Reviewerly/exHarmony huggingface dataset:
Visualizing Worklog Based on Human Working Activity Recognition Using Unsupervised Activity Pattern Encoding

Wearable motion sensor-based complex activity recognition during working hours has recently been studied to evaluate and thereby improve worker productivity. In the application of this technique to practical fields, one of the biggest challenges is performing time-consuming modeling tasks such as data labeling and hand-crafted feature extraction. One way to enable faster modeling is to decrease the time required for the manual tasks by making use of unlabeled motion datasets and the characteristics of complex activities. In this study, we propose a working activity recognition method that combines unsupervised encoding of the activity patterns of motions (denoted as "atomic activities"), the representation of working activities by combination of atomic activities, and the integration of additional information such as sensor time. We evaluated our method using an actual dataset from the caregiving field and found that it had an equivalent recognition performance (70.3% macro F-measure) to conventional hand-crafted feature extraction method. This is also comparable to that of previous methods using large labeled datasets. We also found that our method could visualize daily work processes with the accuracy of 71.2%. These results indicate that the proposed method has the potential to contribute to the rapid implementation of working activity recognition in actual working fields.
Generating X-ray Images from Point Clouds Using Conditional Generative Adversarial Networks

Simulating medical images such as X-rays is of key interest to reduce radiation in non-diagnostic visualization scenarios. Past state of the art methods utilize ray tracing, which is reliant on 3D models. To our knowledge, no approach exists for cases where point clouds from depth cameras and other sensors are the only input modality. We propose a method for estimating an X-ray image from a generic point cloud using a conditional generative adversarial network (CGAN). We train a CGAN pix2pix to translate point cloud images into X-ray images using a dataset created inside our custom synthetic data generator. Additionally, point clouds of multiple densities are examined to determine the effect of density on the image translation problem. The results from the CGAN show that this type of network can predict X-ray images from points clouds. Higher point cloud densities outperformed the two lowest point cloud densities. However, the networks trained with high-density point clouds did not exhibit a significant difference when compared with the networks trained with medium densities. We prove that CGANs can be applied to image translation problems in the medical domain and show the feasibility of using this approach when 3D models are not available. Further work includes overcoming the occlusion and quality limitations of the generic approach and applying CGANs to other medical image translation problems.
Conceptualizing the Assimilation and Risk of Online Social Media in Saudi Arabia: An Empirical Study

This chapter examines the assimilation and risk of online social media (OSM) in Saudi Arabia. It seeks to identify the nomological set of antecedents that can explain the variation in the assimilation of OSM and the impact of OSM on the Saudi community. Using the diffusion of innovation theory, the institutional theory, and qualitative data of six case studies (experts of social, media, and regulatory institutions), this chapter proposes a theoretical framework that can be used to explain the factors that could influence the assimilation of OSM and its impact on the Saudi community. A series of theoretical propositions are proposed. The results indicate that several individual, technological, and institutional factors were identified as antecedents for the successful assimilation of OSM. They include personal innovativeness, risk appetite, relative advantages, compatibility, complexity, perceived uncertainty, normative pressure, coercive pressure, and mimetic pressure. The results also show that the assimilation of OSM is likely to be associated with risks, including impact on personality attributes, depression level, productivity level, social values, family structure, public transparency, and religious information.
Load Balancing of Tasks in Cloud Computing Using Fault-Tolerant Honey Bee Foraging Approach

Cloud computing is playing a vital role in the IT industries nowadays due to its enormous advantages of easy unlimited access to cloud services at any time, from any location with portability feature and reduced cost on hiring basis. The provision of various cloud computing services is facing challenges to implement efficient load balancing algorithms with fault tolerance facility in recent technology arena. In this paper, we proposed an algorithm of load balancing of tasks using honey bee approach with fault tolerance architecture. We have used CloudSim to simulate the algorithm and have done a comparative study with the existing one.
Maximal Coordinate Discrepancy as Accuracy Criterion of Image Projective Normalization for Optical Recognition of Documents

Application of projective normalization (a special case of orthocorrection and perspective correction) to photographs of documents for their further optical recognition is generally accepted.In this case, inaccuracies of normalization can lead to recognition errors.To date, a number of normalization accuracy criteria are presented in the literature, but their conformity with recognition quality was not investigated.In this paper, for the case of a fixed structured document, we justify a uniform probabilistic model of recognition errors, according to which the probability of correct recognition of a character abruptly falls to zero with an increase in the coordinate discrepancy of this character.For this model, we prove that the image normalization accuracy criterion, which is equal to the maximal coordinate discrepancy in the text fields of a document, monotonously depends on the probability of correct recognition of the entire document.Also, we show that the problem on computing the maximal coordinate discrepancy is not reduced to the nearest known one, i.e. the linearfractional programming problem.Finally, for the first time, we obtain an analytical solution to the problem on computing the maximal coordinate discrepancy on a union of polygons.
FINET: Fine-grained Feature Interaction Network for Click-through Rate Prediction

Click-Through Rate prediction (CTR) is an critical task in recommender systems and computing advertisements because the prediction accuracy affects the user experience and the revenue of merchants. The instances in the CTR prediction task are usually sparse and high-dimensional. In real scenarios, an effective prediction relies on feature engineering which is difficult and requires domain knowledge. With the great success of deep neural networks (DNNs) in various fields, many models have been proposed to extract useful and important feature interactions from high-dimensional and sparse features. However, most models extract the feature interactions in a simple manner, and there is no comprehensive extraction of fine-grained and high-order feature interactions. In this paper, we propose a novel Fine-grained feature Interaction Network (FINET), which can automatically learn finegrained and high-order feature combinations of input features. We first use convolutional neural network (CNN) mechanism which leverages the fine-grained feature interactions to generate higher-order feature interactions. Then useful and important features can be selected through attention mechanism to further reduce the difficulty of learning a deep neural network. We conduct comprehensive experiments on two real-world datasets to demonstrate the effectiveness of FINET.
Clustering Anchor for Faster R-CNN to Improve Detection Results

Object detection has made impressive progress in recent years where Faster R-CNN is the mainstream framework for region-based object detection methods. However, a single Faster R-CNN framework no longer has advantages compared with the latest detection models. So based on Faster R-CNN, a model that focuses on features, normalization methods, and anchor sizes is proposed to improve detection results. The model integrates Feature Pyramid Networks (FPN), Group Normalization (GN) with k-means clustering. FPN is used to produce a multi-scale feature representation, which enables the model to detect objects across a wide range of scales. GN addresses the problem of the small training batch size effectively. K-means clustering algorithm is used finally to determine anchor sizes of the network on the purpose of making the network do bounding-box regression more easily. Without bells and whistles, the detection model achieves state-of-the-art object detection accuracy on the MSCOCO datasets.
Spatio-temporal Collaborative Convolution for Video Action Recognition

Although video action recognition has achieved great progress in recent years, it is still a challenging task due to the huge computational complexity. Designing a lightweight network is a feasible solution, but it may reduce the spatio-temporal information modeling capability. In this paper, we propose a novel novel spatio-temporal collaborative convolution (denote as “STC-Conv”), which can efficiently encode spatio-temporal information. STC-Conv collaboratively learn spatial and temporal feature in one convolution filter kernel. In short, temporal convolution and spatial convolution are integrated in the one STC convolution kernel, which can effectively reduce the model complexity and improve the computational efficiency. STC-Conv is a universal convolution, which can be applied to the existing 2D CNNs, such as ResNet, DenseNet. The experimental results on the temporal-related dataset Something Something V1 prove the superiority of our method. Noticeably, STC-Conv enjoys more excellent performance than 3D CNNs at even lower computation cost than standard 2D CNNs.
A Machine Learning Assistant for Choosing Operators and Tuning Their Parameters in Image Processing Tasks

Operator choosing and parameter tuning in image processing tasks is still to be a challenging problem to achieve good results. This paper discusses the formulation of a solution combining both machine learning and multi-agent system to help users to choose best operators and attribute appropriate values to their parameters in image processing applications. The aspect of cooperative learning makes this solution faster and outperforms one agent learning. The empirical study shows that our solution is effective on finding the optimal vision operators and their parameters’ values.
Sparse Gaussian Process Based On Hat Basis Functions

Gaussian process is a popular non-parametric Bayesian methodology for modeling the regression problem, which is completely determined by its mean and covariance function. Nevertheless, this method still has two major disadvantages: it is difficult to handle large datasets and may not meet inequality constraints in specific problems. These two issues have been addressed by the so-called sparse Gaussian process and constrained Gaussian process in recent years. In this paper, to reduce the overall computational complexity in the exact Gaussian process, we propose a new sparse Gaussian process method to solve the unconstrained regression problem. The idea is inspired by the constrained Gaussian process method. The critical point of our method is that we introduce the hat basis function, which is mentioned in the constrained Gaussian process, and modify its definition according to the range of training or test data. It turns out that this method belongs to the spectral approximation methods. Similar to the exact Gaussian process and Gaussian process with Fully Independent Training Conditional approximation, our method obtains satisfactory approximate results on analytical functions or open-source datasets.
Hardware Security Reconnaissance Application using FCC ID Lookup and Computer Vision

The reconnaissance process is a time consuming and risky task for hardware researchers and pen-testers. First, the exterior of the device is assessed and then opened to inspect the interior. The device can be seriously damaged due to this and cannot be reused. With the help of Computer Vision - Optical Character Recognition with Machine Learning, implemented in the app, one has to click on the text containing FCC ID real-time and is directed to the site containing all the required information. This saves time and the information of the device is readily available without the need to open up the device. FCC ID is considered as one of the protocols by the pen-testers.
Edge Detection Canny Algorithm Using Adaptive Threshold Technique

Detection of edge is most basic operations which is needed in processing of objects in image processing identification. Hence, edge detection is the most likely operation for the processing of image in real-time applications with optimized results which is accurate, and architecture with less complexity results in less latency. Hence, edge detection with adaptive threshold technique plays a vital role in present-day edge detection techniques. The computation is carried with threshold values which are automatically adopted according to the image specification which helps to reduce the memory and computations along with decision-making will take less time. Hence, delay gets reduced with improved detection performance along with increased efficiency. The proposed architecture is implemented using Xilinx system generator tool on Spartan6 ATLYS board.
A Real-time Multipoint-based Object Detector

A real-time multipoint-based object detector - EMPDet is proposed in this paper to improve the processing speed with reasonable sacrifice in accuracy. A lightweight neural network block is proposed and integrated into the compact hourglass networks to reduce the consumption in image feature extraction. The channel mechanism is used to enhance the performance of the convolutional neural network to screen shallow semantic information in high-resolution feature maps. Experiments results on the detection benchmark (Microsoft COCO) show that the proposed detector has superior performance compared to the current most popular YOLOv3 under reasonable overhead.
SmartDFRelevance: A Holonic Agent Based System for Engineering Industrial Projects in Concurrent Engineering Context

In the era of industry 4.0, the design of complex engineered systems is a challenging mission, and most researchers would argue that it is linked to intentional action and it cannot emerge out of complexity. In fact, to achieve the common 'cost/quality/delay' targets, engineers must work together to create the best overall solution from their individual components, and exploit and regenerate the required intellectual capital (Knowledge). Therefore, to be effective, we need a knowledge based system that takes into consideration all the users' needed methods to create share reuse and evaluate knowledge. Thus, using complex adaptive system theory, mainly Distributed Artificial Intelligence (holonic agent paradigm), a new knowledge-based system can be designed to address this research issue. In this regard, the purpose of this paper is first to provide a comparative analysis of the appropriate method that considers the global challenges for designing a product in a concurrent engineering context. Second, a holonic multi-agent system, called SmartDFRelevance is proposed based on Agent-oriented Software Process for Complex Engineering Systems methodology.
P1000 Induced Brain Signal Analysis for Assessing Subjective Pain Sensitivity using Type-2 Fuzzy Classifier

This paper intends to develop a novel methodology that helps to determine the variation of pain perception across various individuals using EEG signal analysis. Three types of touch stimuli: heat, bristles and pinch with varying intensity levels are utilized for the experiment. The brain signals acquired are analyzed using eLORETA software that confirms the involvement of frontal and parietal lobes for this cognitive activity. Additionally, frequency analysis undertaken infers the participation of alpha and theta bands for the said task. The signals are further evaluated to inspect the existence of any Event Related Potential (ERP) signal. A unique and notable ERP signal has been found when a subject finds the perceived stimuli to be painful. However, no relevant ERP component is generated when the subject finds the presented stimuli to be completely painless. A novel Interval Type-2 fuzzy classifier has been designed to classify these two distinct conditions (painful and non-painful). Performance analysis undertaken confirms the superlative behaviour of the proposed classifier with respect to other standard ones. Moreover, statistical evaluation also assures the superior performance of the proposed classifier model. Hence, this method can act as a neuronal marker to detect an individual's pain sensitivity that can be used to diagnose and treat various neurological disorders and chronic pain based diseases.
InvUnet:Inverse the Unet for Nuclear Segmentation in H&amp;E Stained Images

Nuclear segmentation is the starting point for most cancer pathological analysis. Recently,A series of Unet-like deep neural networks have achieved good result in this task. These models are variants of encoder-decoder architecture and can be traced from FCN, which is designed for semantic segmentation task of natural images. However, compared with natural images, the target regions of H&E images are more salient and have lower entropy. In light of the characteristics of H&E stained images and inspired by the two-stage framework of traditional pure image processing methods, we propose an end-to-end network named InvUnet, which considers nuclear segmentation as a bottom-up stage-wise noise reduction process. The key insight is to take lower-level feature maps of CNN as the output of coarse segmentation, and assume that abstract semantic information from higher layers could guide the denoising process. InvUnet is U-shaped and consists of a contracting path like Unet and a denoising path. Contrary to the top-down structure of Unet's expansive path, the denoising path is bottom-up, starting from the bottom features and combining higher level information to refine the segmentation step-by-step. We conduct experiments on the public avaliable nuclear segmentation dataset. On pixel level segmentation task, InvUnet achieves cleaner segmentation results than Unet with only 1.26M parameters. On instance-level segmentation task, InvUnet combined with weight map for implicit contour learning achieves the best results in separating touching nuclei.
Firefly Algorithm For Optimization of Association Rules

With the remarkable increase in online transactions, market basket analysis has become more relevant. Identifying the buying patterns and generating more relevant recommendations to users is the prime goal of online shopping portals. However, extracting associations between products from millions of transactions is a big challenge. Association rule mining techniques are commonly used to discover the associations among transactional databases. To further enhance the elicitation of important strong rules between items, this work applies metaheuristic algorithms such as Genetic Algorithm, Particle Swarm Optimization and Firefly algorithm on Apriori algorithm. The performance of proposed approach is evaluated in terms of number of rules generated and the time complexity of the algorithm. Experiments performed over two different transactional datasets of varying size reveals that Apriori with firefly results into least number of rules as compared to GA and PSO.
Keyword Spotting Scores Fusion based on Fuzzy Integral and Curvelet Descriptor

This paper deals with a new matching scheme for keyword spotting that combines advantages of two set of different descriptors using the Discrete Choquet Fuzzy Integral (DCFI) combiner and the Dynamic Time Warping (DTW) algorithm. In fact, DTW computes for each descriptor the score of matching between two word images. However, the idea of FI technique is that the final matching decision can be significantly increased by giving the combiner a possibility to bias the different DTW output scores based on a priori knowledge about the worthiness degree of each descriptor. Certainly, a complementary information can be derived from two matching system with two different inputs. The experiments conducted on two different document images show that the fusion of statistical and Curvelet descriptors provides a higher spotting precision in comparison with other descriptors.
FORMATION OF EMOTIONAL INTELLIGENCE OF FUTURE MUSICAL ART TEACHERS: RESULTS OF AN ASCERTAINING EXPERIMENT

The article is devoted to the issue of formation of emotional intelligence by future teachers of music art in the process of professional training and analysis of its modern state in the context of strategic tasks of modernization of higher pedagogical education and prospects for its further improvement. The purpose, tasks and diagnostic methods of the ascertainment experiment with the participation of first-year (bachelor), second (master), full-time and part-time students, professors and music teachers were shown. It is noted that the analysis of normative documents of higher educational institutions of Ukraine, which carry out training of specialists with major in «Music Arts», testified the lack of special educational disciplines aimed at forming the emotional intelligence of a future teacher. At the same time, such disciplines are envisaged in the curricula for training specialists of socio-economic professions. Specially designed blocks of questionnaire allowed to examine the opinion of the respondents regarding the awareness of the concept of «emotional intelligence», understanding of its essence, characteristic features, their presence in the interviewed and expediency of its formation as an integral, individual-personal and professionally significant quality of a modern teacher. The results of the study in figures show that the majority of respondents are unfamiliar with the concept of «emotional intelligence», although most respondents gave a positive answer to the question of the appropriateness of mastering the components of this complex psychic education. As a result of the ascertainment experiment, it was concluded that the current professional training of future teachers of music art does not fully meet the requirements of building a new Ukrainian school. It needs the development of the concept of formation of emotional intelligence of future teachers of music art, substantiation of its theoretical and methodological foundations, development and implementation of innovative approaches in the content and organization of their professional and professional training.
A recommender system for job seekers to show up companies based on their psychometric preferences and Company sentiment scores

Recent statistics show an increase in staff turnover which has negatively impacted the organization as well as the employee. The reasons being company culture, working flexibility (work from home opportunity), no learning advancements and pay scale. In this paper, we propose an approach to study the available review data on IT companies (score their reviews based on user review sentiments) and gather information on job seekers which includes their Psychometric evaluations. Then presents the job seeker with useful information or rather outputs on which company is most suitable for the job seeker. The theoretical approach and the importance of such system will be discussed in this paper.