Reviewerly/exHarmony huggingface dataset:
Brief Paper: Drivable Area Detection with Region-based CNN Models to Support Autonomous Driving

In autonomous driving, object recognition based on machine learning is one of the core software technologies.In particular, the object recognition using deep learning becomes an essential element for autonomous driving software to operate.In this paper, we introduce a drivable area detection method based on Region-based CNN model to support autonomous driving.To effectively detect the drivable area, we used the BDD dataset for model training and demonstrated its effectiveness.As a result, our R-CNN model using BDD datasets showed interesting results in training and testing for detection of drivable areas.
Active shooter: An agent-based model of unarmed resistance.

Mass shootings unfold quickly and are rarely foreseen by victims. Increasingly, training is provided to increase chances of surviving active shooter scenarios, usually emphasizing Run, Hide, Fight. Evidence from prior mass shootings suggests that casualties may be limited should the shooter encounter unarmed resistance prior to the arrival of law enforcement officers (LEOs). An agent-based model (ABM) explored the potential for limiting casualties should a small proportion of potential victims swarm a gunman, as occurred on a train from Amsterdam to Paris in 2015. Results suggest that even with a miniscule probability of overcoming a shooter, fighters may save lives but put themselves at increased risk. While not intended to prescribe a course of action, the model suggests the potential for a reduction in casualties in active shooter scenarios.
“Mobility game”: interactive technology for urban planning education

Abstract Modern trends in the development of urban planning education dictate the need to implement new innovative technologies for comprehension of urban processes. The idea «from Smart Cities to Smart Territories» determines the current interest for the introduction of software systems that allow students creating and studying a smart urban environment. The article is devoted to the introduction of interactive technologies to improve the quality of the educational process in the field of urban planning. The relevance of the topic is due to modern trends in the digitalization of the urban environment. The purpose of the study is to determine the role and importance of interactive technologies in the formation of professional competencies of students in the planning and design of a smart city and its transport system. There is the analysis of possibilities of the software complex “Mobility game”. This interactive game allows creating a city and developing its transport infrastructure based on specific quantitative and qualitative parameters of development of the road network, the composition of the transport system and the mobility of the population. The software package takes into account modern trends in the development of smart urban transport, such as changes in the composition of the traffic flow, increasing the number of micro-cars and passenger transport, the rapid development of information technologies for travel support, and allows students visually exploring the interaction and mutual influence of various parameters in practical classes. The application spectrum of this software is broad. In addition to communicating essential relationships between urban development issues and the necessary transport infrastructure in education contexts, local planning situations can also be modelled using defined scenarios. The implementation of interactive technologies is a proven scientific and practice-oriented tool for this purpose.
Random forest application on cognitive level classification of E-learning content

The e-learning is the primary method of learning for most learners after the regular academics studies. The knowledge delivery through e-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professional do focused studies for carrier advancement, promotion or to improve the domain knowledge. These learner can find many free e-learning web sites from the internet easily in the domain of interest. However it is quite difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. User spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. An intelligent framework using machine learning algorithms with Random Forest Classifier is proposed to address this issue, which classifies the e-learning content based on its difficulty levels and provide the learner the best content suitable based on the knowledge level .The frame work is trained with the data set collected from multiple popular e-learning web sites. The model is tested with real time e-learning web sites links and found that the e-contents in the web sites are recommended to the user based on its difficulty levels as beginner level, intermediate level and advanced level.
Artificially Intelligent Persons

Artificial Intelligence (AI) entities seriously challenge traditional legal frameworks for attribution and liability because they operate at an increasing distance from their developers and owners, resulting in accountability gaps. Consider a scenario in which a self-driving car causes injury or even death to a human. Who do we hold accountable? We have no clear answer as to who can be sued or prosecuted because we lack a comprehensive legal understanding of AI entities. Many scholars propose as a solution to the accountability problem attaching liability to the direct source of the harm, the AI entity itself, by first granting it legal personhood. But the law has yet to answer the question of whether AI entities qualify for legal personhood and, if so, on what legal basis. This article is the first to empirically assess the scope of legal personhood as it relates to AI entities, and to answer this question. I make two claims about the problem of legal personhood for AI. First, I argue that the courts’ overall approach to legal personhood has been more disparate than many have assumed, and it does not support legal personhood for AI entities. To substantiate this position, I analyze the legal basis for judicial decisions conferring legal personhood on artificial entities across U.S. courts from 1809 to the present, and I offer a statistical analysis of the frequency with which different conditions for legal personhood appear in these decisions. I find a clear dissonance between legal doctrine and existing theory on legal personhood for AI entities. Second, I argue that empirically understanding the legal landscape for legal personhood prevents courts from conferring legal personhood on AI entities, and should give legislators pause before doing so. If courts and legislators consider the conditions for legal personhood that this article identifies in answering questions of liability for AI entities, they will discover the incompatibility between legal personhood and these entities. Without recognition of this incompatibility, theory, policy, and litigation surrounding AI entities could move in a direction that undermines legal certainty and upsets legal expectation.
Detecting Ransomware in Encrypted Web Traffic

To date, only a small amount of research has focused on detecting ransomware at the network level, and none of the published proposals have addressed the challenges raised by the fact that an increasing number of ransomware are using encrypted channels for communication with the command and control (C&C) server, mainly, over the HTTPS protocol. Despite the limited amount of ransomware-specific data available in network traffic, network-level detection represents a valuable extension of system-level detection as this could provide early indication of ransomware activities and allow disrupting such activities before serious damage can take place. To address the aforementioned gap, we propose, in the current paper, a new approach for detecting ransomware in encrypted network traffic that leverages network connections, certificate information and machine learning. We leverage an existing feature model developed for general malware and develop a robust network flow behavior analysis model using machine learning that separates effectively ransomware traffic from normal traffic. We study three different classifiers: random forest, SVM and logistic regression. Experimental evaluation on a diversified dataset yields a detection rate of 99.9% and a false positive rate of 0% for random forest, the best performing of the three classifiers.
Analysis of Ultrasound Sensor Applicability in AAL Systems for Cooking Process Monitoring

The trend of population aging and increasing number of people with cognitive and other impairments caused more demands in Ambient Assisted Living solutions development. The kitchen space and cooking process are identified as a critical in this domain, from security and assistive aspects. In this paper, we present a short overview of technical solutions related to the cooker monitoring and corresponding sensors. In order to improve existing solutions, we invoke the ultrasound sensor utilization for cookware detection, liquid level measurement and detection of user activities regarding the cooking process. We have setup the testing prototype based on ultrasound sensor and acquired some experimental results, which will be presented.
Survey and Evaluation of Blue-Green Deployment Techniques in Cloud Native Environments

Today, the cloud computing customers assume that the services or applications consumed from the cloud are always on, highly available for uninterrupted utilization. The requirement then for the service providers becomes to minimize the planned maintenance windows duration in order to reduce their repercussions on the service availability for the consumers. We evaluate in this paper the continuous deployment methodology called Blue/Green deployment which aims to support zero maintenance windows, and consequently to avoid any interruption to the end users. Our experiments analyze the most common Blue/Green deployment techniques in the industry, measure and normalize their behavior, and aim to identify the approach with the best performing continuous delivery as compared to the available technologies.
Doing more with less: similarity neural nets and metrics for small class imbalanced data sets

The focus of this article is deep learning on small, class imbalanced data sets in support of explosive hazard detection (EHD) and automatic target recognition (ATR). To this end, we explore artificial neural networks that are driven by similarity versus classification or regression. Similarity can be emphasized via network design, e.g., siamese networks, and/or underlying metric, e.g., contrastive or triple loss. The general goal of a similarity neural network (SNN) is discriminative training via focusing on similarity between tuples of like (and unlike) inputs. As such, SNNs have the potential to learn improved solutions on small data; aka &#100;o more with less". Herein, we explore different avenues and we show that SNNs are essentially neural feature extractors followed by k-nearest neighbor classification. Instead of experimenting on a government data set that cannot be shared, we instead focus on benchmark community data sets for sake of reproducible research. Preliminary findings are encouraging as they suggest that SNNs have great potential for tasks like EHD and ATR.
Prospective Comments on Performance Prediction for Aviation Psychology

Over the past decade, research scientists within the Air Force Research Laboratory’s (AFRL’s) Cognitive Models and Agents branch have been developing and iteratively refining the Predictive Performance Equation (PPE). PPE has been validated across a variety of domains, from simpler laboratory tasks such as paired associate learning to more complex and critical applied domains such as medical skills training. PPE is based on three fundamental findings of the human memory system. First, performance increases with amount of practice—the power law of learning. Second, performance drops with elapsed time since practice occurred—the power law of forgetting. Third, memory and skill retention improves when practice is distributed over time—the spacing effect. These dynamics emerge in PPE through a series of mathematical equations, which are formally documented in recent publications. Here we represent them in a pseudo-equation form for ease of interpretation.
Data Fusion algorithms to improve test range sensors accuracy and precision

Data fusion technology is used in many research areas, such as image processing, automotive, robotics, defense and aerospace. The need to fuse data from different sensors reflects the demand for enhanced measurements precision. Every sensor has pro and cons, data fusion technology for tracking purposes in a test range is supposed to leverage the highest quality features of each sensor to obtain a better position estimation. This paper presents NT multi-sensor tracking algorithm, it is developed to receive target position information from different tracking sensors, radar and electro-optical, and use data fusion technology to obtain better quality data compared to the one provided by a single sensor alone. The algorithm uses a fusion technique which permits a huge performance boost: it associates a weight to every sensor calculated using Kalman filters, properly modeled, to estimate sensor measurements deviations from the estimated target position. These weights help the algorithm understanding which sensor is more reliable and to estimate the best possible position, which is very close to the real target position. Real case tracking scenarios are presented, where the algorithm is tested and compared with reference trajectory data to estimate its accuracy.
Using Internet of Things for Automatic Student Assessment during Laboratory Experiments

A Learning Management Systems (LMS) is a tool for creating, distributing, tracking, and managing various types of educational material. In a previous work, the authors proposed a framework for a future LMS enhanced by Internet of Things (IoT) capabilities. The authors outlined the expected enhancements that IoT will bring to the LMS functionalities. In this paper, the authors explain the detailed implementation of an LMS application that enables a lab instructor to assess students’ performance automatically during and after lab experiments. The authors explain the implementation details of the application, including the software; hardware; and IoT devices and sensors that were used to realize the application objectives and the methods used to connect the LMS to the IoT equipment. In addition, the authors explain the experiments that were made to test the efficiency of the application, which was implemented on top of the Moodle LMS using a custom Plugin that was used to connect Moodle to the IoT tools and execute the functionalities of the application.
Assessing the Information Security Awareness of Employees in PT ABC Against International Organization for Standardization (ISO) 27001:2013

The main purpose of information security is maintaining information assets that are owned by an organization, such as confidentiality, integrity, and availability (known as CIA). In maintaining information assets, a company usually manages information security by making and implementing an Information Security Management System (ISMS) policy. A widely used and applied ISMS policy in Indonesia is ISO/IEC 27001 (International Organization for Standardization/International Electrotechnical Commission). Indonesian telecommunications company PT ABC has implemented the ISO/IEC 27001:2013 standards and procedures. The company conducts an audit once a year to maintain the level of compliance with ISO/IEC 27001:2013. However, only a few people are involved in conducting audits, and it is still unknown how many employees are aware of the company’s information security. This research focused on assessing how much information security awareness exists within PT ABC. Questionnaires were distributed in two departments of the company: supply chain management and service delivery of the Jakarta operations network. This research also examined company documents and surveillance audits in 2018. The employees were grouped based on their length of employment. The results of the questionnaires, with an error margin of 6%, were further compared with the results of the surveillance audit. Our data show that most employees who have worked at the company for more than six years understood and implemented ISO 27001 controls. Meanwhile, companies still need to socialize ISO to employees who have worked at the company for just one to two years.
Designing Process-Centric Blockchain-Based Architectures: A Case Study in e-voting as a Service

This article aims at introducing a new process-centric, trusted, configurable and multipurpose electronic voting service based on the blockchain infrastructure. The objective is to design an e-voting service using blockchain able to automatically translate service configuration defined by the end-user into a cloud-based deployable bundle, automating business logic definition, blockchain configuration, and cloud service provider selection. The architecture includes process mining by design in order to optimize process performance and configuration. The article depicts all the components of the architecture and discusses the impact of the proposed solution.
Hierarchical LTL-Task MDPs for Multi-Agent Coordination through Auctioning and Learning

Given a temporal behavior specification and a team of agents available for execution in a stochastic environment, it is still an open problem how to efficiently decompose and allocate the specifica ...
Learning Entangled Single-Sample Distributions via Iterative Trimming

In the setting of entangled single-sample distributions, the goal is to estimate some common parameter shared by a family of distributions, given one \emph{single} sample from each distribution. We study mean estimation and linear regression under general conditions, and analyze a simple and computationally efficient method based on iteratively trimming samples and re-estimating the parameter on the trimmed sample set. We show that the method in logarithmic iterations outputs an estimation whose error only depends on the noise level of the $\lceil \alpha n \rceil$-th noisiest data point where $\alpha$ is a constant and $n$ is the sample size. This means it can tolerate a constant fraction of high-noise points. These are the first such results for the method under our general conditions. It also justifies the wide application and empirical success of iterative trimming in practice. Our theoretical results are complemented by experiments on synthetic data.
Build and Conquer: Solving N Queens Problem using Iterative Compression

The N queens problem is a very hot topic for research use. Nonetheless all previous algorithms that solved the N queens problem treat each order of it as if they are separate problems with no connection among them. The main idea of this paper is to connect the dots among various orders of the N queens problem and demonstrate that all orders of the N-queens problem are connected with each other. And then we propose an algorithm using iterative compression to solve the problem i.e., beginning from the solution of the least order of the problem and then by using the relation among different orders we add a queen in every iteration until we have the ${N}$ queens residing in the NxN board.
Moving Particle Semi-Implicit Method for Control of Swarm Robotic Systems

The advancements of Multiple Robot Systems (MRS) have shown advantages over single robot systems. Generally, the motivations for the development of MRS are task flexibility, time efficiency, and single-point failure resiliency. The challenge in MRS, however, is the control and coordination of all the members in the system when performing tasks. Swarm robotics is a branch of MRS that deals with groups of homogeneous robots. The goal of swarm robotics is to produce systems that are scalable, flexible, and robust. The control of swarm robotic systems, however, looks to be one of the main challenges. These control concepts are inspired by biological swarms and, more recently, physics concepts. The success of the swarm’s control algorithm will also lead to the swarm’s ability to perform cooperative tasks. The use of homogeneous robots in swarm systems makes it advantageous to model the swarm robots as particles in a fluid. The Moving Particle Semi-Implicit (MPS) Method, a particle-based method in fluid dynamics, is proposed to be used as a control algorithm for swarm robotics.
Temporal 3D Human Pose Estimation for Action Recognition from Arbitrary Viewpoints

This work presents a new view-invariant action recognition system that is able to classify human actions by using a single RGB camera, including challenging camera viewpoints. Understanding actions from different viewpoints remains an extremely challenging problem, due to depth ambiguities, occlusion, and large variety of appearances and scenes. Moreover, using only the information from the 2D perspective gives different interpretations for the same action seen from different viewpoints. Our system operates in two subsequent stages. The first stage estimates the 2D human pose using a convolution neural network. In the next stage, the 2D human poses are lifted to 3D human poses, using temporal convolution neural network that enforces the temporal coherence over the estimated 3D poses. The estimated 3D poses from different viewpoints are then aligned to the same camera reference frame. Finally we propose to use a temporal convolution network based classifier for cross-view action recognition. Our results show that we can achieve state of art view-invariant action recognition accuracy even for the challenging viewpoints by only using RGB videos, without pre-training on synthetic or motion capture data.
Applications of the Projective Plane in Coding Theory

The goal of this paper was to study the applications of the projective plane PG (2, q) over a Galois field of order q in the projective linear (n, k, d, q) -code such that the parameters length of code n, the dimension of code k, and the minimum distance d with the error-correcting e according to an incidence matrix have been calculated. Also, this research provides examples and theorems of links between the combinatorial structures and coding theory. The calculations depend on the GAP (groups, algorithms, and programming) system. The method of the research depends on the classification of the points and lines in PG (2, q).
A Corporative System of Edge Mapping and Hybrid Path A*-Douglas-Pucker Algorithm Planning Method

This paper proposes a corporative system of the edge mapping and hybrid path planning. The mapping process is predicated on modified canny edge detection algorithm. The presented mapping algorithm is found to be adaptable to various scenarios; it is tested on several cases to extract the impediments as well as the constrained region of the map. The generated map is a 2D edge map that contains the information about starting point, goal and obstacles. After that, the outcome of the edge map is processed in path planning algorithm in order to identify the optimum path. The proposed path planning is a hybrid A*-Douglas pucker algorithm. The presented path planning algorithm is reduced the set points and computational time.