent, a new large-scale dataset for recognition and retrieval of design patent drawings. The dataset provides more than 350,000 design patent drawings for the purpose of image retrieval. Unlike existing datasets, DeepPatent provides fine-grained image retrieval associations within the collection of drawings and does not rely on cross-domain associations for supervision. We develop a baseline deep learning model, named Patent-Net, based on best practices for training retrieval models for static images. We demonstrate the superior performance of PatentNet when trained on our fine-grained associations of DeepPatent against other deep learning approaches and classic computer vision descriptors. With the introduction of this new dataset, and benchmark algorithms, we demonstrate that the analysis and retrieval of technical drawings remains an open challenge in computer vision; and that patent drawing retrieval provides a real-world testbench to spur research.
Real-Time FaaS: Towards a Latency Bounded Serverless Cloud

Today, Function-as-a-Service is the most promising concept of serverless cloud computing. It makes possible for developers to focus on application development without any system management effort: FaaS ensures resource allocation, fast response time, schedulability, scalability, resiliency, and upgradability. Applications of 5G, IoT, and Industry 4.0 raise the idea to open cloud-edge computing infrastructures for time-critical applications too, i.e., there is a strong desire to pose real-time requirements for computing systems like FaaS. However, multi-node systems make real-time scheduling significantly complex since guaranteeing real-time task execution and communication is challenging even on one computing node with multi-core processors. In this paper, we present an analytical model and a heuristic partitioning scheduling algorithm suitable for real-time FaaS platforms of multi-node clusters. We show that our task scheduling heuristics could outperform existing algorithms by 55%. Furthermore, we propose three conceptual designs to enable the necessary real-time communications. We present the architecture of the envisioned real-time FaaS platform, emphasize its benefits and the requirements for the underlying network and nodes, and survey the related work that could meet these demands.
Class-Balanced Active Learning for Image Classification

Active learning aims to reduce the labeling effort that is required to train algorithms by learning an acquisition function selecting the most relevant data for which a label should be requested from a large unlabeled data pool. Active learning is generally studied on balanced datasets where an equal amount of images per class is available. However, real-world datasets suffer from severe imbalanced classes, the so called long-tail distribution. We argue that this further complicates the active learning process, since the imbalanced data pool can result in suboptimal classifiers. To address this problem in the context of active learning, we proposed a general optimization framework that explicitly takes class-balancing into account. Results on three datasets showed that the method is general (it can be combined with most existing active learning algorithms) and can be effectively applied to boost the performance of both informative and representative-based active learning methods. In addition, we showed that also on balanced datasets our method <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> generally results in a performance gain.
Temporal-adaptive sparse feature aggregation for video object detection

Video object detection is a challenging task due to the appearance deterioration in video frames. To enhance feature representation of the deteriorated frames, previous methods usually aggregate features from fixed-density and fixed-length adjacent frames. However, due to the redundancy of videos and irregular object movements over time, temporal information may not be efficiently exploited using the traditional inflexible strategy. Alternatively, we present a temporal-adaptive sparse feature aggregation framework, an accurate and efficient method for video object detection. Instead of adopting a fixed-density and fixed-length window fusion strategy, a temporal-adaptive sparse sampling strategy is proposed using a stride predictor to encode informative frames more efficiently. A collaborative feature aggregation framework, which consists of a pixel-adaptive aggregation module and an object-relational aggregation module, is proposed for feature enhancement. The pixel-adaptive aggregation module enhances pixel-level features on the current frame using corresponding pixel-level features from other frames. Similarly, the object-relational aggregation module further enhances feature representation at proposal level. A graph is constructed to model the relations between different proposals so that the relation features and proposal features are adaptively fused for feature enhancement. Experiments demonstrate that our proposed framework significantly surpasses traditional dense aggregation methods, and comprehensive ablation studies verify the effectiveness of each proposed module in our framework.
Intelligent Traffic Signal Automation Based on Computer Vision Techniques Using Deep Learning

Traffic congestion in highly populated urban areas is a huge problem these days. A lot of researchers have proposed many systems to monitor traffic flow and handle congestion through different techniques. But the current systems are not reliable enough to perceive traffic signals in real-time. Therefore, we aim to build a system that can efficiently perform real-time environments to solve the traffic congestion problem through signal automation. Since vehicle detection and counting are crucial in any traffic system, we use state-of-the-art deep learning techniques to detect and count vehicles in real-time. We then automate the signal timings by comparing the count of traffic on all sides of a junction. These automated signal timings sufficiently reduce congestion and improve traffic flow. We prepared a dataset of 4500 images and achieved about 91% accuracy by training it on Faster RCNN.
An imbalanced learning method by combining SMOTE with Center Offset Factor

SMOTE is a well-known oversampling method for learning on imbalanced datasets. However, it has the risk of introducing noisy instances and overfitting problems. In order to improve its performance, this paper proposes an oversampling method called SMOTE-COF, which is an improvement of SMOTE based on center offset factor. The SMOTE-COF method first removes noisy samples, then computes center offset factor to select sparsely distributed minority class samples. Furthermore, these samples are used to generate new minority class samples with other minority class instances distributed in the same sub-cluster by SMOTE. Comparative experiments on one simulated dataset and fourteen UCI datasets provide evidence that the SMOTE-COF can effectively reduce noisy samples, generate better minority classes, and improve classification performance for imbalanced datasets.
An improved brain storm optimization algorithm with new solution generation strategies for classification

In recent years, brain storm optimization (BSO) algorithm has received much attention in solving classical optimization problems and is used to implement evolutionary classification models. However, in practical applications, large-scale datasets complicate the structure of the classification model, which can have a great impact on the classification performance. In the optimization process, the traditional single-strategy BSO cannot preserve the information of dominant solution well, and its generation strategy is inefficient in solving various complex practical problems. To solve this problem, we introduce feature selection to improve the optimization model structure. Meanwhile, in order to enhance the search capability of BSO, three new generation strategy are embedded in the BSO algorithm in this paper. With the three generation methods of global optimal, local optimal and nearest neighbor, the information of the dominant solution can be better preserved and the search efficiency can be improved. The performance of the proposed generation strategy in solving classification problems is demonstrated on ten datasets with different sizes and dimensions. The experimental results reveal that the new generation strategy can enhance the performance of BSO algorithm for solving classification problems.
Revisiting Local Descriptor for Improved Few-Shot Classification

Few-shot classification studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images. In this context, recent research efforts have been aimed at designing more and more complex classifiers that measure similarities between query and support images but left the importance of feature embeddings seldom explored. We show that the reliance on sophisticated classifiers is not necessary, and a simple classifier applied directly to improved feature embeddings can instead outperform most of the leading methods in the literature. To this end, we present a new method, named DCAP, for few-shot classification, in which we investigate how one can improve the quality of embeddings by leveraging Dense Classification and Attentive Pooling (DCAP) . Specifically, we propose to train a learner on base classes with abundant samples to solve dense classification problem first and then meta-train the learner on plenty of randomly sampled few-shot tasks to adapt it to few-shot scenario or the test time scenario. During meta-training, we suggest to pool feature maps by applying attentive pooling instead of the widely used global average pooling to prepare embeddings for few-shot classification. Attentive pooling learns to reweight local descriptors, explaining what the learner is looking for as evidence for decision making. Experiments on two benchmark datasets show the proposed method to be superior in multiple few-shot settings while being simpler and more explainable. Code is publicly available at https://github.com/Ukeyboard/dcap/ .
Dynamic Multi-projection Mapping Based on Parallel Intensity Control

Projection mapping using multiple projectors is promising for spatial augmented reality; however, it is difficult to apply it to dynamic scenes. This is because the conventional method decides all pixel intensities of multiple images simultaneously based on the global optimization method, and it is hard to reduce the latency from motion to projection. To mitigate this, we propose a novel method of controlling the intensity based on a pixel-parallel calculation for each projector in real-time with low latency. This parallel calculation leverages the insight that the projected pixels from different projectors in overlapping areas can be approximated independently if the pixel is sufficiently small relative to the surface structure. Additionally, our pixel-parallel calculation method allows a distributed system configuration, such that the number of projectors can be increased to form a network for high scalability. We demonstrate a seamless mapping into dynamic scenes at 360 fps with a 9.5-ms latency using ten cameras and four projectors.
Analysis on Item-Based and User-Based Collaborative Filtering for Movie Recommendation System

Recommender system are used to provide recommendations to users on all aspects technology and it is very important for every domain. There are different types of recommendation system are available such as Content Based, Hybrid Based, Collaborative filtering Based etc. Collaborative filtering-based Recommendation is divided into User-based and Item-based Collaborative filtering. The objective of the paper is to analyzed both the collaborative filtering recommendation method for a movie dataset. The outcome of User based and Item based recommendation method show the performance of both method and analyzing which one is provide good results.
The Online Hate Speech Cycle of Violence: Moderating Effects of Moral Disengagement and Empathy in the Victim-to-Perpetrator Relationship

Adolescents around the globe are increasingly exposed to online hate speech (OHS). And yet little is known about the varying roles of involvement and the determinants of adolescents' hate speech perpetration. Building on previous research, this study aims to test the cycle of violence hypothesis for OHS and to analyze whether moral disengagement (MD) and empathy moderate the victim-to-perpetrator relationship. The sample consists of 3,560 seventh to ninth graders (52.1 percent girls), recruited from 40 schools across Germany and Switzerland. Self-report questionnaires were administered to assess OHS involvement, MD, and empathy. Multilevel analyses revealed that victims of OHS were more likely to report OHS perpetration. In addition, victims of OHS were more likely to report OHS perpetration when they reported higher levels of MD than those with lower levels of MD. Finally, victims of OHS were less likely to report OHS perpetration when they reported higher levels of empathy than those with lower levels of empathy. The findings extend the cycle of violence hypothesis to OHS and highlight the need to address MD and empathy in hate speech prevention. Implications for future research will be discussed.
Sign Stochastic Gradient Descents without bounded gradient assumption for the finite sum minimization

Sign-based Stochastic Gradient Descents (Sign-based SGDs) use the signs of the stochastic gradients for communication costs reduction. Nevertheless, current convergence results of sign-based SGDs applied to the finite sum optimization are established on the bounded assumption of the gradient, which fails to hold in various cases. This paper presents a convergence framework about sign-based SGDs with the elimination of the bounded gradient assumption. The ergodic convergence rates are provided only with the smooth assumption of the objective functions. The Sign Stochastic Gradient Descent (signSGD) and its two variants, including majority vote and zeroth-order version, are developed for different application settings. Our framework also removes the bounded gradient assumption used in the previous analysis of these three algorithms.
Legitimate Surveillance of Suspicious Computation Offloading in Mobile Edge Computing Networks

In this paper, the legitimate surveillance of a suspicious mobile edge computing (MEC) network consisting of a suspicious edge server (SES) and multiple suspicious users (SUs), in the presence of a full-duplex monitor is studied. Each SU has a computation task to complete within a time deadline and can completely or partially offload the task to SES, while the monitor can either jam or assist the suspicious communications during task uploading and result downloading. With the heterogeneous offloading model adopted by the SUs, the problem of optimizing the monitor mode and transmit power to maximize the average ratio of successfully eavesdropped tasks, subject to the monitor transmit power constraint and the task completion time deadline constraint is investigated. The problem is solved via exploring the particular problem structure and adopting the sum-of-ratios optimization. Simulation results show that the proposed algorithm significantly outperforms the benchmark algorithms, especially for the SUs with partial offloading. It is also shown that the proposed algorithm is of low-complexity and achieves almost the same performance as the high-complexity optimal algorithm. Besides, compared to the SUs with binary offloading, the eavesdropping performance for the SUs with partial offloading is shown to be much better.
Gold prospectivity mapping and exploration targeting in Hutti-Maski schist belt, India: Synergistic application of Weights-of-Evidence (WOE), Fuzzy Logic (FL) and hybrid (WOE-FL) models

Our study attempts to map gold prospectivity for deriving optimal exploration targets using data-driven, knowledge-driven and hybrid approaches. Prospectivity models viz. Weights of Evidence (WOE), Fuzzy Logic (FL) and a hybrid model that combines the data-driven and knowledge-driven components (WOE-FL) were applied to a part of the auriferous Hutti-Maski schist belt of 1352 km2 area with 20 known gold occurrences. With a pixel resolution of 500 m, 16 spatial evidential raster layers were created on a GIS platform encompassing viable predictive indicators, critical in gold exploration. Modelling inputs include essential ingredients and mappable criteria of the conceived orogenic gold mineral system in the study area. Multi-source geological data such as lithostratigraphic units, favourable litho-contacts, structural deformation sites, geochemical anomalies of selected gold pathfinder elements, and hydrothermal alteration zones derived from digital image processing of Landsat 8 OLI satellite imagery were integrated to generate prospectivity maps for delineation of future targets. A quantitative evaluation of the resulting three prospectivity maps was performed using concentration-area (C-A) fractal analysis, prediction–area (P–A) plot, fitting-rate curve (FRC) and area under curve (AUC). Comparative analysis indicates that the performance of the hybrid model (WOE-FL) stands out to be the most efficient, with a prediction rate of 87% and AUC of 91.40% compared to WOE and FL. A risk assessment was performed combining the outputs of prospectivity models that returned 10% of the study area as potential exploration targets out of which the low-risk exploration targets comprises merely 4.5% representing the optimal targets for gold exploration in the study area.
Short-term PV power forecasting in India: recent developments and policy analysis

Abstract With ambitious renewable energy capacity addition targets, there is an ongoing transformation in the Indian power system. This paper discusses the various applications of variable generation forecast, state-of-the-art solar PV generation forecasting methods, latest developments in generation forecasting regulations and infrastructure, and the new challenges introduced by VRE generation. Day-ahead NWP-based GHI forecasting are validated against ground measurements from single and multiple sites in India. Recommendations for improving overall the forecasting infrastructure in India are presented.
A probabilistic data analytics methodology based on Bayesian Belief network for predicting and understanding breast cancer survival

Understanding breast cancer survival has proven to be a challenging problem for practitioners and researchers. Identifying the factors affecting cancer progression, their interrelationships, and their influence on patients’ long-term survival helps make timely treatment decisions. The current study addresses this problem by proposing a Tree-Augmented Bayesian Belief Network (TAN)-based data analytics methodology comprising of four steps: data acquisition and preprocessing, variable selection via Genetic Algorithm (GA), data balancing with synthetic minority over-sampling and random under-sampling methods, and finally the development of the TAN model to determine the probabilistic inter-conditional dependency structure among breast cancer-related variables along with the posterior survival probabilities The proposed model is compared to well-known machine learning models. A what-if analysis has also been conducted to verify the associations among the variables in the TAN model. The relative importance of each variable has been investigated via sensitivity analysis. Finally, a decision support tool is developed to further explore the conditional dependency structure among the cancer-related factors. The results produced by the proposed methodology, namely the patient-specific posterior survival probabilities and the conditional relationships among the variables, can be used by healthcare professionals and physicians to improve the decision-making process in planning and managing breast cancer treatments. Our generic methodology can also accommodate other types of cancer and be applied to manage various medical procedures.
Not all datasets are born equal: On heterogeneous tabular data and adversarial examples

Recent work on adversarial learning has mainly focused on neural networks and domains in which those networks excel, such as computer vision and audio processing. Typically, the data in those domains is homogeneous, whereas domains with heterogeneous tabular datasets remain underexplored, despite their prevalence. When searching for adversarial patterns within heterogeneous input spaces, an attacker must simultaneously preserve the complex domain-specific validity rules of the data and the adversarial nature of the identified samples. As such, applying adversarial manipulations to heterogeneous datasets has proven challenging, and a generic attack method has not yet been proposed. However, this study argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data, such as images. To support this claim, a generic optimization framework for identifying adversarial perturbations in heterogeneous input spaces is introduced. The framework defines distribution-aware constraints to preserve the consistency of the adversarial examples and then incorporate them by embedding the heterogeneous input into a continuous latent space. Due to the nature of the underlying datasets, we focus on ℓ0 perturbations and demonstrate their applicability in real life. The effectiveness of the suggested approach is demonstrated using three datasets from different content domains. The results show that despite the constraints imposed on the input validity in heterogeneous datasets, machine learning models trained using such data are still susceptible to adversarial examples.