Reviewerly/exHarmony huggingface dataset:
A Flexible High-Level Fusion for an Accurate Human Action Recognition System

Action recognition is a very effective method of computer vision areas. In the last few years, there has been a growing interest in Deep learning networks as the Long Short–Term Memory (LSTM) architectures due to their efficiency in long-term time sequence processing. In the light of these recent events in deep neural networks, there is now considerable concern about the development of an accurate action recognition approach with low complexity. This paper aims to introduce a method for learning depth activity videos based on the LSTM and the classification fusion. The first step consists in extracting compact depth video features. We start with the calculation of Depth Motion Maps (DMM) from each sequence. Then we encode and concatenate contour and texture DMM characteristics using the histogram-of-oriented-gradient and local-binary-patterns descriptors. The second step is the depth video classification based on the naive Bayes fusion approach. Training three classifiers, which are the collaborative representation classifier, the kernel-based extreme learning machine and the LSTM, is done separately to get classification scores. Finally, we fuse the classification score outputs of all classifiers with the naive Bayesian method to get a final predicted label. Our proposed method achieves a significant improvement in the recognition rate compared to previous work that has used Kinect v2 and UTD-MHAD human action datasets.
Multiple Cyclic Swarming Optimization for Uni- and Multi-modal Functions

This paper proposedFares, I. multiple cyclic exploration andRizk-Allah, Rizk M. exploitationHassanien, Aboul Ella at two levels on the basis of improvedVaclav, Snasel whale swarming and particle swarm optimization algorithms. The first level covers exploration, and different subsets of candidate solutions are used to explore the search space, while the second level covers exploitation. The whale optimization algorithm (WOA) was used at the first level and the particle swarm optimization (PSO) at the second level. The proposed approach is tested on twenty benchmark test functions, and initiatory results are presented. According to the computational results, the proposed approach more successful of the state-of-the-art algorithms.
Multimodal 3D Shape Reconstruction under Calibration Uncertainty Using Parametric Level Set Methods

We consider the problem of 3D shape reconstruction from multimodal data, given uncertain calibration parameters. Typically, 3D data modalities can come in diverse forms such as sparse point sets, volumetric slices, and 2D photos. To jointly process these data modalities, we exploit a parametric level set method that utilizes ellipsoidal radial basis functions. This method not only allows us to analytically and compactly represent the object; it also confers on us the ability to overcome calibration-related noise that originates from inaccurate acquisition parameters. This essentially implicit regularization leads to a highly robust and scalable reconstruction, surpassing other traditional methods. In our results we first demonstrate the ability of the method to compactly represent complex objects. We then show that our reconstruction method is robust both to a small number of measurements and to noise in the acquisition parameters. Finally, we demonstrate our reconstruction abilities from diverse modalities such as volume slices obtained from liquid displacement (similar to CT scans and X-rays) and visual measurements obtained from shape silhouettes as well as point clouds.
Embedding QR Codes on the Interior Surfaces of FFF Fabricated Parts

Quick Response (QR) codes have been utilized in various fields of manufacturing after their introduction to the automotive industry. Due to their error correction algorithms, they have advantages over the other coding schemes. Various information regarding the parts can be stored into these QR codes, which are conventionally printed on the labels of the artifacts. In this study, we propose an automated approach for embedding QR codes on the interior flat surfaces of 3D printed artifacts. Since we are using the interior surfaces, the outer surface or the shape of the object is not affected by the QR code patterns. We used Rhinoceros3D (CAD software) and Grasshopper3D (analytical modeling tool for Rhinoceros3D) environments for the realization of the proposed method. We fabricated several parts with QR codes in them utilizing a Fused Filament Fabrication type of 3D printer and showed that the codes are readable via the cameras of the mobile phones. We concluded the paper with proposing some application areas of the method.
The Web Penetration Based SQL Injection

With the development of web technology, the communication based on C/S transmission mode is gradually replaced by B/S communication mode. The updating of intrusion technology and web source code visibility and, and the construction of joint query statements based on SQL database query language make the web penetration mode based on SQL injection become a direct database-oriented penetration mode. This paper analyses the principle of SQL injection, completes the verification test on the target machine, and puts forward a new direction for the development of SQL injection.
Apprenticeship Learning for Continuous State Spaces and Actions in a Swarm-Guidance Shepherding Task

Apprenticeship learning (AL) is a learning scheme using demonstrations collected from human operators. Apprenticeship learning via inverse reinforcement learning (AL via IRL) has been used as one of the primary candidate approaches to obtain a near optimal policy that is as good as that of the human policy. The algorithm works by attempting to recover and approximate the human reward function from the demonstrations. This approach assists in overcoming limitations such as the sensitivity associated with the variance in the quality of human data and the short sighted decision time that does not consider future states. However, addressing the problem of continuous action and state spaces has still been challenging in the AL via IRL algorithm. In this paper, we propose a new AL via IRL approach that is able to work with continuous action and state spaces. Our approach is used to train an artificial intelligence (AI) agent acting as a shepherd of artificial sheep-inspired swarm agents in a complex and dynamic environment. The results show that the performance of our approach is as good as that of the human operator, and particularly, the agent's movements seem to be smoother and more effective.
On the Application of Financial Security Standards in Blockchain Platforms

Security standards such as the Payment Application Data Security Standard (PA-DSS) have been developed to keep transaction data secured in traditional payment systems. However, blockchain systems are not in the scope of these security standards. In this work, we highlight the differences between traditional and decentralized payment platforms and we present an adaptation of the PA-DSS standards to apply them in transaction-supported, decentralized blockchain platforms. We evaluate the QTUM and Ethereum blockchain platforms by using our adapted standards and we report security gaps on each platform. We conclude that neither platform is suitable for business adoption based on the adapted PA-DSS standard’s evaluation results.
Using QR Codes on Professional Posters to Increase Engagement and Understanding

By Tara Spalla King, PhD, RN, Director of Assessment and Evaluation and Assistant Professor of Clinical Nursing, The Ohio State University College of Nursing, Columbus, Ohio, [email protected] Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.nurseeducatoronline.com).
On divergence-based author obfuscation: An attack on the state of the art in statistical authorship verification

Abstract Authorship verification is the task of determining whether two texts were written by the same author based on a writing style analysis. Author obfuscation is the adversarial task of preventing a successful verification by altering a text’s style so that it does not resemble that of its original author anymore. This paper introduces new algorithms for both tasks and reports on a comprehensive evaluation to ascertain the merits of the state of the art in authorship verification to withstand obfuscation. After introducing a new generalization of the well-known unmasking algorithm for short texts, thus completing our collection of state-of-the-art algorithms for verification, we introduce an approach that (1) models writing style difference as the Jensen-Shannon distance between the character n-gram distributions of texts, and (2) manipulates an author’s writing style in a sophisticated manner using heuristic search. For obfuscation, we explore the huge space of textual variants in order to find a paraphrased version of the to-be-obfuscated text that has a sufficiently high Jensen-Shannon distance at minimal costs in terms of text quality loss. We analyze, quantify, and illustrate the rationale of this approach, define paraphrasing operators, derive text length-invariant thresholds for termination, and develop an effective obfuscation framework. Our authorship obfuscation approach defeats the presented state-of-the-art verification approaches, while keeping text changes at a minimum. As a final contribution, we discuss and experimentally evaluate a reverse obfuscation attack against our obfuscation approach as well as possible remedies.
On-Chip Continuous-Variable Quantum Key Distribution(CV-QKD) and Homodyne Detection

An on-chip continuous-variable quantum key distribution(CV-QKD) system is integrated using silicon photonics fabrication process and demonstrates the capability of transceiving Gaussian-modulated coherent states and homodyne detection.
Efficient Sample-based Neural Architecture Search with Learnable Predictor

Neural Architecture Search (NAS) has shown great potentials in finding a better neural network design than human design. Sample-based NAS is the most fundamental method aiming at exploring the search space and evaluating the most promising architecture. However, few works have focused on improving the sampling efficiency for NAS algorithm. For balancing exploitation and exploration, we propose BONAS (Bayesian Optimized Neural Architecture Search), a sample-based NAS framework combined with Bayesian Optimization. The main components of BONAS are Sampler and Learnable Embedding Extractor. Specifically, we apply Evolution Algorithm method as our sampler and apply Graph Convolutional Network predictor as a surrogate model to adaptively discover and incorporate nodes structure to approximate the performance of the architecture. For NAS-oriented tasks, we also design a weighted loss focusing on architectures with high performance. Extensive experiments are conducted to verify the effectiveness of our method over many competing methods, e.g. 123.7x more efficient than Random Search and 7.5x more efficient than previous SOTA LaNAS for finding the best architecture on the largest NAS data set NAS-Bench-101.
Global Evaluation of the Status and Sustainability of Terrestrial Landscapes and Water Bodies

The foundations of the methodology, methods of integrated assessment of the state and emergent properties (productivity, sustainability, environmental well-being) of complex natural systems are considered. The aim of the research was to summarize the results of the development and testing of models classifications of an integral assessment of the state, sustainability of terrestrial landscapes, water bodies and river systems and then, at the next stage, an integral assessment of their systemic well-being. Terrestrial landscapes, marine and lake systems and water catchment systems were selected as the objects of the study. The subjects of research were theoretical and methodological aspects and methods for constructing integral indicators of the state (integral indicator of productivity, integral indicator of environmental quality, etc.), integral indicators of sustainability, integral indicators of well-being (health) of land, lake and river systems. The basics of the methodology for assessing the state, stability and well-being of terrestrial and aquatic geosystems are considered, as well as the integral estimation algorithms; the results of assessment studies: an integrated assessment of the trophic status of water bodies; integral assessment of the resistance of water bodies to changes in the parameters of the natural and anthropogenic regimes; paleoreconstruction of hydroecological conditions on the Laptev Sea shelf based on integral indices; integral assessment of the ecological status and ecological well-being of reservoirs, watercourses, water catchment systems; integral assessment of the sustainability of terrestrial landscapes; integral assessment of the stability of a sociological system with a hypothetical change in the quality of the environment on the example of northern regions of Russia. Research was focused on the development of multi-criteria and multilevel classifications for the study of the emergent properties of complex systems in nature and society and the construction of integral indicators based on Analysis and Synthesis of Indicators for Information Deficiency (ASIID) and Aggregated Preference Indices System (APIS) methods and models.
Automated 3D Labelling of Fibroblasts and Endothelial Cells in SEM-Imaged Placenta using Deep Learning

Analysis of fibroblasts within placenta is necessary for research into placental growth-factors, which are linked to lifelong health and chronic disease risk.2D analysis of fibroblasts can be challenging due to the variation and complexity of their structure.3D imaging can provide important visualisation, but the images produced are extremely labour intensive to construct because of the extensive manual processing required.Machine learning can be used to automate the labelling process for faster 3D analysis.Here, a deep neural network is trained to label a fibroblast from serial block face scanning electron microscopy (SBFSEM) placental imaging.
Neural Visual Social Comment on Image-Text Content

Social bots are computer software designed for content production and interaction with humans. With the popularity of images in social networks, social bots need to have visual awareness of image content while only understanding texts is far from enough to be active in social networks. We introduce a novel task, Visual Social Comment (VSC), in which social bots should generate relevant and informative comments on social contents of both images and texts. In this task of multimodal context, our work focuses on how to extract and fuse the information of vision and text to improve the quality of generated comments, and how to deal with the problem that neural dialog models trained with maximum likelihood estimation (MLE) criteria tend to generate generic responses. In order to fuse visual and textual context features closely through the relationship between them, we adopt joint attention of multimodal context to modify the standard sequence-to-sequence (Seq2Seq) framework. We also leverage the topic information transferred from a topic classification model to build a perceptual loss function, which encourages the generative comment model to generate more informative and diverse comments with the topic corresponding to context. The experimental results of models trained with data from Sina Weibo show that comments generated by our proposed models achieve better performance in both relevance and informativeness than those generated by other baseline models.
Patrolling Strategy for Multiple UAVs with Recharging Stations in Unknown Environments

One of the primary concerns related to Unmanned Aerial Vehicles (UAV), especially multirotors, is the limited endurance to perform patrolling missions in large scale applications. The use of recharging stations spread throughout the area of interest is an alternative to prolong the total mission execution time. In such scenario, the UAV performs a coverage task and must return to one of the stations from time to time to recharge or replace the battery. In this paper, we propose a patrolling strategy for multiple UAVs with recharging stations in an unknown environment. The aerial vehicles should patrol the area using the NC-Drone algorithm while searching for a target. They build a matrix-based internal map to register visited places and the positions of the recharging stations. The UAVs are also able to exchange information among each other when they are within a communication range. During the patrolling, the aerial vehicles constantly monitor the energy spent and the remaining energy available in their batteries, according to a previously proposed energy model. We evaluate the proposed approach in three scenarios with different types of targets. The patrolling strategy was able to successfully perform the mission, detecting the targets and safely returning to the recharging stations multiple times. We were able to prolong the mission execution time, avoiding battery exhaustion and unexpected crashes.
Robust Poisson Multi-Bernoulli Mixture Filter With Inaccurate Process and Measurement Noise Covariances

This paper proposes a robust Poisson multi-Bernoulli mixture (PMBM) filter with inaccurate process and measurement noise covariances. A derivation of the robust PMBM filter is provided for jointly estimating the kinematic state, the predicted state covariance, and the measurement noise covariance. By modeling the augmented state as a Gaussian inverse Wishart inverse Wishart (GIWIW) distribution, a computationally feasible implementation of the robust PMBM (GIWIW-PMBM) filter is given for linear Gaussian systems. To guarantee the conjugacy of the GIWIW distribution, the variational Bayesian (VB) approach is employed to approximate the posterior density. Finally, simulation results show that the GIWIW-PMBM filter has the best overall performance compared to existing state-of-the-art filters regarding computational cost and filtering performance.
Design of Digital Test Using Wondershare in Supporting the Blended Learning with Kelase Platform

This research was aimed to provide an overview of digital test design that created using the Wondershare application and then imported into the Kelase platform to realize good blended learning to improve students' cognitive abilities.The approach of this research was development research, which focused on developing digital test instruments.The stages of development include: making the instrument items, validating the contents of instrument items, analyzing the instrument items, making the final items, importing the final items to Wondershare, and importing the digital tests to the Kelase platform.The research subjects that were involved in simulating content validation were two experts.Item analysis was done by determining the score of content validation and that score then compared with the validity categorization table that refers to Guilford.The validation score of the instrument contents was 0.850, so it was classified as excellent validity.The results of this research showed that 17 final items were imported into the Wondershare application and the Kelase platform, while three items were removed.
The Metric for Automatic Code Generation

At present, there are a lot of researches on the metric about static source code. However, there are few studies about automatic code generation. Based on the quality and efficiency of automatic code generation, this paper proposes the metric model including six metric characteristics. Then, three automatic code generation tools are used to measure the experimental source code. Finally, the experimental results showed that the metric of automatic code generation is effective.
Principles of dashboard adaptability to get insights into origin-destination data

Nowadays large amounts of movement data is available. This makes it important not only to be aware of how to collect and store this data, but also how to visually represent the information to get insights and "read" the story behind data. When visualising origin-destination data, the traditional flow map is the solution most often selected. A single flow map, however, does not necessarily show all the available attribute variables and also tends too clutter quickly.A more appropriate solution is a dashboard. It provides users with summaries of the represented information. Despite the dashboard suitability to support getting insights, current dashboards have some limitations regarding the flexibility of the layout. To overcome these limitations, we introduce adaptability in dashboards. In our case adaptability ensures that users get insights into the component of interest (space, time, or attribute) on 3 levels of detail. Adaptability is initiated by user tasks to resulting in changes in the visualizations of represented information and dashboard interfaces. We illustrate the concept of an adaptable dashboard with two case studies.
SecPump: A Connected Open-Source Infusion Pump for Security Research Purposes

This letter presents SecPump, a new open-source wireless infusion pump platform dedicated to security researchers. The novelty of the platform is that it is “plug and play.” Indeed, SecPump simulates a functional infusion pump system on a single board without requiring additional hardware or mechanical components. The presented cyber-physical platform intends to provide a framework for security evaluation, tailored for countermeasure development against security flaws related to medical devices. This letter presents the functionality of the cyber-physical device, its wireless features, and its portability across several hardware architectures. Finally, both hardware and software attacks are showcased on the platform.
Review of Optimization Methods of Medical Image Segmentation

Medical image segmentation is an important component in medical image analysis and diagnosis which is used as a useful application for medical image processing. Image segmentation of medical images has been implemented and studied by numerous researchers in their various research activities. Robustness of the method is all-time challenge in this type of application of medical image processing. The robustness has been addressed by few researchers but still remains challenging task. The performance of existing research work on medical image segmentation is improved by using optimization techniques. This paper studies and presents a critical review of existing research work that has been used for optimizing the segmentation results. An attempt has also been made to suggest a plan for further formulating a more powerful optimization method to optimize the results that could help in the automated diagnosis of different types of medical images.
Detection of Counterfeit Currency using Image Processing Techniques

The growth in the number of fake notes in the system has been tremendous over the past few years. The counterfeiters have keep developing new ways to get as close to the real paper currency as possible. This puts the common masses under grave danger of being robbed of their hard earned money. To overcome this issue, various researchers have tried to come up with different procedures to detect fake notes. In this paper, we will try to understand some of the techniques that are based on image processing and perform a comparative study of the same.