Reviewerly/exHarmony huggingface dataset:
Automated Graph Learning via Population Based Self-Tuning GCN

Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classification. Traditional GCN models suffer from the issues of overfitting and oversmoothing, while some recent techniques like DropEdge could alleviate these issues and thus enable the development of deep GCN. However, training GCN models is non-trivial, as it is sensitive to the choice of hyperparameters such as dropout rate and learning weight decay, especially for deep GCN models. In this paper, we aim to automate the training of GCN models through hyperparameter optimization. To be specific, we propose a self-tuning GCN approach with an alternate training algorithm, and further extend our approach by incorporating the population based training scheme. Experimental results on three benchmark datasets demonstrate the effectiveness of our approaches on optimizing multi-layer GCN, compared with several representative baselines.
Variable Interval Time Sequence Modeling for Career Trajectory Prediction: Deep Collaborative Perspective

In today's fast-evolving job market, the timely and effective understanding of the career trajectories of talents can help them quickly develop necessary skills and make the right career transitions at the right time. However, it is a non-trivial task for developing a successful career trajectory prediction method, which should have the abilities for finding the right timing for job-hopping, identifying the right companies, and matching the right positions for the candidates. While people have been trying to develop solutions for providing some of the above abilities, there is no total solution or complete framework to integrate all these abilities together. To this end, in this paper, we propose a unified time-aware career trajectory prediction framework, namely TACTP, which is capable of jointly providing the above three abilities for better understanding the career trajectories of talents. Along this line, we first exploit a hierarchical deep sequential modeling network for career embedding and extract latent talent factors from multiple networks, which are designed with different functions of handling related issues of the timing, companies, and positions for job-hopping. Then, we perform collaborative filtering for generating personalized predictions. Furthermore, we propose a temporal encoding mechanism to handle dynamic temporal information so that TACTP is capable of generating time-aware predictions by addressing the challenges for variable interval time sequence modeling. Finally, we have conducted extensive experiments on large-scale real-world data to evaluate TACTP against the state-of-the-art baselines, and the results show that TACTP has advantages over baselines on all targeted tasks for career trajectory prediction.
Machine Learning Algorithms and their Real-life Applications: A Survey

Machine learning (ML) is nothing but the study based on arithmetical and non-arithmetical methods for refining outcome and performance by systematizing the attainment of know how and knowledge from understanding and experience. From last several years, Machine Learning has progressed from struggle of limited computer devotees using the option of computers learning for basic statistics/mathematics which occasionally reflected computational methods, playing games, to a liberated discipline of research which has presented required platform for mathematical analysis, computational principles of learning techniques and has also established several algorithms which are commonly utilized for pattern recognition, analysis of text, explanation/interpretation and plenty of additional money-making drives and have managed to a distinct research curiosity in field of data mining to explore concealed consistencies or anomalies in social data which is emergent every minute. Continual efforts, growth, progressive refinement and improvement in ML has enabled the successful production of thousands of AI expert systems which are presently being used commonly in business and industries. This work emphasis on elucidating the idea along with fruition of ML, few prevalent ML algorithms and their actual life applications.
Adversarial scale-adaptive neural network for crowd counting

Crowd counting aims to count the number of pedestrians in an image or a video. Currently, scale variations in crowd counting are inevitable and challenging in practice. Besides, for the crowd counting task, there is only a small amount of annotated data available. It can be seen from previous methods of crowd counting and object detection that the two tasks have similar attention areas. However, existing methods in crowd counting generally ignore the similarities and specialties between the crowd counting task and the object detection task. In this paper, in order to solve the above challenges, we propose an adversarial scale-adaptive neural network (ASANet), consisting of three branches. First, a private branch for the crowd counting task concentrates on generating high-quality density maps. Second, another private branch for the object detection task aims to correctly detect and recognize objects. Third, we design a common branch to learn the similar attention area of the two tasks and assist crowd counting. Experimental results demonstrate an outstanding performance of the ASANet over state-of-the-art methods on three public datasets (ShanghaiTech, UCF_CC_50, and UCF_QNRF).
WiseKG: Balanced Access to Web Knowledge Graphs

SPARQL query services that balance processing between clients and servers become more and more essential to handle the increasing load for open and decentralized knowledge graphs on the Web. To this end, Linked Data Fragments (LDF) have introduced a foundational framework that has sparked research exploring a spectrum of potential Web querying interfaces in between server-side query processing via SPARQL endpoints and client-side query processing of data dumps. Current proposals in between typically suffer from imbalanced load on either the client or the server. In this paper, to the best of our knowledge, we present the first work that combines both client-side and server-side query optimization techniques in a truly dynamic fashion: we introduce WiseKG, a system that employs a cost model that dynamically delegates the load between servers and clients by combining client-side processing of shipped partitions with efficient server-side processing of star-shaped sub-queries, based on current server workload and client capabilities. Our experiments show that WiseKG significantly outperforms state-of-the-art solutions in terms of average total query execution time per client, while at the same time decreasing network traffic and increasing server-side availability.
Towards socialVR: evaluating a novel technology for watching videos together

Social VR enables people to interact over distance with others in real-time. It allows remote people, typically represented as avatars, to communicate and perform activities together in a shared virtual environment, extending the capabilities of traditional social platforms like Facebook and Netflix. This paper explores the benefits and drawbacks provided by a lightweight and low-cost Social VR platform (SocialVR), in which users are captured by several cameras and reconstructed in real-time. In particular, the paper contributes with (1) the design and evaluation of an experimental protocol for Social VR experiences; (2) the report of a production workflow for this new type of media experiences; and (3) the results of experiments with both end-users (N = 15 pairs) and professionals (N = 22 companies) to evaluate the potential of the SocialVR platform. Results from the questionnaires and semi-structured interviews show that end-users rated positively towards the experiences provided by the SocialVR platform, which enabled them to sense emotions and communicate effortlessly. End-users perceived the photo-realistic experience of SocialVR similar to face-to-face scenarios and appreciated this new creative medium. From a commercial perspective, professionals confirmed the potential of this communication medium and encourage further research for the adoption of the platform in the commercial landscape.The online version contains supplementary material available at 10.1007/s10055-022-00651-5.
#Twiti: Social Listening for Threat Intelligence

Twitter is a popular public source for threat hunting. Many security vendors and security professionals use Twitter in practice for collecting Indicators of Compromise (IOCs). However, little is known about IOCs on Twitter. Their important characteristics such as earliness, uniqueness, and accuracy have never been investigated. Moreover, how to extract IOCs from Twitter with high accuracy is not obvious. In this paper, we present Twiti, a system that automatically extracts various forms of malware IOCs from Twitter. Based on the collected IOCs, we conduct the first empirical assessment and thorough analysis of malware IOCs on Twitter. Twiti extracts IOCs from tweets identified as having malware IOC information by leveraging natural language processing and machine learning techniques. With extensive evaluation, we demonstrate that not only can Twiti extract malware IOCs accurately, but also the extracted IOCs are unique and early. By analyzing IOCs in Twiti from various aspects, we find that Twitter captures ongoing malware threats such as Emotet variants and malware distribution sites better than other public threat intelligence (TI) feeds. We also find that only a tiny fraction of IOCs on Twitter come from commercial vendor accounts and individual Twitter users are the main contributors of the early detected or exclusive IOCs, which indicates that Twitter can provide many valuable IOCs uncovered in commercial domain
Generalized Zero-shot Intent Detection via Commonsense Knowledge

Identifying user intents from natural language utterances is a crucial step in conversational systems that has been extensively studied as a supervised classification problem. However, in practice, new intents emerge after deploying an intent detection model. Thus, these models should seamlessly adapt and classify utterances with both seen and unseen intents -- unseen intents emerge after deployment and they do not have training data. The few existing models that target this setting rely heavily on the training data of seen intents and consequently overfit to these intents, resulting in a bias to misclassify utterances with unseen intents into seen ones. We propose RIDE: an intent detection model that leverages commonsense knowledge in an unsupervised fashion to overcome the issue of training data scarcity. RIDE computes robust and generalizable relationship meta-features that capture deep semantic relationships between utterances and intent labels; these features are computed by considering how the concepts in an utterance are linked to those in an intent label via commonsense knowledge. Our extensive experimental analysis on three widely-used intent detection benchmarks shows that relationship meta-features significantly improve the detection of both seen and unseen intents and that RIDE outperforms the state-of-the-art models.
Embedded Architecture Composed of Cognitive Agents and ROS for Programming Intelligent Robots

This paper proposes and evaluates an embedded architecture aimed to promote the utilization of cognitive agents in cooperation with the Robotic Operating System (ROS), serving as an alternative for programming intelligent robots. It promotes the programming abstraction level in two directions. The first direction regards using cognitive agents facilities for programming the robots intelligence, consisting of its perceptions and related actions. The second direction exploits the facilities of using ROS layers for programming the robot interaction with its sensors and actuators. The paper reports experiments of using agents to command simulated UAVs while measuring performance metrics that allowed us to evaluate the benefits of the proposed architecture.
People Detecting and Counting System

In present days, people detecting and counting is an important aspect in the video investigation and subjective demand in computer vision systems. For many applications, it is necessary to identify people and then accurately count the number of people in real time or near real time. To perform people counting, a robust system for people detection is needed. The system is designed to be able to calculate the number of people entering and exiting a room. The system is implemented using a raspberry pi camera to capture images, laptop to train a model for specific dataset, and raspberry pi 3 model B to apply the model, count detected person and send the number of people going in and out of room to the web server. Information from people counting support the retail shops to analyze customer visit patterns. In addition, it can also give useful information in the implementation of internet of things for smart rooms or smart buildings such as automation of room lights.
New efficient algorithms for the centroid of an interval type-2 fuzzy set

In a type-2 fuzzy logic system , one of the important operations is to calculate the centroid of an interval type-2 fuzzy set (IT2 FS). In this paper, two novel algorithms called binary algorithms are proposed to calculate the centroid of IT2 FSs. Then, the outputs of the proposed binary algorithms are proven to be the optimal values. After analyzing the computational complexities of Karnik-Mendel (KM) algorithms, enhanced Karnik-Mendel (EKM) algorithms, enhanced iterative algorithms based on stopping condition (EIASC) algorithms and the proposed binary algorithms, it is found that the proposed binary algorithms are superior to the KM algorithms, EKM algorithms and EIASC algorithms. Finally, two extended binary algorithms are proposed to compute the centroid of an IT2 FS. The efficiencies of the proposed binary algorithms and extended binary algorithms are demonstrated by extensive simulations.
End-to-end keyword search system based on attention mechanism and energy scorer for low resource languages

Keyword search (KWS) means searching for keywords given by the user from continuous speech. Conventional KWS systems are based on Automatic Speech Recognition (ASR), where the input speech has to be first processed by the ASR system before keyword searching. In the recent decade, as deep learning and deep neural networks (DNN) become increasingly popular, KWS systems can also be trained in an end-to-end (E2E) manner. The main advantage of E2E KWS is that there is no need for speech recognition, which makes the training and searching procedure much more straightforward than the traditional ones. This article proposes an E2E KWS model, which consists of four parts: speech encoder–decoder, query encoder–decoder, attention mechanism, and energy scorer. Firstly, the proposed model outperforms the baseline model. Secondly, we find that under various supervision, character or phoneme sequences, speech or query encoders can extract the corresponding information, resulting in different performances. Moreover, we introduce an attention mechanism and invent a novel energy scorer, where the former can help locate keywords. The latter can make final decisions by considering speech embeddings, query embeddings, and attention weights in parallel. We evaluate our model on low resource conditions with about 10-hour training data for four different languages. The experiment results prove that the proposed model can work well on low resource conditions.
Noise Robust Face Hallucination Based on Smooth Correntropy Representation

Face hallucination technologies have been widely developed during the past decades, among which the sparse manifold learning (SML)-based approaches have become the popular ones and achieved promising performance. However, these SML methods always failed in handling noisy images due to the least-square regression (LSR) they used for error approximation. To this end, we propose, in this article, a smooth correntropy representation (SCR) model for noisy face hallucination. In SCR, the correntropy regularization and smooth constraint are combined into one unified framework to improve the resolution of noisy face images. Specifically, we introduce the correntropy induced metric (CIM) rather than the LSR to regularize the encoding errors, which admits the proposed method robust to noise with uncertain distributions. Besides, the fused LASSO penalty is added into the feature space to ensure similar training samples holding similar representation coefficients. This encourages the SCR not only robust to noise but also can well exploit the inherent typological structure of patch manifold, resulting in more accurate representations in noise environment. Comparison experiments against several state-of-the-art methods demonstrate the superiority of SCR in super-resolving noisy low-resolution (LR) face images.
Robust dense light field reconstruction from sparse noisy sampling

• A unified light field reconstruction framework for sparse noisy inputs. • A scheme for suppressing noise propagation and improving the noise tolerance. • A residual-learning based noise filtering for the blind denoising. • A complementary-learning based view rendering from corrupted candidate views. Reconstructing densely-sampled light fields has attracted extensive attention recently. Numerous methods have been developed on the basis of various geometric characteristics of light field. However, these methods lose sight of the potential impact of input noise. Noise is ubiquitous in light field imaging of real-world scenes. The noise presented in the input views can propagate into the subsequent stages, and can significantly degrade the performance of the light field reconstruction. To remedy this problem, we propose a unified learning framework that learns the noise-invariant representations of light field, and reconstructs a clean densely-sampled light field from sparse noisy sampling. The novelty of this paper is the scheme to suppress input noise propagation and to improve the noise tolerance of view synthesis. Three convolutional sub-networks are jointly designed for this task: an encoder-decoder based unsupervised learning for the noise-independent estimation of light field depth, a residual learning based noise filtering for the unsupervised blind denoising of the sparse noisy views, and a complementary learning based rendering for the clean virtual view synthesis. We have made a comprehensive evaluation on 5 public datasets under different types of noise. The visual and quantitative comparisons demonstrate that our method not only exhibits significant superiority in noise tolerance, but also achieves excellent light field reconstruction performance.
Deep Fusion Driven Semantic Segmentation for the Automatic Recognition of Concealed Contraband Items

Automatic detection of prohibited items in passenger baggage is a challenging task, especially in cluttered and occluded concealment scenarios. In this paper, we present a deep fusion driven semantic segmentation network that leverages multi-scale feature representations (extracted via CNN backbone) to generate highly accurate segmentation masks of the suspicious items irrespective of the clutter and concealment. Assessed with the public GDXray, SIXray and OPIXray datasets our proposed architecture reached a mean IoU performance of 0.7768, 0.6263, and 0.6713 respectively, outperforming the leading frameworks.
Integration of airborne geophysics and satellite imagery data for exploration targeting in porphyry Cu systems: Chahargonbad district, Iran

ABSTRACT This study illustrates the application of a geometric average integration of aeromagnetic, radiometric and satellite imagery data over a region prone to Cu‐bearing mineralization at Chahargonbad area in Kerman province of Iran. Processing aeromagnetic, radiometric and Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data can provide exploratory insights about favourable zones in association with porphyry‐type ore occurrences, which can be synthesized through a combination of knowledge‐ and data‐driven approaches as a geometric average and be represented in a mineral prospectivity map. The existence of known deposits in a prospect region can facilitate the investigation of significant exploratory footprints extracted from airborne data by calculating each indicator layer's weight by plotting a prediction–area curve accompanied by a concentration–area fractal curve. Among various indicators, the most important ones are determined based on derived weights from the prediction–area plots to be synthesized in a single Cu favorability map. To fulfil this aim, indicator layers from airborne geophysics (magmatic bodies, magnetic lineaments and potassium radiometry) and remote‐sensing data (alterations such as argillic, phyllic, propylitic and iron oxide along with geological lineaments) were prepared and evaluated using the known porphyry Cu mineralization by the simultaneous plot of the concentration–area fractal model and the prediction–area curve to attain the ore prediction rate and the relevant occupied area of each map for weight assignment of indicators. The geometric average prospectivity model was applied to synthesize the leading indicators, and the result was compared with a multi‐class index overlay map. This study's significance lies in improvement of the performance of the mineral prospectivity/potential mapping after running a geometric average by a higher ore prediction rate of 79%, which has occupied 21% of the area as potential zones for further mining investigations.
Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing

We proposes a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks. The key to DiOr is a novel recurrent generation pipeline to sequentially put garments on a person, so that trying on the same garments in different orders will result in different looks. Our system can produce dressing effects not achievable by existing work, including different interactions of garments (e.g., wearing a top tucked into the bottom or over it), as well as layering of multiple garments of the same type (e.g., jacket over shirt over t-shirt). DiOr explicitly encodes the shape and texture of each garment, enabling these elements to be edited separately. Joint training on pose transfer and inpainting helps with detail preservation and coherence of generated garments. Extensive evaluations show that DiOr outperforms other recent methods like ADGAN [28] in terms of output quality, and handles a wide range of editing functions for which there is no direct supervision.