System for Remote Collaborative Embedded Development

This paper explores the challenges and devised solutions for embedded development which arose during the COVID-19 pandemic. While software development, nowadays with modern tools and services such as git, virtual machines and commu-nication suits, is relatively una˙ected by resource location. That is not the case for firmware and embedded systems, which relies on physical hard-ware for design, development, and testing. To overcome the limitations of remote work and ob-structed access to actual hardware, two ideas were implemented and tested. First, based on inte-grated circuit emulation using QEMU to emulate an ARM core and custom software to facilitate communication with the embedded system. Sec-ond, remote programming and debugging over the internet with a dedicated computer system acting as a middle man between a development environ-ment and physical hardware using OpenOCD de-bugger.

The Influence of Ecological Variables on Archaeological Site Density in the Owyhee Region, Southwest Idaho

Vegetational resources are reported to have had multiple uses in indigenous groups who were present in the Great Basin area throughout the Archaic periods. Resource acquisition and position of resources is documented to have had impacts on settlement patterns, but the impact of the range of vegetational resources, specifically, is lacking thorough study in the northern Great Basin area. Due to fluctuating climates, modern development, and other factors both anthropogenic and otherwise, Archaic vegetation ranges may not be wholly visible in the same locations today; however, the environments surrounding sites may be determined by observing a variety of ecological variables, including soil type, hydrology, slope, and elevation. Using Owyhee County, Idaho for an example, this study seeks to evaluate if known locations of archaeological sites have any visible correlation to four variables reported to have critical importance to the ecology and ranges of vegetation communities: soil type, groundwater accessibility, slope, and elevation. I analyze how ecological variables heavily associated with vegetation types can be mapped against known archaeological resource location ‘hotspots’, and use them to create a well-informed analysis of the vegetations correlated with these variables and estimate a general assessment of the resources most likely to have been available in these locations. Observing how these variables are associated with vegetation that correlates to documented ethnographic usages, this thesis advances possible factors that influence the selection of residential, temporary camp, and resource-specific processing site locations, and provides strong evidence for the need to consider environmental factors when conducting archaeological surveys.

Semi-supervised learning approach for malicious URL detection via adversarial learning1

Uniform Resource Location (URL) is the network unified resource location system that specifies the location and access method of resources on the Internet. At present, malicious URL has become one of the main means of network attack. How to detect malicious URL timely and accurately has become an engaging research topic. The recent proposed deep learning-based detection models can achieve high accuracy in simulations, but several problems are exposed when they are used in real applications. These models need a balanced labeled dataset for training, while collecting large numbers of the latest labeled URL samples is difficult due to the rapid generation of URL in the real application environment. In addition, in most randomly collected datasets, the number of benign URL samples and malicious URL samples is extremely unbalanced, as malicious URL samples are often rare. This paper proposes a semi-supervised learning malicious URL detection method based on generative adversarial network (GAN) to solve the above two problems. By utilizing the unlabeled URLs for model training in a semi-supervised way, the requirement of large numbers of labeled samples is weakened. And the imbalance problem can be relieved with the synthetic malicious URL generated by adversarial learning. Experimental results show that the proposed method outperforms the classic SVM and LSTM based methods. Specially, the proposed method can obtain high accuracy with insufficient labeled samples and unbalanced dataset. e.g., the proposed method can achieve 87.8% /91.9% detection accuracy when the number of labeled samples is reduced to 20% /40% of that of conventional methods.

Design and implementation of computer aided resource management system for dance teaching

Traditional teaching methods are limited to time and place, and the performance of dance teaching resources management is poor. Design a computer-assisted dance teaching resource management system. The functional structure of the system includes core computer-assisted teaching and teaching management applications. The data management module is used to store the processed data in data files, and the dance teaching content release module retrieves requests and multimedia. The remote image resource location request of the management module responds to the feedback. In order to improve the management of computer-aided dance teaching resources, this article takes dance robots as the research object, takes dance video information as input, uses deep learning methods to estimate the human body posture in the video, and obtains the key point position coordinates of the human body; The inverse kinematics calculation of the robot obtains the angle values of each joint of the robot, and the angle values of the lower body joints are adjusted to maintain the balance of the robot. In addition, this paper also proposes a method to automatically generate robot dance sequence. Gated cyclic unit (GRU) network is used to learn the correlation between the global characteristics of music and dance gesture relationship characteristics, the correlation between music local characteristics and dance movement density characteristics, and then combine the dance movement graphs to sample and plan Robot dance moves synchronized with the beat. Experimental results show that whether it is robot dance movement imitation or dance movement generation, it can improve the computer-aided management of dance teaching.

Emergency Resource Location and Allocation in Traffic Contingency Plan for Sports Mega-Event

In view of the transactional and textual features on issue handling in mega-event traffic contingency plan, this paper gives a quantitative method for emergency resources location and allocation. Given that the requirement on safeguards in the sports mega-events is temporary and stringent, we first divide the facilities into temporary emergency facilities and fixed emergency facilities and the resources into material resources and human resources. Considering the uncertainty of emergency incidents, we then construct a mixed integer linear programming model. To solve this model, the bisection method is used to import the material quantity placed in each emergency facility, and the shortest path algorithm is used to import the rescue time matrix. Considering the slowness of convergence rate when the road network is large, a modified matrix real-coded genetic algorithm is designed with the crossover operator based on a greedy algorithm. The application of the model and algorithms is validated by the case based on 2022 Beijing Winter Olympics. Sensitivity analysis of some important parameters is also conducted to provide insights for traffic emergency resources management in sports mega-event.

Cloud-Based Smart City Using Internet of Things

The cloud-based smart city is a way to provide resources and data on demand. Two technologies used to build cloud-based smart city, IoT, and cloud computing are explored. Using smart sensors can capture the movement of the environment, humans, and city infrastructure like building maintenance, traffic control, transportation, pollution monitoring. This is possible through IoT. Future movement could be predicted based on present and past data. Cloud computing is used for cloud storage. Using cloud, users can access resources in virtual mode at any time or anywhere. It can be accessed at different locations at the same time through high speed internet. Cloud is managed by a third party. Users don't have any knowledge regarding resource location and data, such as where user data is stored. Users use cloud service in virtual mode. Basically, cloud is a service provider platform that provides resources and data storage facility in a virtual way; users don't need to purchase resources.

Do the Stripes on Three-Dimensional Models Overcome the Odor Signals in Tabanids Landing Choice?

Information about the important factors in tabanid flies visual orientation to hosts has been largely derived from experimental modifications of visual traps and decoys. In the present study performed in wood pasture (Hästhult), southern Sweden, three-dimensional striped models resembling the shape of Zebra, Bongo, Kudu and four control models of different homogenous colors (black, white, reddish-brown and brown) were baited with acetone and aged cow urine in order to test the behavioral preferences in terms of visual and olfactory stimuli in host-seeking tabanids. Attraction of tabanid flies to these models (3D) was high, possibly due to the greater visibility from several directions and also from a greater distance. Vision is important in activating, orienting tabanid flies to the host, as well as for their decision whether and where to land. This research revealed that the visual cues such as stripes on striped models became increasingly important in directing tabanids landing and searching behavior at close range. Likewise, the tabanids approach to attractants sources was overridden by visual cues (stripes) at greater extent compared with the more attractiveness to homogenous colors on control models. Moreover, the visual stimuli (stripes) played also a supplementary role, modifying the selection of landing area on striped model (land on homogenous color part in Bongo and Kudu) once alighting responses were initiated by odor. Tabanid species, especially Haematopota pluvialis and Tabanus bromius exhibited a preference for landing mostly on reddish-brown control model when given the choice of other colors. However, the complex interaction of attractants and visual cues (stripes, color, shape) in the later stages of resource location, remains relatively little studied in all species of tabanids.

New generation of sensors for landslide observation: first results

<p>Rupture processes comprehension and dynamics of slope movements have been studied for several decades through surface observations of unstable objects (INSAR, LIDAR, geomorphological...) and very punctually in the slid masses (inclinometric survey). That kind of observations usually requires heavy amenities that are energy-consuming, vulnerable, and very expensive. We have developed within a public-private partnership a new generation of connected sensors. In this paper, we present a set of displacement data collected on active landslides located in the Alpes-Maritimes region of France. This is a region subject to intense climatic forcing, in areas of high vulnerability, and potentially a hotspot of climate change in the coming years. This climate, referred as North Mediterranean, is defined by intense rainfall (>100mm/day). This territory is particularly vulnerable due to its abrupt pre-Alps reliefs, which are located very close to the sea, and also constrained by strong urban pressure.</p><p>The acquisition of good-quality observational data and the installation of sensors on this type of landslide remain a difficult scientific challenge which is full of compromises in an attempt to obtain, in the long term, effective warning systems. The accessibility of the study site, its lithologic and hydrogeological complexities, and the management of the installed sensors (energy resource, location representative of the mass, cost ...) are issues to the development of these systems.</p><p>Two sites instrumented during 2019 suffered from heavy weather during autumn (cumulative rainfall of more than 800 mm over 2 months), causing an acceleration of the displacements, and allowing us to watch the transition from the latency phase to the gravitational paroxysm. This period of severe weather is part of a succession of climatic events that we call "Mediterranean events", producing cumulative rainfall in a few hours/days/weeks higher than the yearly normals.</p><p>The data set presented and discussed consists of (1) meteorological observations (with a focus on rainfall accumulation), (2) piezometric observations (subsurface ground water level and conductivity), (3) borehole inclinometer measurements, (4) GNSS displacement observations (daily solutions), (5) displacement observations between two points using laser rangefinders, and (6) surface clinometric observations.</p><p>This new generation of sensors increases the frequency of measurement, which makes it possible to visualize the “life of the slope” and thus to refine the knowledge of the transition phases. These dormant phases, or saturation, are key moments in the transition from a stable state to an unstable state, and reveal the “breathing” of the slope.</p><p>This communication will be made in the framework of a PhD funded by the socio-economic partner Azur Géo Logic, and the Provence-Alpes-Côte d'Azur region.</p>

Identification of Schedule-Performance Indicators and Delay-Recovery Strategies for Low-Cost Housing Projects

Many construction projects suffer from schedule delays that ultimately lead to considerable cost overruns and defeat the purpose of low-cost housing (LCH), which is to support low-income earners. It is, therefore, vital that the schedule delays and cost overruns be minimized. The objectives of this research were to investigate, identify, and classify the schedule-delay indicators (SDIs), prioritize them based on their level of impact, and formulate constructive strategies to improve the schedule performance. To achieve the objectives set forth, 68 interviews were conducted with professionals who are active in LCH projects, and a structured survey was developed and distributed to other experts involved in LCH projects to validate the result of the interviews and collect additional data. Survey responses were collected from 101 individuals and were analyzed. The significant SDIs were identified and classified by the research team and were ranked and prioritized, using the Relative Importance Index (RII) method. The results demonstrated that the identified SDIs could be classified into the following eight main categories: legal, design and technology, project characteristic, project management, material resource, human resource, location, and finance. The outcomes of this study will help project managers and stakeholders identify the causes of schedule delays early in the project and implement effective strategies for improving project performance in low-cost housing projects.