Reviewerly/exHarmony huggingface dataset:
Exploring How Personality Models Information Visualization Preferences

Recent research on information visualization has shown how individual differences act as a mediator on how users interact with visualization systems. We focus our exploratory study on whether personality has an effect on user preferences regarding idioms used for hierarchy, evolution over time, and comparison contexts. Specifically, we leverage all personality variables from the Five-Factor Model and the three dimensions from Locus of Control (LoC) with correlation and clustering approaches. The correlation-based method suggested that Neuroticism, Openness to Experience, Agreeableness, several facets from each trait, and the External dimensions from LoC mediate how much individuals prefer certain idioms. In addition, our results from the cluster-based analysis showed that Neuroticism, Extraversion, Conscientiousness, and all dimensions from LoC have an effect on preferences for idioms in hierarchy and evolution contexts. Our results support the incorporation of in-depth personality synergies with InfoVis into the design pipeline of visualization systems.
GNSS Signal Quality Monitoring Based on a Reference Station Network

This paper analyzed the feasibility of development of a GNSS signal quality monitoring system that utilizes observation data provided by a reference station network. We present an overview of possible data sources and analysis techniques that can be used. The details about the software prototype developed are provided. Finally, we elaborate on the system scalability and the steps to extend the capabilities.
On Privatizing Equilibrium Computation in Aggregate Games over Networks

We propose a distributed algorithm to compute an equilibrium in aggregate games where players communicate over a fixed undirected network. Our algorithm exploits correlated perturbation to obfuscate information shared over the network. We prove that our algorithm does not reveal private information of players to an honest-but-curious adversary who monitors several nodes in the network. In contrast with differential privacy based algorithms, our method does not sacrifice accuracy of equilibrium computation to provide privacy guarantees.
CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation

Sequential recommendation techniques provide users with product recommendations fitting their current preferences by handling dynamic user preferences over time. Previous studies have focused on modeling sequential dynamics without much regard to which of the best-selling products (i.e., head items) or niche products (i.e., tail items) should be recommended. We scrutinize the structural reason for why tail items are barely served in the current sequential recommendation model, which consists of an item-embedding layer, a sequence-modeling layer, and a recommendation layer. Well-designed sequence-modeling and recommendation layers are expected to naturally learn suitable item embeddings. However, tail items are likely to fall short of this expectation because the current model structure is not suitable for learning high-quality embeddings with insufficient data. Thus, tail items are rarely recommended. To eliminate this issue, we propose a framework called CITIES, which aims to enhance the quality of the tail-item embeddings by training an embedding-inference function using multiple contextual head items so that the recommendation performance improves for not only the tail items but also for the head items. Moreover, our framework can infer new-item embeddings without an additional learning process. Extensive experiments on two real-world datasets show that applying CITIES to the state-of-the-art methods improves recommendation performance for both tail and head items. We conduct an additional experiment to verify that CITIES can infer suitable new-item embeddings as well.
On the use of blockchain for agrifood traceability

Blockchain food traceability is currently among the trending topics of the global agrifood segment. The possibility of rapidly assessing the full information of a food product, from its origin to the final consumer destination, covering every single point of its journey through the food chain, gives blockchain a promising disruptive tool that offers reliability and safety with effectiveness. Consumers could rely on the possibility of tracing the entire food path from “farm to fork” with a simple scan. This chapter provides a short introduction to blockchain theory and fundamentals, covering the most common platforms, and reviews the most recent applications to food traceability. Blockchain has the potential to be a paradigm shift if some barriers can be overcome along with its application.
Improving the accuracy of Persian HMM-based Voice Command Detection System in Smart Homes Based on Ontology Method

Today, smart home appliances are controlled using different user interfaces and based on various input devices. Speech is a natural and easy way for communication between human and machine. However, smart device manufacturers use a limited set of words to control them and their users must be familiar with the device control words. If there is any difference between the device control words and the user words, the device cannot execute the user query, correctly. In order to solve this problem, an ontology-based method has been proposed in this paper to improve the accuracy of the Hidden Markov Model (HMM)-based voice command detection system. The experimental results show that using the ontology besides the HMM-based voice command detection system improved its performance about 54.5 percent in comparison to the "without ontology case".
Dynamic Stability Control of Inverted-Pendulum-Type Robotic Wheelchair for Going Up and Down Stairs

The wheelchair is the major means of transport for elderly and physically disabled people in their daily lives. However it cannot overcome architectural barriers such as curbs and stairs. In this study, we developed an inverted-pendulum-type robotic wheelchair for climbing stairs. The wheelchair has a seat slider and two rotary links between the front and rear wheels on each side. When climbing up stairs, the wheelchair rotates the rotary links while maintaining an inverted state of a movable body by controlling the position of the center of gravity using the seat slider. In previous research, we proposed the control method for climbing up stairs using the rotary links and seat slider, confirming a period of approximately 5 s to rotate the rotary links. In this paper, we propose a control method for going down stairs, and experimentally verify the control effectiveness and stability.
Deepfake Video Detection Using Audio-Visual Consistency

Benefit from significant advances in deep learning, widespread Deepfake videos with the convincing manipulations, have posted a serious of threats to public security, thus the identification of fake videos has become increasingly active in current researches. However, most present Deepfake detection methods concentrate on exposing facial defects through direct facial feature analysis while merely considering the synergies with authentic behavior information outside the facial regions. Meanwhile, schemes based on meticulous-designed neural networks are rarely efficient to provide subjective interpretations of the final identification evidences. Therefore, to further enrich the diversity of detection method and increase the interpretability of detection evidences, this paper proposes a self-referential method to exploit audio-visual consistency by introducing synchronous audio recordings as reference. In preprocess phase, we propose an audio-visual matching strategy based on phonemes to segment videos, and control experiments have proved that strategy outperforms common equal-length partition. To deal with such video segments, an audio-visual coupling model (AVCM) is employed for audio-visual feature representations, then similarity metrics are measured for mouth frames and related speech segments. Actually, synchronized pairs mean the high scores of similarity and asynchronous pairs opposite. The evaluations on DeepfakeVidTIMIT indicate that our method has achieved competitive results compared with current main methods, especially in high quality datasets.
Shipdenet-18: An Only 1 Mb With Only 18 Convolution Layers Light-Weight Deep Learning Network For Sar Ship Detection

With the rise of Artificial Intelligence (AI), many previous studies have already applied Deep Learning (DL) for ship detection from Synthetic Aperture Radar (SAR) imagery. However, these network scale and model size are both rather huge, leading to more computation costs. As a result, ship detection speed is bound to decline due to more computation costs, and FPGA/DSP transplantation also becomes more challenging coming from huge mode size. Therefore, to solve these problems, this paper proposes a novel lightweight deep learning network for SAR ship detection named ShipDeNet-18 (only 18 convolution layers). Essentially, fewer layers and fewer kernels jointly contribute to ShipDeNet-18's light-weight characteristic. In addition, to compensate for the severe detection accuracy's sacrifice, we also propose a Deep and Shallow Feature Fusion Module (DSFF-Module) and a Feature Pyramid Module (FP-Module), which can effectively improve its detection accuracy. Experimental results on the open SAR Ship Detection Dataset (SSDD) reveal that ShipDeNet-18's detection speed is largely superior to the other state-of-the-art detectors, meanwhile its detection accuracy is only slightly inferior to others. ShipDeNet-18 is a brand-new deep learning network built from scratch, more light-weight than the other detectors, with fewer parameters (228,246), lower computation costs (456,042 FLOPs), and smaller model size (1 MB). It is of great value in some real-time SAR application, and is also convenient for future hardware transplantation (FPGA/DSP).
Image Segmentation Based on Galactic Swarm Optimization

Image segmentation is one of the most challenging and interesting research topics and draws the attention of the many researchers. Different methods have been proposed but a single method is not sufficient enough to segment a wide variety of images. Due to this reason, continuous research need to be carried out to improve the efficiency and the real life acceptability. In this work, a new image segmentation method is proposed which is based on galactic swarm optimization. Galactic swarm optimization is new metaheuristic method which is based on the movement of stars, galaxies and other objects of space. The proposed method use thresholding with the galactic swarm optimization where, the value of the threshold is to be determined by the metaheuristic algorithm. The optimal threshold value can efficiently segment an image. Experiments shows the efficiency and drawbacks of the proposed method. The advantages and problems of this method is discussed along with the future research directions.
Viral surveillance: Governing social isolation in São Paulo, Brazil, during the COVID-19 Pandemic

In the context of COVID-19 pandemics, Brazilian cities implemented social isolation policies and deployed digital systems to monitor urban mobility. This article addresses the setting of two digital technologies based on massive cell-phone data collection by private companies in São Paulo state. We relied on secondary data from multiple sources (press conferences, interviews, newspaper articles, public documents), complemented by primary data from the authors' ongoing research. In our analysis of heterogeneous and contingent techniques of pandemic control, we found that although these monitoring technologies seem to be effective in assisting public services and informing society, they also raise issues about performativity and transparency, with relevant consequences for their adoption in sanitary emergencies, and their potential legacy to São Paulo's public safety management.
A Chaotic Disturbance Wolf Pack Algorithm for Solving Ultrahigh-Dimensional Complex Functions

The optimization of high-dimensional functions is an important problem in both science and engineering. Wolf pack algorithm is a technique often used for computing the global optimum of a multivariable function. In this paper, we develop a new wolf pack algorithm that can accurately compute the optimal value of a high-dimensional function. First, chaotic opposite initialization is designed to improve the quality of initial solution. Second, the disturbance factor is added in the scouting process to enhance the searching ability of wolves, and an adaptive step length is designed to enhance the global searching ability to prevent wolves from falling into the local optimum effectively. A set of standard test functions are selected to test the performance of the proposed algorithm, and the test results are compared with other algorithms. The high-dimensional and ultrahigh-dimensional functions (500 and 1000) are tested. The experimental results show that the proposed algorithm features in good global convergence, high accuracy calculation, strong robustness, and excellent performance in high-dimensional functions.
A machine learning based framework for assisting pathologists in grading and counting of breast cancer cells

Breast cancer normally occurs in the breast cells of both men and women, but is prominent in women. Computer aided detection increases the chance of early detection and diagnosis. This paper proposes a breast cancer detection method using Nuclear Atypia Scoring (NAS). The proposed cancer detection method works by converting each and every cancerous tissue into objects. Along with detecting the grade, proposed mechanism gives the count of the detected cells. This assists pathologists in identifying whether cells are cancerous or not along with the count of each type. Proposed model was evaluated on MITOS-ATYPIA-14 Challenge dataset. Accuracy of 0.89 and precision of 0.87 is obtained by the best method. Results indicate that the proposed machine learning technique has better performance as compared to existing methods and can aid pathologists in the detection process.
The Unreasonable Ineptitude of Deep Image Classification Networks

The success of deep image classification networks has been met with enthusiasm and investment from both the academic community and industry. We hypothesize users will expect these systems to behave similarly to humans, and to succeed and fail in ways humans do. To investigate this, we tested six popular image classifiers on imagery from ten tool categories, examining how 17 visual transforms impacted both human and AI classification. Results showed that (1) none of the visual transforms we examined produced substantial impairment for human recognition; (2) human errors were limited to mostly to functional confusions; (3) almost all visual transforms impacted nearly every image classifier negatively and often catastrophically; (4) human expectations about performance of AI classifiers map more closely onto human error than AI performance; and (5) models trained with an enriched training set involving examples of the transformed imagery achieved improved performance but were not inoculated from error.
Self-Supervised Remote Sensing Image Retrieval

Current remote sensing platforms generate a vast amount of imagery but the best current methods to index and retrieve that data require expensive and difficult to procure labels. In this paper, we aim to address this problem by presenting a performant content based image retrieval (CBIR) system that is capable of indexing and retrieval using only unlabelled data. We investigate the use of self-supervised learning, a method for end-to-end learning of visual features from large datasets. In particular, we investigate the performance of four state-of-the-art self-supervised learning methods: variational autoencoders, bidirectional GANs, colourisation networks and DeepCluster, and evaluate the quality of the representations learned on remote sensing CBIR problems. Experiments on two very high resolution datasets show that the best of these methods, DeepCluster, is able to achieve near parity with supervised transfer learning despite not using any label information.
Benchmarking framework for robotic arc welding motion planning

Producing a small series of large complex parts using robotic arc welding is challenging due to the time it takes to program the robot. Currently, offline programming software assists the operator in programming hundreds of welds. This is often still an iterative process because the welding robot or torch may be in collision during part of the weld motion. Adding motion planning software in this workflow could reduce the time required to generate a robot program. However, the capabilities of motion planning algorithms in this context are not clear. We present a generic framework to evaluate and compare motion planning algorithms for robot arc welding. This framework allows integration of existing open source and commercial planners on different levels of abstraction depending on their capabilities. Different planners can be combined and benchmarked as a group if a single planner is not sufficient to solve all aspects of the problem. The framework is applied to two realistic welding cases to show its potential.
Implementation of Inverse Perspective Mapping for Camera-Vision Water-Level Measurements

To prevent floods from endangering lives and property, surveillance cameras are widely used in places prone to floods, such as low-lying areas and rivers, to monitor water levels during typhoons. Recently, with the advancement of surveillance camera technologies, surveillance cameras have been widely applied to automatically measure water levels. In this study, an embedded system based on a single camera is proposed, where camera images are used to measure water levels. Digital image processing is used to identify the current water level from an image of the on-site staff gauge. The fact that the camera position typically results in a non-orthogonal angle between the camera's optical axis and the staff gauge plane on-site was taken into consideration due to the fact that it causes perspective distortion and leads to deviations in water level measurements. In this study, the inverse perspective mapping (IPM) method was applied in the system to overcome this problem. Using IPM to transform the coordinate system of images from one perspective plane to another rectified the perspective distortion. To simplify the image processing of every input frame, only the region of interest (ROI) underwent IPM transformation during the water level calculation in order to improve the system operating performance. The experimental results proved that the water level measurement system using the IPM method effectively reduced measurement deviations for confined space applications and effectively tracked real-world water level changes.
Multi-threaded Spotted Hyena Optimizer with thread-crossing techniques

This paper presents a Multi-threaded version of the Spotted Hyena Optimizer algorithm with thread-crossing techniques (MT-SHO) to improve the ability of the algorithm to explore the search space. The original algorithm is inspired by the hunting behavior of the spotted hyena. Along the different sections of the work, we explain in detail how the original algorithm simulates the spotted hyena’s behavior to optimize highly complex mathematical functions and how we handle the procedures and results of the multi-threaded version, with thread-crossing techniques that improve the ability to explore and exploit the search space by letting threads learn between them. We present the experiments used to determine the best value of the parameters used in the parallel version of the algorithm and to prove that our proposal obtains significantly good results we compare the results obtained by evaluating 24 benchmark functions with the results published for the original algorithm as well as other metaheuristic algorithms.
A Case for Camera-as-a-Service

A new vision of harnessing the ocean of video data captured by cameras in an efficient, secure, and manageable way. Following the hardware trends in reality, we advocate to treat cameras as the first-class computing and storage platform, and expose their services to third parties. To this end, the cameras should move from hard-coded to software-defined, from dumb data source to autonomous data processor, from isolated to orchestrated, and from one-shot to human-in-the-loop.
Quantum correlation swapping between Werner and separable states

Abstract In this paper we study quantum correlation (QC) swapping between two different kinds of quantum-correlated states. To be concrete, one is the famous Werner state while another is a kind of separable state. We employ measurement-induced disturbance (Luo 2008 Phys. Rev. A 77 02230) as the quantifier to characterize and quantify QC. We work out QCs in all the concerned states and concretely analyze their behaviors and properties such monotony features and thresholds. We find that a long-distance shared QC can be generated from two short-distance ones via intermediate measure. Moreover, the amount of the long-distance shared QC cannot reach that in the Werner state but that in the separable state.
An Intelligent Framework for Malware Detection in Internet of Things (IoT) Ecosystem

The Heuristic-Based Analysis method, which is otherwise known as Behavioural-Based Analysis technique applied by several Anti-Virus and Anti-Malware vendors of computer network systems has not been sufficient. There are gaps in the field of research to develop a novel approach to solving the inherent and existential threats and attacks in the interconnected computer network systems, especially in the fast growing IoT Ecosystems that has now become ubiquitous to everyday life. We proposed and presented the experimental implementation of an intelligent framework, including the realization of the Multilayer Perceptron (MLP) with multiple hidden layers by exploiting the capabilities of Hierarchical Extreme Learning Machine (H-ELM). This has the preeminent underexplored potentials for accelerated speed, rapid feature learning, training, improved classification performance, and accuracy of Detecting, Recognizing, and Predicting (DRP) anomalies in an Internet of Things (IoT) ecosystem. The experimental implementation demonstrated the effectiveness of the proposed Intelligent DRP-Framework also known as the iDRP-Framework using the generalized MLP technique for demonstrating how a non-traditional malware detection mechanism can be applied to dynamically and intelligently Detect, Recognize, and Predict malwares in the generated big dataset of an IoT ecosystem through converted image file, such as the Log files, and Binary dataset to create machine learning model. The result indicates that the proposed iDRP-Framework delivered significant performance and accuracy in the detection of anomalies in an interconnected computer network system.