Miracle-dz/UniFAR-FTUs huggingface dataset:
DroneSAR: extending physical spaces in spatial augmented reality using projection on a drone

Spatial Augmented Reality (SAR) transforms real-world objects into interactive displays by projecting digital content using video projectors. SAR enables co-located collaboration immediately between multiple viewers without the need to wear any special glasses. Unfortunately, one major limitation of SAR is that visual content can only be projected onto its physical supports. As a result, displaying User Interfaces (UI) widgets such as menus and pop-up windows in SAR is very challenging. We are trying to address this limitation by extending SAR space in mid-air. In this paper, we propose Drone-SAR, which extends the physical space of SAR by projecting digital information dynamically on the tracked panels mounted on a drone. DroneSAR is a proof of concept of novel SAR User Interface (UI), which provides support for 2D widgets (i.e., label, menu, interactive tools, etc.) to enrich SAR interactive experience. We also describe the implementation details of our proposed approach.
Taming Extreme Heterogeneity via Machine Learning based Design of Autonomous Manycore Systems

To avoid rewriting software code for new computer architectures and to take advantage of the extreme heterogeneous processing, communication and storage technologies, there is an urgent need for determining the right amount and type of specialization while making a heterogeneous system as programmable and flexible as possible. To enable both programmability and flexibility in the heterogeneous computing era, we propose a novel complex network inspired model of computation and efficient optimization algorithms for determining the optimal degree of parallelization from old software code. This mathematical framework allows us to determine the required number and type of processing elements, the amount and type of deep memory hierarchy, and the degree of reconfiguration for the communication infrastructure, thus opening new avenues to performance and energy efficiency. Our framework enables heterogeneous manycore systems to autonomously adapt from traditional switching techniques to network coding strategies in order to sustain on-chip communication in the order of terabytes. While this new programming model enables the design of self-programmable autonomous heterogeneous manycore systems, a number of open challenges will be discussed.
Evaluation of Factors Affecting Workplace Accident at MUTU Coal Company using Human Factor Analysis and Classification System for Coal Mines

The coal mining industry has some unique characteristics, that distinguish this industry with other industries: capital intensive, technology-intensive, and has high risk. A workplace accident is one of the risks that has high importance because can cause various losses such as material loss, disruptions to the production process and damages to the environment. MUTU coal company, a company engaged in the coal mining sector in Indonesia, is experiencing an increasing number of workplace accident by 34.7% from 2017 to 2018. The workplace accident caused by various factors from various sources or levels, starting from the level of operators, supervisors, management, and company. Human Factor Analysis and Classification Systems Method for Coal Mines (HFACS-CM) was used in this study to analyze human factors that cause workplace accident, starting from the level of unsafe acts, preconditions for unsafe acts, unsafe leadership, organizational influences, and external environmental factors. Structural Equation Modeling (SEM) was constructed to analyze the relationship between factors affecting workplace accident based on the HFACS-CM model. The result proved that external environmental has a significant impact on unsafe acts, preconditions for unsafe acts have a significant impact on unsafe acts, organizational influences have a significant impact on unsafe leadership, unsafe leadership has a significant effect on the preconditions for unsafe acts, and external environment is significantly related to organizational influences.
Container-based real-time scheduling in the Linux kernel

In recent years, there has been a growing interest in supporting component-based software development of complex real-time embedded systems. Techniques such as machine virtualisation have emerged as interesting mechanisms to enhance the security of these platforms, while real-time scheduling techniques have been proposed to guarantee temporal isolation of different virtualised components sharing the same physical resources. This combination also highlighted criticalities due to overheads introduced by hyper-visors, particularly for low-end embedded devices. This led to the need of investigating deeper into solutions based on lightweight virtualisation alternatives, such as containers. In this context, this paper proposes to use a real-time deadline-based scheduling policy built into the Linux kernel to provide temporal scheduling guarantees to different co-located containers. The proposed solution extends the SCHED_DEADLINE scheduling policy to schedule Linux control groups, allowing user threads to be scheduled with fixed priorities inside the control group scheduled by SCHED_DEADLINE. The proposed mechanism can be configured via control groups, and it is compatible with commonly used tools such as LXC, Docker and similar. This solution is compatible with existing hierarchical real-time scheduling analysis, and some experiments demonstrate consistency between theory and practice.
Improvement of surface temperature control system based on fuzzy adaptive PID algorithm

The thermostatic control system of traditional dough mixer has weak adaptive ability and large overshoot. In order to improve the self-adjusting ability of the constant temperature control system of the dough mixer, we have carried out many experimental analyses and finally come to the conclusion that the fuzzy self-adaptive PID algorithm can effectively improve the constant temperature system of the dough mixer. Traditional PID algorithm is composed of proportional unit P, integral unit I and differential unit D. Once the parameters P, I and D are established, they cannot be changed. Therefore, this algorithm has a weak adaptive ability and cannot be changed according to the environment. The fuzzy adaptive PID algorithm used in this paper refers to the input error e(t) and error change rate ec(t). Fuzzy reasoning is carried out by using fuzzy rules, and the fuzzy matrix table is consulted to meet the adaptive adjustment of PID parameters at different times, so as to achieve the adaptive PID algorithm. For the temperature control system of dough mixer, PID algorithm and fuzzy adaptive PID algorithm are used to construct the constant temperature system respectively. Based on the specific environment of the dough mixer, after three times of comparative experimental analysis, the final conclusion is that the fuzzy self-adaptive PID algorithm overshoot is small, which can reach the stable range faster, and the accuracy is improved by 0.6% compared with the traditional PID algorithm.
Follow the Rays: Understanding the Interplay between Environment and System through In-Situ Wireless Modelling

The Internet of Things and Cyber-Physical Systems rely on wireless interconnections. The importance of understanding the behaviour of wireless communication in practice has promoted many experimental studies, characterising signal propagation in specific environments. Despite the gathered knowledge, the perceived discrepancy between abstract models and actual network performance has supported the belief that system design and debugging can rely only on direct experience and trial-and-error. As a result, reasoning about wireless systems is nowadays a tedious, manual process. This work walks the playground between model and reality to make wireless networks understand their own behaviour in the environment where they operate. First, we introduce a practical method able to efficiently and accurately characterise wireless signal propagation in 3D environments with obstacles. We then exploit the model to make systems autonomously derive the impact of obstacles such as doors and windows on the link conditions. With an outline of the operational scenario and measurements from deployed devices, it is possible to effortlessly generate a situated, dynamic wireless map. We demonstrate our approach in an indoor testbed, showing its practicability and effectiveness. Our evaluation shows that our approach attributes up to 91% of the measurements to the corresponding obstacles correctly. We also show that different modelling techniques have an impact on such detection accuracy, promoting the use of 3D environment descriptions.
Experimental study of effects of grid screens on reduction of fan noise

In many application scenarios of fans, inlet flow distortion usually caused by the structures at the inlet, is unavoidable. The distorted inlet flow would induce tonal noise at the frequency of the rotation, and the turbulence of the distorted inlet flow would have contribution to the broadband noise of the fan. In this study, a passive method of inlet flow conditioning with grid screen is experimentally studied. The study the acoustic benefit of the screen was tested on a small axial-flow fan with inlet flow distortion in an anechoic chamber. Theoretical analysis was conducted on the parameter selection of the screen, and on the loss that the screen brought along with the acoustic improvement. The result shows that, it is clear that with the flow resistance induced by the grid screen, the aerodynamic performance of the fan is weakened, but the effect is not significant. Due to the effects on suppressing wake velocity deficit and turbulence intensity, the noise level of the fan induced by the inlet flow distortion is reduced, even with the aerodynamic performance compensated by increasing the rotary speed. The effect of noise reduction increases with the decrease of the porosity of the screen.
Infrared small target detection based on salient region extraction and gradient vector processing

Infrared small target detection plays a significant role in modern infrared search and track systems(IRST) and has also received much attention. Based on this background, a novel approach to detect infrared small target based on salient region extraction (SRE) and gradient vector processing (GVP) is proposed in this paper. This approach consists of three parts: pre-processing based on fast median filtering, SRE based on spectral residual algorithm (SRA) processing and similarity analysis algorithm (SAA) processing, and target detection in infrared gradient vector field (IGVF). Both theory and experimental have shown that the proposed algorithm can detect the target more robustly and accurately, and spend less time processing compared with the existing infrared small target detection algorithm. It is a small infrared target detection method with superior performance and good application prospect.
Exploring proprioceptive take-over requests for highly automated vehicles

The uprising levels of autonomous vehicles allow the drivers to shift their attention to non-driving tasks while driving (i.e., texting, reading, or watching movies). However, these systems are prone to failure and, thus, depending on human intervention becomes crucial in critical situations. In this work, we propose using human actuation as a new mean of communicating take-over requests (TOR) through proprioception. We conducted a user study via a driving simulation in the presence of a complex working memory span task. We communicated TORs through four different modalities, namely, vibrotactile, audio, visual, and proprioception. Our results show that the vibrotactile condition yielded the fastest reaction time followed by proprioception. Additionally, proprioceptive cues resulted in the second best performance of the non-driving task following auditory cues.
Evaluation of attention inducing effects using ubiquitous humanlike face robots

A human gaze not only has the meaning that someone is looking at something, but also has various effects on the surrounding people. For example, previous studies have shown that attention is induced in the gaze direction of others and that consciousness of being seen by others affects human behavior. In the future, android robots will play an active role in society. We can predict that communication and information transmission with such gaze information will be possible between humans and robots. However, to the best of our knowledge, no study has yet investigated the effect of robot gazes on people in a situation where many robots exist ubiquitously in daily life. Therefore, in this study, we aim to evaluate the attention inducing effect of the gaze of the face robots installed in the daily living environment. In this paper, we present several examples of services in a situation, where face robots are established ubiquitously in daily life. We verified the attention inducing effect in three test cases. As a result of the experiment, the number of times when the participants focused their attention in the direction of the gaze of the face robot was small. However, the feasibility of the service was demonstrated through the results of the experiment, such as surrounding situation, user's consciousness about the face robots, and attention induction timing.
Stochastic Modeling and Simulation for Redundancy and Coexistence in Graphs Resulting from Log-Normal Shadowing

Recent studies have identified redundancy and coexistence as a supporting graph structure for building connected intersection free planar drawings in wireless network graphs. Empirical evidence suggests that under certain conditions these properties can be assumed to hold with high probability. In this paper we advance insight on these probabilities with a rigorous stochastic treatment studying randomly generated network graphs pertaining to the log-normal shadowing model. We derive nested integral expressions to compute probabilities of redundancy and coexistence numerically under the standard uncorrelated log-normal shadowing model. For a recent model extension including correlation among network links our findings provide a means for efficient stochastic simulation of these probabilities. We illustrate numerical and simulation application of the derived formulae with a comprehensive parameter study for redundancy and coexistence under correlated and uncorrelated log-normal shadowing modeled randomly generated graphs. We also demonstrate how far support for these properties can be improved by artificially cutting communication distance while keeping the network well connected in terms of percolation bounds.
In-Database Geospatial Analytics using Python

The amount of spatial data acquired from crowdsourced platforms, mobile devices, sensors and cartographic agencies has grown exponentially over the past few years. Nearly half of the spatial data available currently are stored and processed through large relational databases. Due to a lack of generic open source tools, researchers and analysts often have difficulty in extracting and analyzing large amounts of spatial data from traditional databases. In order to overcome this challenge, the most effective way is to perform the analysis directly in the database, which enables quick retrieval and visualization of spatial data stored in relational databases. Also, working in-database reduces the network overhead, as users do not need to replicate the complete data into their local system. While a number of spatial analysis libraries are readily available, they do not work in-database, and typically require additional platform-specific software. Our goal is to bridge this gap by developing a new method through an open source software to perform fast and seamless spatial analysis without having to store the data in-memory. We propose a framework implemented in Python, which embeds geospatial analytics into a spatial database (i.e. IBM DB2 ®). The framework internally translates the spatial functions written by the user into SQL queries, which follow the standards of Open Geospatial Consortium (OGC) and can operate on single as well as multiple geometries. We then demonstrate how to combine the results of spatial operations with visualization methods such as choropleth maps within Jupyter notebooks. Finally, we elaborate upon the benefits of our approach via a real-world use case, in which we analyze crime hotspots in New York City using the in-database spatial functions.
Using Grover's search quantum algorithm to solve Boolean satisfiability problems, part 2

In the first part of this article, 1 we explained how to perform a quantum formulation of the classical satisfiability (SAT) problem using the well-known Grover’s quantum search algorithm [1]. The quantum search operated by the classical Grover is composed by a black box (oracle model) able to acknowledge x0 such that f(x0) = 1 and f(x) = 0 for all other values of x. This feature transposed to a SAT oracle allows the execution of computations to find possible solutions of a conjunctive normal form (CNF). Moreover, Grover search is known for speeding up the solution to NP-complete problems such as SAT. Therefore, in part two of this article we walk through an actual implementation proposed for solving a three-clauses, three-literals SAT problem using the IBM Qiskit (Quantum Information Science Kit) environment. We resort to near-term quantum devices available in the IBM Q Experience cloud service and provide a full implementation using the Python programming language and the IBM quantum assembly (QASM) [2].
Performance-aware load shedding for monitoring events in container based environments

Runtime monitoring tools have become fundamental to assess the correct operation of complex systems and applications. Unfortunately, the more precise is the monitoring (sampling rate, information granularity, and so on), the higher is the overhead introduced in the system itself. In this paper, we propose a new load shedding framework that enables runtime adaptation of monitoring agents under heavy system load, exploiting an heuristic Load Manager to control the agent status and a runtime support for domain-specific policies. We implemented the proposed methodology on Sysdig, with an average control error improvement of 3.51x (12.25x at most), w.r. t. previous solutions.
Analysis of On-Board Passenger Information Display Interface in Mass Transportation Modes Based on Emotional Design

Telecommunication facilities as a media of delivering information are a mandatory facility in rail-based transportation modes. Delivering information in transportation modes can be done through visual-based information, the On-Board Passenger Information Display. Information that is shown to the passengers can affect trust, safety, travel, and comfort, so it is very important to design an On-Board Passenger Information Display that is suitable to the user's desire. Therefore, this study aims to analyze the On-Board Passenger Information Display interface that is suitable for the user's emotional perspective. The method used is Kansei Engineering through factor analysis and association rules. Kansei Engineering is used to translate the user's feeling into design specification of the user interface. Association rules inferred the relationship of a combination of design elements and attributes with the degree of emotion. This study produced three designs from Kansei words, they were 'Futuristic', 'Simple' and 'Dynamic'. Based on the test results done by performance metrics, self-reported metrics, issued based metrics, and eye tracking, Kansei word "Simple" has the highest ranking and can be classified as the most suitable On-Board Passenger Information Display interface to be applied.
Review of SLAM Techniques For Autonomous Underwater Vehicles

Simultaneous Localization and Mapping (SLAM) is an important method for autonomous positioning and navigation of robots. SLAM application in underwater area has become an active field of research. Firstly, this paper presents the research status of SLAM technology follow by its application on underwater vehicles. Then, the implementation methods and main difficulties of underwater SLAM are explained. Finally, the research trend of underwater SLAM in the future is discussed.
An In-depth Analysis of the Impact of Battery Usage Patterns on Performance of Task Allocation Algorithms in Sparse Mobile Crowdsensing

Mobile Crowdsensing leverages the sensing capabilities of multiple mobile devices to execute large-scale sensing tasks by breaking them into smaller tasks for execution on individual mobile devices. Task allocation algorithms are used to efficiently distribute these smaller sensing tasks to a subset of participants while optimizing system-level goals (such as location accuracy or data quality) for participant selection. The sensing tasks, e.g., collecting GPS tagged data, are often energy-intensive and battery consumption during sensing task execution remains a major concern for participants. So far no in-depth study exists that evaluates the impact of battery consumption on allocation algorithms. In this work, we conducted an in-depth study on the effects of battery consumption patterns of smartphone users. We studied the impact of battery consumption patterns extracted from a real-world data-set on standard as well as state-of-the-art algorithms to show how different battery usage patterns affect the performance of allocation algorithms. Our work provides an important insight into factors affecting the performance of allocation algorithms and advocates incorporating battery usage patterns for the future development of these algorithms.
Research on Data Uncertainty and Lineage Through Trio

It is an indisputable fact that the amount of data has grown exponentially, and the resulting uncertainty of data has become more and more common in recent years, which has brought many negative effects to our life and scientific research. Through nearly a decade of research, the Trio team developed a relatively mature system prototype Trio to demonstrate their solutions to uncertain data and theoretically demonstrated. The Trio system supplements and expands the traditional relational database. In order to inherit the many advantages of the relational database, an improved ULDB data model and corresponding TriQL query language for uncertain databases are proposed. Studying a lot of Stanford's Trio project related materials, this article has mainly done the following work: 1. Analyze and organize the logical relationships in the complex Trio system, so that readers have a clearer understanding of their proposals in a simple way; 2. Analyze and compare the data model and query implementation between traditional database and extended database with uncertainty; In addition, the paper also simplifies the working principle and implementation of the complex Trio system, provides convenience and reference for further research, and provides a new perspective for the current research in the field of uncertain data.
Spatial Issues in Modeling LoRaWAN Capacity

All existing models for analyzing the performance of LoRaWAN assume a constant density of nodes within the gateway range. We claim that such a situation is highly unlikely for LoRaWAN cells whose range can attain several kilometers in real-world deployments. We thus propose to analyze the LoRa performance under a more realistic assumption: the density of nodes decreases with the inverse square of the distance to the gateway. We use the LoRaWAN capacity model by Georgiou and Raza to find the Packet Delivery Ratio (PDR) for an inhomogeneous spatial distribution of devices around a gateway and obtain the number of devices that benefit from a given level of PDR. We analyze the LoRaWAN capacity in terms of PDR for various spatial configurations and Spreading Factor allocations.