Reviewerly/exHarmony huggingface dataset:
Characterizing the Quality of Insight by Interactions: A Case Study

Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying insight are rare.This paper presents a case study to investigate the possibility of characterizing the quality of insight via the interactions performed.To do this, we devised the interaction of a visualization tool-MediSyn-for insight generation.MediSyn supports five types of interactions: selecting, connecting, elaborating, exploring, and sharing.We evaluated MediSyn with 14 participants by allowing them to freely explore the data and generate insights.We then extracted seven interaction patterns from their interaction logs and correlated the patterns to four aspects of insight quality.The results show the possibility of qualifying insights via interactions.Among other findings, exploration actions can lead to unexpected insights; the drill-down pattern tends to increase the domain values of insights.A qualitative analysis shows that using domain knowledge to guide exploration can positively affect the domain value of derived insights.We discuss the study's implications, lessons learned, and future research opportunities.
Inception DenseNet With Hybrid Activations For Image Classification

The familly of Inception network and DenseNets are some of the most successfull CNN architectures proposed in recent years. Researchers of Inception-ResNet combined the residual connections and the Inception architecture by replaceing the tensor concatenation step of the Inception modules using residual connections. Later, the DenseNet archicture demonstrates that using dense connections (implemented via filter concatenation) instead of residual connections can also be very effective to train deeper networks. This gives the problem of are there any performance on combining the Inception-like blocks with dense connection. In this paper, we design a network architecture by embedding the Inception-like blocks into DenseNet architecture, which is called Inception-DenseNet architecture. Another innovation is that our inception-like blocks introduce hybrid activation operations, which is different from previous inception blocks. Visualization experiments show that this hybrid activation mode can make more flexible response to object semantic regions. The performance of our net models are compared with the current best net models such as DenseNets, ResNets on several image datasets. The experiment results indicate that our Inception-DenseNet can obtain the same or better classification accuracy while use a smaller number of trainable parameters.
Identification of Digitally Modulated Signals-Status and Challenges

There are many methods for the digital modulation identification task. Despite of the method or approach, there is some kind of signal feature extraction represented by sets of parameters to make the identification possible. This paper provides the study of the parameters of some important identification techniques used for digitally modulated signals identification.
Model-Agnostic Metric for Zero-Shot Learning

Zero-shot Learning (ZSL) aims to learn a classifier to recognize unseen categories without training samples. Most ZSL works based on embedding models handle the visual space and the semantic space through a common metric space and then apply a simple nearest neighbor search which directly leads to the hubness problem, one of the main challenges of ZSL. Contrary to recent works, whose conclusions about hubs are drawn based on Euclidean and specific models like ridge regression, we adopt cosine metric and for the first time prove cosine is model-agnostic to alleviate the hubness problem in ZSL. Assuming that the normalized mapped semantic vectors follow a uniform distribution, we provide theoretical analysis which demonstrates that hubs can be better reduced with a higher-dimensional cosine metric space. Moreover, we introduce a diversity-based regularizer with the cosine metric which underpins the assumption about the uniform distribution and further improves the model's discriminative ability. Extensive experiments on five benchmarks and large-scale Imagenet dataset show that our method can improve the performance, surpassing previous embedding methods by large margins.
Inference of Suspicious Co-Visitation and Co-Rating Behaviors and Abnormality Forensics for Recommender Systems

The pervasiveness of personalized collaborative recommender systems has shown the powerful capability in a wide range of E-commerce services such as Amazon, TripAdvisor, Yelp, etc. However, fundamental vulnerabilities of collaborative recommender systems leave space for malicious users to affect the recommendation results as the attackers desire. A vast majority of existing detection methods assume certain properties of malicious attacks are given in advance. In reality, improving the detection performance is usually constrained due to the challenging issues: (a) various types of malicious attacks coexist, (b) limited representations of malicious attack behaviors, and (c) practical evidences for exploring and spotting anomalies on real-world data are scarce. In this paper, we investigate a unified detection framework in an eye for an eye manner without being bothered by the details of the attacks. Firstly, co-visitation and co-rating graphs are constructed using association rules. Then, attribute representations of nodes are empirically developed from the perspectives of linkage pattern, structure-based property and inherent association of nodes. Finally, both attribute information and connective coherence of graph are combined in order to infer suspicious nodes. Extensive experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks. Additionally, abnormality forensics metrics including distribution of rating intention, time aggregation of suspicious ratings, degree distributions before as well as after removing suspicious nodes and time series analysis of historical ratings, are provided so as to discover interesting findings such as suspicious nodes (items or ratings) on real-world data.
Diffusion In The Wavelet Domain For Denoising Radiographic Images Of Welding Defects

In this paper, we aimed to filter radiographic weld images to facilitate weld defects detection and to improve the automatic industrial inspection. The noisy images were contaminated by three types of noise: the multiplicative speckle noise, the additive Gaussian white noise, and the mixed noise combining the two kinds of noise. Wavelet-based filters and anisotropic diffusion techniques have proven their worth in reducing both Gaussian additive noise and speckle noise. We presented in this work a filtering algorithm based on diffusion in the wavelet packet domain to enhance the quality of the noisy weld images. Comparing the performance of this approach to other wavelet based methods, experiments proved the wavelet packet diffusion's effectiveness in reducing noise and preserving defects details.
Risky Business: Foreign Direct Investment and the Economic Consequences of Electoral Violence

Abstract While the social and political consequences of electoral violence are widely discussed in the literature, less is known about the economic repercussions of electoral violence. Our study investigates this dynamic, asking how episodes of electoral violence affect existing levels of foreign direct investment. We argue that, when managing their investments in foreign markets, multinational firms weigh the expected gains of the investment with the probability that the host country experiences significant lapses in political stability. Electoral violence signals a greater risk of this undesirable outcome and, thus, increases the probability that firms divest in favor of more favorable investment environments. Using firm-level data, we investigate the investment behavior of seventy-seven multinational firms in twenty-five Sub-Saharan African countries from 1995 to 2008 with a series of logistic regression models. Directly modeling the choices that firms make with respect to yearly changes in investment, we demonstrate that firms are more likely to divest following events of electoral violence in a host country. This study carries important implications for our understanding of how electoral violence may undermine the economic stability of emerging economies.
Going Much Wider with Deep Networks for Image Super-Resolution

Divide and Conquer is a well-established approach in the literature that has efficiently solved a variety of problems. However, it is yet to be explored in full in solving image super-resolution. To predict a sharp up-sampled image, this work proposes a divide and conquer approach based wide and deep network (WDN) that divides the 4× up-sampling problem into 32 disjoint subproblems that can be solved simultaneously and independently of each other Half of these subproblems deal with predicting the overall features of the high-resolution image, while the remaining are exclusively for predicting the finer details. Additionally, a technique that is found to be more effective in calibrating the pixel intensities has been proposed. Results obtained on multiple datasets demonstrate the improved performance of the proposed wide and deep network over state-of-the-art methods.
Multi-process and multi-scale spatial predictive analysis of an orogenic Archean gold system, Rio das Velhas Greenstone Belt, Brazil

There has always been a need for new methodologies and research to improve the decision-making process at the early stages of mineral exploration. This article presents a novel approach to integrating geodata in support of a mineral systems-based spatial analysis of orogenic gold deposits in the Rio das Velhas Greenstone Belt (RVGB), Quadrilátero Ferrífero Province, Brazil. The gold mineralization in the RVGB is spatially associated with thrust faults and shear zones and mainly hosted by iron-rich rocks such as mafic–ultramafic sequences and banded iron formations. To best represent the targeting elements of this mineral system spatially, a knowledge-based fuzzy logic method was employed to map the expressions of the gold depositional processes at the province (1:500,000), district (1:100,000) and camp (1:50,000) scales. At each scale, multivariate statistical techniques served to enhance multiple geological, geophysical, and geochemical datasets and extract from these data spatial proxies of the gold depositional processes. The results of this multi-scale predictive analysis were as follows: The first, province-scale model (M1) identified the entire gold prospective tract and the areas within it that may be of greatest relevance to future exploration. The second, district-scale model (M2) identified the different gold camps within the prospective tract and mapped the areas of gold favorability in a more detailed manner. The third, camp-scale model (M3) identified areas that, based on the current knowledge and distribution of high resolution geodata, are the most favorable whilst also being small enough as to permit target testing using conventional mineral exploration tools such as geophysics, geochemistry and/or drilling. The results obtained from our predictive models were validated by comparing them against the known gold occurrences using ROC (receiver operating characteristics) curves and AUC (area under the curve) graphs. According to these validations, model M1 scored an accuracy of 93.38%, whereas models M2 and M3 scored accuracies of 88.31% and 93.38%, respectively. A key observation made in the course of this study is that the gold prospective area as predicted by models M1, M2 and M3 varies according to the scale of the analysis. A novel factor in our approach is that we aimed assess the targeting criteria and spatial datasets that underpin them according to their spatial resolution and presented the results in form of integrated maps. In addition, the tools developed in this study have the capacity to reduce the cost of direct detection technologies regarding the transition from broad regional to camp scale at the early stages of mineral exploration, where the most initial decisions in search and area reduction are critical.
A New Secure and Efficient Approach for TRNG and Its Post-Processing Algorithms

Random numbers are important parameters for the security of cryptographic applications. In this study, a secure and efficient generator is proposed to generate random numbers. The first part of the generator is a true random number generator that consists of chaotic systems implemented on FPGA. The second part of the generator is a post-processing algorithm used to overcome the problems that emerge from the generator or environmental factors. As the post-processing algorithm, Keccak, the latest standard of hash algorithm, was rearranged and used. Random numbers with the proposed approach meet the security requirements for cryptographic applications. Furthermore, the NIST 800-22 test suite and autocorrelation test are used to ensure the generated numbers have no statistical weakness. The successful test results demonstrate the security of the generated numbers. An important advantage of the proposed generator does not cause any data loss and perform 100% efficiency although data loss can be up to 70% in some post-processing algorithms.
Video-based Skeletal Feature Extraction for Hand Gesture Recognition

Hand gesture recognition is a hot topic and a central key for different types of application. As applications of computers and intelligent systems are growing in our daily life, facilitating natural human computer interaction becomes more important. In this paper, we focus on video-based approach on hand gesture recognition integrated with 3-D hand skeletal features to construct the raw video sequences, retaining the key video frames, extracting spatial temporal data and feeding them into a Support Vector Machine model for 2-D hand sign classification. Our novel method integrates hand skeletal descriptor into video sequence to retain the spatial temporal information which will be extracted as vectors for classification task. As oppose to conventional method of requiring a well placed pair of cameras or depth detection hardware, our method only require only one camera. The proposed approach outperforms state-of-the-art static hand gesture recognition methods, achieving almost 100% accuracy among 24 classes.
Design and Implement a Robot for Security Purposes

Technology robots are now arrived for a mature race where it has been used in many industrial fields in factories, production lines and in automobile factories, medical industries, military industries, security solutions and in education. Using robot systems in state of human provides a lot of advantages, like decrease the number of employee or increase the security of any system, because it works at same level of efficiency all time. The proposed system offers the design of a robot that can achieve purposes like: secure treatments with staff or with bank customers.
Continuous Authentication for the Internet of Things Using Channel State Information

In the centralized Internet of Things (IoT) archi- tecture there exists gateways, which gather sensed data from sensor nodes frequently. Due to numerous security threats and attacks in wireless sensor networks (WSNs), how to achieve device-to-device authentication is a fundamental security issue. In some IoT environments such as environmental monitoring systems, sensor nodes need to transmit sensed data to gateways periodically. Previous authentication schemes make sensor nodes and gateways authenticate each other frequently in the beginning of each transmission session and affect the real-time nature of sensed data. In this paper, we explore a lightweight device fingerprint extraction and verification algorithm using Channel State Information (CSI). We propose a CSI-based continuous authentication scheme for the Internet of Things. The proposed scheme can achieve continuous authentication for the target IoT device and finish each authentication process within 1.5 ms. Our experiments show that the proposed scheme can resist identity- based attacks and has better performance than existing continuous authentication schemes for the Internet of Things.
Socio-Economic Contributions of Mobile Applications in Africa: Impact of Local Mobile Applications

Mobile applications (mobile apps) are increasingly being adopted and disseminated in Africa, and every socio-economic activity sector uses mobile apps to deliver a variety of services. Several mobile apps innovations startups have helped rejuvenate African innovation in the use of mobile technologies and the development of various types of mobile apps. These locally created mobile apps are actively used to perform various activities. This paper aims to investigate the contributions of Africans' mobile apps to determine their impact on the development of Africa. A survey was conducted in five selected countries in Africa. Of the 2300 questionnaires distributed, a total of 1285 were returned, giving a response rate of 55.87%. The findings indicate that a large proportion of African populations are mobile app users and mobile apps have a significant impact on Africa's socio-economic sectors. This research will be of interest to Africa's knowledge base and mobile app development stakeholders.
Crowded Human Detection via an Anchor-pair Network

This paper presents an anchor-pair network for crowded human detection, which can overcome and solve the difficulties caused by occlusion in crowded scenes. Specifically, we use a function-aware network structure to extract more distinctive and discriminative features for head and full-body respectively, and then a CNN module is also exploited to fuse the features by learning the correlations between head and full-body to reduce crowd errors. Meanwhile, a novel paired form for anchors, denoted as anchor-pair, is proposed to estimate the head regions and full-body regions simultaneously. Furthermore, a new ingenious Joint-NMS is introduced to perform on the detected head and full-body box pairs, which produces significant performance improvement in heavily occluded scenarios at tiny computational cost. Our anchor-pair network achieves a state-of-the-art result on the CrowdHuman dataset which reduces the MR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> to 55.43%, achieving 11.59% relative improvement over our dataset baseline.
Measuring the Utilization of Public Open Spaces by Deep Learning: a Benchmark Study at the Detroit Riverfront

Physical activities and social interactions are essential activities that ensure a healthy lifestyle. Public open spaces (POS), such as parks, plazas and greenways, are key environments that encourage those activities. To evaluate a POS, there is a need to study how humans use the facilities within it. However, traditional approaches to studying use of POS are manual and therefore time and labor intensive. They also may only provide qualitative insights. It is appealing to make use of surveillance cameras and to extract user-related information through computer vision. This paper proposes a proof-of-concept deep learning computer vision framework for measuring human activities quantitatively in POS and demonstrates a case study of the proposed framework using the Detroit Riverfront Conservancy (DRFC) surveillance camera network. A custom image dataset is presented to train the framework; the dataset includes 7826 fully annotated images collected from 18 cameras across the DRFC park space under various illumination conditions. Dataset analysis is also provided as well as a baseline model for one-step user localization and activity recognition. The mAP results are 77.5% for pedestrian detection and 81.6% for cyclist detection. Behavioral maps are autonomously generated by the framework to locate different POS users and the average error for behavioral localization is within 10 cm.
Driver Action Recognition Based on Attention Mechanism

According to the world health organization, millions of people are killed by traffic accidents worldwide every year, and more than 80 percent of accidents are caused by unsafe driving. This paper studies driver behavior recognition, aiming to standardize driver's driving behavior and reduce the probability of traffic accidents. However, the inter-class variance of drivers' different actions is small, making it difficult to identify. To improve fine-grained identification, an attention module is designed to be inserted into convolutional neural network, which consists of two parallel parts: channel level attention and space level attention. The introduction of attention mechanism can focus the weight of the network on the meaningful pixels and channels, promote the expression of effective features, and suppress the interference of noise. The experiments show that the recognition accuracy is improved after applying attention mechanism. The visualization results show that the introduction of attention mechanism can make the network focus on the prominent areas of the feature map.
hBOSOS: An Ensemble of Butterfly Optimization Algorithm and Symbiosis Organisms Search for Global Optimization

The present study proposes a hybrid metaheuristic algorithm, viz. hBOSOS with the help of two popular metaheuristics, viz. BOA and SOS. The global search ability of BOA and the local search ability of SOS are combined here to make the proposed algorithm robust and efficient. The proposed algorithm is tested with 26 classical benchmark functions and also to compare, several metaheuristics are taken from the literature. From the comparison, it can be observed that the hBOSOS is superior to the other compared algorithms.
An Incremental Kernel Extreme Learning Machine for Multi-Label Learning With Emerging New Labels

Multi-label learning with emerging new labels is a practical problem that occurs in data streams and has become an important new research issue in the area of machine learning.However, existing models for dealing with this problem require high learning computational times, and there still exists a lack of research.Based on these issues, this paper presents an incremental kernel extreme learning machine for multi-label learning with emerging new labels, consisting of two parts: a novelty detector; and a multi-label classifier.The detector with free-user-setting threshold parameters was developed to identify instances with new labels.A new incremental multi-label classifier and its improved version were developed to predict a label set for each instance, which can add output units incrementally and update themselves in unlabeled instances.Comprehensive evaluations of the proposed method were carried out on the problems of multilabel classification with emerging new labels compared to comparative algorithms, which revealed the promising performance of the proposed method.
Tiny Object Detection:Comparative Study using Single Stage CNN Object Detectors

The ability of vehicles to perceive the surrounding environment is enhanced by their capacity to detect and localize the objects around them, be it traffic signs, road signs, other vehicles, landmarks etc., the vehicle movement and varying object dimension poses huge challenge on detection especially smaller dimension objects from far distance. In the proposed work such objects are detected at far distance using different single stage CNN detectors. A comparative study was done on three models such as SSD300 and its variants, Tiny-YOLO V3 and RetinaNet. The networks were trained using 7082 sample images and tested on 6500 images. Detection was tested on images with varying scale and rotation in realtime with single class object size of 2-3-inch diameter (20*20 pixels to 40*50 pixels) at 3 to 4 meters of distance. Tiny-YOLO V3 performs comparatively better with 60% accuracy, speed of 0.09 SPF and the model size satisfy the memory constraint.