Reviewerly/exHarmony huggingface dataset:
A human fall detection framework based on multi-camera fusion

A sudden fall accident is the main concern for the elderly and disabled people. Automatic detection of the falls from video sequences is an assistive technology for surveillance systems. In this study, a three-stage framework was presented and implemented based on the combination of the data from multiple cameras to address the challenges of occlusion and visibility. In the first stage, the number of used cameras was specified. In the second stage, each camera was decided locally based on its data about the fall incident. In the third and final stage, the aggregation function was used to combine the single camera’s decision considering the coverage rate coefficient of the used cameras. Experiments on the multiple-camera fall dataset demonstrated that our method is comparable to other state-of-the-art methods.
An Analysis and Investigation of InfoStealers Attacks during COVID’19: A Case Study

A COVID-19 pandemic is a rare event that distorted the lives of many civilians worldwide stemming from what is commonly referred to as the new-normal regarding communal standards and the way we live and work. Apart from the devastating influence on humanity and business, the pandemic also spawned a set of unprecedented cyber-attacks. These cyber-attacks during such critical time have traumatized the society. The rise in cyber-attacks is triggered due to rising apprehensions due to this pandemic like never before. Resultantly, a significant increase in the quantity and variety of cyber-attacks is observed since emergence of Covid-19. Cybercriminals promptly leveraged this pandemic premise to rebrand general attack vectors. These attacks are typically info stealers and vary from attacks of minimal intricacy such as 404 Keylogger to the latest and more frequent attacks such as Lokibot. This manuscript analyses the COVID-19 pandemic from the perspective of cyber-attack and establishes that a huge surge has been recorded in the cyberattacks during this pandemic across the globe. In this manuscript, authors analyze the cyberattacks from the perspective of critical macro events to divulge the modus-operandi of cybercrimes. This analysis helps to employ the USA as a case study to determine how cyber-criminals gained access to vital information related to significant events. As a result, this also helps to create and project cyber-crime campaigns meticulously.
Microarchitectural Timing Channels and their Prevention on an Open-Source 64-bit RISC-V Core

Microarchitectural timing channels use variations in the timing of events, resulting from competition for limited hardware resources, to leak information in violation of the operating system's security policy. Such channels also exist on a simple in-order RISC-V core, as we demonstrate on the open-source RV64GC Ariane core. Time protection, recently proposed and implemented in the seL4 microkernel, aims to prevent timing channels, but depends on a controlled reset of microarchitectural state. Using Ariane, we show that software techniques for performing such a reset are insufficient and highly inefficient. We demonstrate that adding a single flush instruction is sufficient to close all five evaluated channels at negligible hardware costs, while requiring only minor modifications to the software stack.
SEEING FROM THE USERS' EYES: AN OUTLOOK TO VIRTUAL-REALITY BASED EMPATHIC DESIGN RESEARCH

Abstract The extent to which designers can understand users often determines the quality of design outcomes. A deep understanding of users allows the designers to focus on the right problem and make optimal design decisions, which encouraged designers to empathize with users. However, the current imagination-based empathizing strategy appears to be too susceptible to their previous experience and knowledge, which has been questioned concerning effectiveness and accuracy. On the other hand, Virtual Reality (VR) technology provides an opportunity for designers to gain experience-driven empathy by immersing them in a virtual environment that mimics the users' surroundings as if they are seeing the world from users' eyes. While abundant studies covered empathy VR and empathy for design, limited attention has been paid to the chance of bringing VR, empathy, and design research together. Addressing this gap, this study explored literature across domains, identified major concerns about this approach, synthesized the evidence, and discussed the feasibility and validity of the VR-based empathic design research approach.
Full-reference Screen Content Image Quality Assessment by Fusing Multilevel Structure Similarity

Screen content images (SCIs) usually comprise various content types with sharp edges, in which artifacts or distortions can be effectively sensed by a vanilla structure similarity measurement in a full-reference manner. Nonetheless, almost all of the current state-of-the-art (SOTA) structure similarity metrics are “locally” formulated in a single-level manner, while the true human visual system (HVS) follows the multilevel manner; such mismatch could eventually prevent these metrics from achieving reliable quality assessment. To ameliorate this issue, this article advocates a novel solution to measure structure similarity “globally” from the perspective of sparse representation. To perform multilevel quality assessment in accordance with the real HVS, the abovementioned global metric will be integrated with the conventional local ones by resorting to the newly devised selective deep fusion network. To validate its efficacy and effectiveness, we have compared our method with 12 SOTA methods over two widely used large-scale public SCI datasets, and the quantitative results indicate that our method yields significantly higher consistency with subjective quality scores than the current leading works. Both the source code and data are also publicly available to gain widespread acceptance and facilitate new advancement and validation.
Label Design-based ELM Network for Timing Synchronization in OFDM Systems with Nonlinear Distortion

Due to the nonlinear distortion in Orthogonal frequency division multiplexing (OFDM) systems, the timing synchronization (TS) performance is inevitably degraded at the receiver. To relieve this issue, an extreme learning machine (ELM)-based network with a novel learning label is proposed to the TS of OFDM system in our work and increases the possibility of symbol timing offset (STO) estimation residing in intersymbol interference (ISI)-free region. Especially, by exploiting the prior information of the ISI-free region, two types of learning labels are developed to facilitate the ELM-based TS network. With designed learning labels, a timing-processing by classic TS scheme is first executed to capture the coarse timing metric (TM) and then followed by an ELM network to refine the TM. According to experiments and analysis, our scheme shows its effectiveness in the improvement of TS performance and reveals its generalization performance in different training and testing channel scenarios.
CONTROL OF A DRONE WITH BODY GESTURES

Abstract Drones are becoming more popular within military applications and civil aviation by hobbyists and business. Achieving a natural Human-Drone Interaction (HDI) would enable unskilled drone pilots to take part in the flying of these devices and more generally easy the use of drones. The research within this paper focuses on the design and development of a Natural User Interface (NUI) allowing a user to pilot a drone with body gestures. A Microsoft Kinect was used to capture the user’s body information which was processed by a motion recognition algorithm and converted into commands for the drone. The implementation of a Graphical User Interface (GUI) gives feedback to the user. Visual feedback from the drone’s onboard camera is provided on a screen and an interactive menu controlled by body gestures and allowing the choice of functionalities such as photo and video capture or take-off and landing has been implemented. This research resulted in an efficient and functional system, more instinctive, natural, immersive and fun than piloting using a physical controller, including innovative aspects such as the implementation of additional functionalities to the drone's piloting and control of the flight speed.
Machine learning algorithms for teaching AI chat bots

Machine learning is a method of data analysis, which allows the analytical system to learn in the course of solving many similar problems. Machine learning is based on the idea that analytical systems can learn how to identify patterns and make decisions with minimal human involvement. The history of already completed dialogues between users is used to train chat bots for automated communication with interlocutors. There are many machine learning algorithms, and this article describes the most popular of them and their use for teaching chat bots.
Efficient Light Field Reconstruction via Spatio-Angular Dense Network

As an image sensing instrument, light field images can supply extra angular information compared with monocular images and have facilitated a wide range of measurement applications. Light field image capturing devices usually suffer from the inherent tradeoff between the angular and spatial resolutions. To tackle this problem, several methods, such as light field reconstruction and light field super-resolution, have been proposed but leaving two problems unaddressed, namely domain asymmetry and efficient information flow. In this article, we propose an end-to-end spatio-angular dense network (SADenseNet) for light field reconstruction with two novel components, namely correlation blocks and spatio-angular dense skip connections to address them. The former performs effective modeling of the correlation information in a way that conforms with the domain asymmetry. Also, the latter consists of three kinds of connections enhancing the information flow within two domains. Extensive experiments on both real-world and synthetic datasets have been conducted to demonstrate that the proposed SADenseNet's state-of-the-art performance at significantly reduced costs in memory and computation. The qualitative results show that the reconstructed light field images are sharp with correct details and can serve as preprocessing to improve the accuracy of related measurement applications.
An ontology based energy management for smart home

• Development of an energy intelligent management based on the import of an existing ontology. • Application of the developed solution in a smart home in the Adrar city -Algeria-. • Estimation of the energy saving using the NewOSEIM. In this work, the main objective is oriented towards the development of a completed system of saving energy at a home in south Algeria. The proposed previously solution (OSEIM) is an intelligent solution based on domain ontology for the electrical energy management in the home; these are designed, built, and operated with increasingly complex technologies. However, the ontology used in this work brings together a body of knowledge that represents all the objects that can positively or negatively influence the consumption of electrical energy, all of this information is linked to each other by meaningful and useful relationships. In addition, the NewOSEIM solution comprising a knowledge base of the OWL (Web Ontology Language) and SWRL rules (Semantic Web Rule Language) providing a reasoning mechanism is developed to offer an optimal solution in energy saving. This work mainly depends on the import of the OSEIM ontology and other ontologies in the field of energy management. Finally, it is noted that the NewOSEIM solution provides an additional energy saving of 2.11 %.
CARMEN: A framework for the verification and diagnosis of the specification of security requirements in cyber-physical systems

In the last years, cyber-physical systems (CPS) are receiving substantial mainstream attention especially in industrial environments, but this popularity has been accompanied by serious security challenges. A CPS is a complex system that includes hardware and software components, with different suppliers and connection protocols, forcing complex data management and storage. For this reason, the construction, verification and diagnosis of security CPS become a major challenge, which involves a correct specification of security requirements, the verification of the correct system configurations, and if necessary, the diagnosis to detect the features to be modified to obtain a security configuration. In this paper, we propose a framework for the verification and diagnosis of security requirements, according to the possible correct configurations of the CPS. The framework is based on the specification of the security requirements and their analysis supported by Model-Driven Engineering and Software Product Line Engineering (SPLE) approaches. To illustrate the usefulness, the proposal has been applied to the security requirements in an Agriculture 4.0 scenario based on automated hydroponic cultivation.
Learning Unbiased Representations via Mutual Information Backpropagation

We are interested in learning data-driven representations that can generalize well, even when trained on inherently biased data. In particular, we face the case where some attributes (bias) of the data, if learned by the model, can severely compromise its generalization properties. We tackle this problem through the lens of information theory, leveraging recent findings for a differentiable estimation of mutual information. We propose a novel end-to-end optimization strategy, which simultaneously estimates and minimizes the mutual information between the learned representation and specific data attributes. When applied on standard benchmarks, our model shows comparable or superior classification performance with respect to state-of-the-art approaches. Moreover, our method is general enough to be applicable to the problem of "algorithmic fairness", with competitive results.
Mining frequent pyramid patterns from time series transaction data with custom constraints

For the problem of mining pyramid scheme patterns, the traditional sequential pattern mining algorithm Prefixspan has many disadvantages such as poor timeliness, uniform threshold, etc. Therefore, we propose a timeliness variable threshold and increment Prefixspan algorithm, named TVI-Prefixspan, for mining the sequential patterns from time series transaction data. To be specific, TVI-Prefixspan aims to mine the patterns that co-occurrence in both an individual sequence and different sequences with high frequency. The most important challenges are how to define the thresholds of frequent one-item and pyramid patterns. We firstly analyze the attributes of the patterns which are hidden in the financial activities between different bank accounts. Secondly, the frequent threshold of each one-item is determined by its different frequency value in normal and pyramid related transaction sequences. We also consider the special relationships in both numerical values and time-series aspects between each pattern's item. Therefore, TVI-Prefixspan produces the frequent one-item set based on its difference of the normal frequency, and then, mines the pyramid patterns with formulated relation constraints. For describing the correlation, we consider sequential, time interval and one-off constraints simultaneously. The experimental results, in real financial data containing pyramid transactions, show that TVI-Prefixspan algorithm succeeds in mining pyramid scheme patterns quickly and effectively. It is superior to traditional sequential pattern mining algorithms such as Prefixspan in efficiency and mining effect.
The WM-q multiple exact string matching algorithm for DNA sequences

The string matching algorithms are among the essential fields in computer science, such as text search, intrusion detection systems, fraud detection, sequence search in bioinformatics. The exact string matching algorithms are divided into two parts: single and multiple. Multiple string matching algorithms involve finding elements of the pattern set P in a given input text T. String matching processes should be done in a time-efficient manner for DNA sequences. As the volume of the text T increases and the number of search patterns increases, the total runtime increases. Efficient algorithms should be selected to perform these search operations as soon as possible. In this study, the Wu-Manber algorithm, one of the multiple exact string matching algorithms, is improved. Although the Wu-Manber algorithm is effective, it has some limitations, such as hash collisions. In this study, the WM-q algorithm, a version of the Wu-Manber algorithm based on the perfect hash function for DNA sequences, is proposed. String matching is performed using different block lengths provided by the perfect hash function instead of using the fixed block length as in the traditional Wu-Manber algorithm. The proposed approach has been compared with E. Coli and Human Chromosome1 datasets, frequently used in the literature, using multiple exact string matching algorithms. The proposed algorithm gives better results for performance metrics such as the average runtime, the average number of characters and hash comparisons.
Scanline Resolution-Invariant Depth Completion Using a Single Image and Sparse LiDAR Point Cloud

Most existing deep learning-based depth completion methods are only suitable for high (e.g. 64-scanline) resolution LiDAR measurements, and they usually fail to predict a reliable dense depth map with low resolution (4, 8, or 16-scanline) LiDAR. However, it is of great interest to reduce the number of LiDAR channels in many aspects (cost, weight of a device, power consumption). In this letter, we propose a new depth completion framework with various LiDAR scanline resolutions, which performs as well as methods built for 64-scanline resolution LiDAR inputs. For this, we define a consistency loss between the predictions from LiDAR measurements of different scanline resolutions. (i.e., 4, 8, 16, 32-scanline LiDAR measurements) Also, we design a fusion module to integrate features from different modalities. Experiments show our proposed method outperforms the current state-of-the-art depth completion methods for input LiDAR measurements of low scanline resolution and performs comparably to the methods(models) for input LiDAR measurements of 64-scanline resolution on the KITTI benchmark dataset.
A collaborative GIS programming course using GitHub Classroom

Abstract Geospatial software developers often rely on Git to collaborate with each other and manage source code in an efficient way. Yet, most GIS programming courses do not prepare students for such a work environment. This article proposes a typology that a GIS programming course could follow consisting of three components: group organization, project and evaluation. Based on the typology, a GIS programming course was designed where randomly formed pairs develop a state‐of‐the‐art QGIS plugin. GitHub Classroom was used to facilitate collaboration among students, which also allowed the lecturer to monitor the progress of groups and provide timely feedback. Five out of the six groups were successful in completing the projects, and a substantial majority of the students were satisfied with the course. A strengths–weaknesses–opportunities–threats analysis reveals insights that other lecturers may find useful when designing their GIS programming courses.
Wide-Baseline Multi-Camera Calibration using Person Re-Identification

We address the problem of estimating the 3D pose of a network of cameras for large-environment wide-baseline scenarios, e.g., cameras for construction sites, sports stadiums, and public spaces. This task is challenging since detecting and matching the same 3D keypoint observed from two very different camera views is difficult, making standard structure-from-motion (SfM) pipelines inapplicable. In such circumstances, treating people in the scene as "keypoints" and associating them across different camera views can be an alternative method for obtaining correspondences. Based on this intuition, we propose a method that uses ideas from person re-identification (re-ID) for wide-baseline camera calibration. Our method first employs a re-ID method to associate human bounding boxes across cameras, then converts bounding box correspondences to point correspondences, and finally solves for camera pose using multi-view geometry and bundle adjustment. Since our method does not require specialized calibration targets except for visible people, it applies to situations where frequent calibration updates are required. We perform extensive experiments on datasets captured from scenes of different sizes (80m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 350m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 600m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), camera settings (indoor and outdoor), and human activities (walking, playing basketball, construction). Experiment results show that our method achieves similar performance to standard SfM methods relying on manually labeled point correspondences.
Comparison of Contextual Importance and Utility with LIME and Shapley Values

Different explainable AI (XAI) methods are based on different notions of 'ground truth'. In order to trust explanations of AI systems, the ground truth has to provide fidelity towards the actual behaviour of the AI system. An explanation that has poor fidelity towards the AI system's actual behaviour can not be trusted no matter how convincing the explanations appear to be for the users. The Contextual Importance and Utility (CIU) method differs from currently popular outcome explanation methods such as Local Interpretable Model-agnostic Explanations (LIME) and Shapley values in several ways. Notably, CIU does not build any intermediate interpretable model like LIME, and it does not make any assumption regarding linearity or additivity of the feature importance. CIU also introduces the value utility notion and a definition of feature importance that is different from LIME and Shapley values. We argue that LIME and Shapley values actually estimate 'influence' (rather than 'importance'), which combines importance and utility. The paper compares the three methods in terms of validity of their ground truth assumption and fidelity towards the underlying model through a series of benchmark tasks. The results confirm that LIME results tend not to be coherent nor stable. CIU and Shapley values give rather similar results when limiting explanations to 'influence'. However, by separating 'importance' and 'utility' elements, CIU can provide more expressive and flexible explanations than LIME and Shapley values.
Research on path planning for mobile robots based on improved ACO

To solve the problems that the basic ant colony algorithm (ACO) is easy to fall into the local optimum, the path is too long and there are too many turns when searching the path of the mobile robot, an improved ACO is proposed in this paper. The improved algorithm randomly sets the ant walkable position points in the obstacle-free area of the map, so that the ants reach the next position at any angle according to the proximity point. Then we incorporate the idea of A* valuation function in the heuristic function to let the ants drive to the point on the line between the starting point and the ending point with a greater probability. The simulation results indicate that the improved ACO can reduce the number of turns and iterations in the path planning of mobile robots, so the robot can find a better path in a shorter time.