Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of Visual-Based Localization

The visual-based localization (VBL) obtains the corresponding pose estimation in the localization system by utilizing various useful information in the surrounding environment, such as images, point cloud models, geometric information, semantic information. In recent years, visual-based localization (VBL) has been widely concerned by scientists, mainly because the commonly used GPS localization system cannot be effectively used in various environments. When GPS localization fails in some scenes such as very messy environments and severe signal occlusion, we can consider using visual-based localization to obtain the pose of the query images. Visual-based localization (VBL) has been widely used in the field of visual tasks, such as augmented reality, unmanned vehicle navigation, robotics, closed-loop detection, SFM (Structure from Motion) models. After years of development, the methods of visual-based localization (VBL) have been enriched and developed, In order to better understand the latest developments in VBL, overall research status and possible future development trends, we need make a systematic detailed classification of VBL. Although the predecessors have summarized the methods of VBL, due to the many new breakthroughs in VBL in recent years, the original summary is not perfect enough. So this paper will make a new and more detailed review of VBL in recent years. This paper divides the visual-based localization methods into three categories: image-based localization, localization based on learning model and localization based on 3D structure. And we also detail the principle, development of methods and the advantages and disadvantages of each method and future development trends.
Energy-aware device drivers for embedded operating systems

Energy harvesting solutions with rechargeable batteries are a frequent choice to tackle the problems of supplying continuous power to deeply embedded devices like wireless sensor nodes. However, if the utilization of a node is not thoroughly planned, the battery may be drained too early and a continuous operation of such a device may become impossible. Here, an energy-management solution is required to control the flow of energy. As a foundation for energy management in software, we introduce a concept that allows to model energy consumption of hardware and to synthesize energy aware device drivers from these models. Our drivers are able to account the energy consumption of each driver function call at an accuracy of more than 90%. We provide a detailed overhead and accuracy evaluation of a driver implementation and hence prove the feasibility of our concept.
A Transparent and Multimodal Malware Detection Method for Android Apps

While recent works have shown that deep learning method can improve the malware classification accuracy, the lack of the transparency has restricted its application in anti-virus scan engines. Existing researches have attempted to provide solutions to give high-fidelity explanations of the model's decision. However, current methods are not optimized for application security task, leading to a poor performance in Android malware detection. In this paper, we propose a backtracking method to infer suspicious features of the apps to explain the reason of classification. Besides, we also propose a malware detection model based on the fusion convolutional neural network using different types of features (e.g., permission, API, URL, etc.). For maximizing the benefits of encompassing multiple feature types, our framework trains the sub-models for each type of features separately and merges them at the end of the system to obtain a comprehensive classification result. The experimental results show that the backtracking method has a significant improvement in fidelity level compared with existing methods. Furthermore, we evaluate the performance of the proposed framework with other existing works. Leveraging the backtracking method, our framework has better performance in classification and significantly reduces detection time by 69% compared with prior approaches.
A New Method for Knowledge Discovery of Complex Data Based on Structural Partial-Ordered Theory

In order to develop a new knowledge discovery method with higher generalization ability, this paper proposes the generalized model of partial-ordered structure diagrams. This model is based on two philosophical methodologies: the concept driven methodology and the data driven methodology. In essence, the concept driven methodology is top-down principle, that is, the attributes representing object universality are put at the top of the structural partial-ordered diagram. While the data driven methodology is bottom-up principle in which the attributes representing object specificity are put at the top of the diagram. The method is described by the mathematical partial order theory and formal concept analysis theory. Finally, three concrete data sets are used as examples to generate diagrams. The generated diagrams can clearly reveal the knowledge implied in the complex data. It is proven that the proposed generation theory and the model constructed with partial-ordered diagrams have a good ability of generalization, and they are original methods which can be used in different domains for knowledge discovery.
Plant shadow morphing as a peripheral display

The amount of status and notification information available to people is increasing beyond the capacity of the current primary delivery channel of smartphones. Ambient displays, e.g. embedded in the home or work environment present an alternative channel, suited to less critical information. We created an ambient display by manipulating the natural shadows cast by a house plant to carry information. To explore user perceptions of the shadow display we evaluated it in a focus group (n = 6). Whilst participants saw some potential in the approach, there were concerns about identifying the meaning of the display and the psychological effects of distorting reality. The work highlights the potential for designers to explore the use of functional shadows to carry non-critical information.
A Byzantine-Tolerant Distributed Consensus Algorithm for Connected Vehicles Using Proof-of-Eligibility

Emerging applications in connected vehicles have tremendous potential for advances in safety, navigation, traffic management and fuel efficiency, while also posing new security challenges such as false information attacks. This paper targets the problem of securing critical information that is disseminated among nearby vehicles for safety and traffic efficiency purposes through distributed consensus. We present a consensus algorithm, which uses a "proof of eligibility" test to establish that a group of vehicles are actually within the vicinity of the information source. With the presence of a limited number of compromised (Byzantine faulty) participants, our algorithm provides correct consensus among healthy vehicles in real time. The algorithm provides fast and reliable consensus group formation and private key distribution without privileged members, trusted setup, or leader election. In addition to proving a safety property of our consensus algorithm, we have implemented it on top of a widely-used vehicle simulation environment (SUMO, OMNeT++ and Veins) and evaluated its performance on a model of the streets in a real midtown area. Simulation results demonstrate that the algorithm can reach consensus very efficiently (within 9.5s) and with up to 30% of compromised vehicles in a given area. The simulations also demonstrate the ability of our algorithm to more quickly disseminate information about a traffic accident and more efficiently route traffic around the accident site, as compared to previous robust information dissemination approaches.
Evaluation of LoRaWAN Transmission Range for Wireless Sensor Networks in Riparian Forests

Low power wide area networks (LPWAN) such as long range wide area networks (LoRaWAN), provide several advantages on monitoring systems development in forested environments due to its simple set-up, low cost, low power consumption, and wide coverage. Regarding the coverage area, the transmission in forested environments can be highly attenuated by foliage and must be defined to optimize the number of nodes. This paper discusses an empirical study of LoRa with LoRaWAN transmission range in riparian forests, based on path-loss modeling, using both received signal strength indicator (RSSI) and signal-to-noise-ratio (SNR). The measurements have been conducted in the riparian forest of three local rivers at urban, semi-urban, and rural environments located in the city of Cuenca, Ecuador. The measurement results found that there is a significant distribution difference among measurement places, a high correlation between two banks of the same river, a higher standard deviation in urban measurements and a larger coverage in rural areas.
Combining it all: Cost minimal and low-latency stream processing across distributed heterogeneous infrastructures

Control mechanisms of stream processing applications (SPAs) that ensure latency bounds at minimal runtime cost mostly target a specific infrastructure, e.g., homogeneous nodes. With the growing popularity of the Internet of Things, fog, and edge computing, SPAs are more often distributed on heterogeneous infrastructures, triggering the need for a holistic SPA-control that still considers heterogeneity. We therefore combine individual control mechanisms via the latency-distribution problem that seeks to distribute latency budgets to individually managed components of distributed SPAs for a lightweight yet effective end-to-end control. To this end, we introduce a hierarchical control architecture, give a formal definition of the latency-distribution problem, and provide both an ILP formulation to find an optimal solution as well as a heuristic approach, thereby enabling the combination of individual control mechanisms into one SPA while ensuring global cost minimality. Our evaluations show that both solutions are effective---while the heuristic approach is only slightly more costly than the optimal ILP solution, it significantly reduces runtime and communication overhead.
Understanding indoor orientation through wearable vibrotactile feedback

The main goal of this work is to better understand how vibrotactile feedback obtained through wearable actuators can support indoor orientation in unfamiliar buildings. We designed different wearable vibrotactile prototypes (two wristbands, a flexible/rigid glove, one wristband, a cap and two-bands), and we analysed them in a preliminary test with 7 users to identify the design aspects that are most relevant for a solution guiding a person indoor. We describe the design and its evaluation with 36 users, discussing the results that can be useful for developers who want to use this technology within applications that need support for indoor orientation.
On the FaaS Track: Building Stateful Distributed Applications with Serverless Architectures

Serverless computing is an emerging paradigm that greatly simplifies the usage of cloud resources and suits well to many tasks. Most notably, Function-as-a-Service (FaaS) enables programmers to develop cloud applications as individual functions that can run and scale independently. Yet, due to the disaggregation of storage and compute resources in FaaS, applications that require fine-grained support for mutable state and synchronization, such as machine learning and scientific computing, are hard to build. In this work, we present Crucial, a system to program highly-concurrent stateful applications with serverless architectures. Its programming model keeps the simplicity of FaaS and allows to port effortlessly multi-threaded algorithms to this new environment. Crucial is built upon the key insight that FaaS resembles to concurrent programming at the scale of a data center. As a consequence, a distributed shared memory layer is the right answer to the need for fine-grained state management and coordination in serverless. We validate our system with the help of micro-benchmarks and various applications. In particular, we implement two common machine learning algorithms: k-means clustering and logistic regression. For both cases, Crucial obtains superior or comparable performance to an equivalent Spark cluster.
Stationarity for the Small World in Motion Mobility Model

Human mobility has been shown to have significant impact on the performance of Opportunistic Networks. For reliable performance evaluation, it is important to make sure the models used are in steady state. The mobility model Small World In Motion (SWIM) incorporates statistical properties of human movements in its results and is, at the same time, easy to configure. However, SWIM is not in steady state. In this paper, we show how a steady state for SWIM can be achieved.
Comparison of Traffic Demand Generation Tools in SUMO: Case Study: Access Highways to Quito

SUMO is one of the tools used to generate synthetic traces of vehicles for VANET simulations. Sumo allows creating vehicles' routes manually, generate random traffic or use traffic demand generation tools. In this paper, a qualitative review of the five traffic demand generation tools of SUMO: DUArouter, JTRrouter, DFrouter, OD2trips and MArouter, is presented. We include the advantages and limitations of each of these tools to simulate vehicles' mobility in real scenarios. In addition, a comparative analysis of these tools is carried out over three access highways of Quito. Our results show that most of the tools provide similar trip times among them but lower than the real ones. However, the distribution of trip times differs among them.
Measuring Power Relations Among Locations From Mobility Data

Key location identification in cities is central in human mobility investigation as well as for societal problem comprehension. In this context, we propose a methodology to quantify the power of point-of-interests (POIs) in their vicinity, in terms of impact and independence - the first work in the literature (to the best of our knowledge). Different from literature, we consider the flow of people in our analysis, instead of the number of neighbor POIs or their structural locations in the city. Thus, we first modeled POI's visits using the multiflow graph model where each POI is a node and the transitions of users among POIs are a weighted direct edge. Using this multiflow graph model, we compute the attract, support and independence powers. The attract power and support power measure how many visits a POI gather from and disseminate over its neighborhood, respectively. Moreover, the independence power captures the capacity of POI to receive visitors independently from other POIs. Using a dataset describing the mobility of individuals in the Dartmouth College campus, we identify a slight dependence among buildings as well as the tendency of people to be mostly stationary in few buildings with short transit periods among them.
Modeling and simulation of power consumption and execution times for real-time tasks on embedded heterogeneous architectures

In this work, we introduce a power-consumption model for heterogeneous multicore architectures that captures the variability of energy consumption based on processing workload type, in addition to the classical variables considered in the literature, like type and frequency of the CPU. We motivate the approach presenting experimental results gathered on a Odroid-XU3 board equipped with an Arm big.LITTLE CPU, showing that power consumption has a non-negligible dependency on the workload type. We also present a model to define the execution time of the tasks, which depends on both the workload, and the CPU frequency and architecture. We present our modifications to the open-source RTSIM real-time scheduling simulator to extend its CPU power consumption and execution time duration models, integrating results taken from the real platform. The presented work constitutes a useful base for future research in power-aware real-time scheduling on heterogeneous platforms.
UAV Mobility Model for Dynamic UAV-to-Car Communications

Unmanned Aerial Vehicles (UAVs) can be used to support car-to-car communications when the infrastructures are not available or are unreliable. However, since cars in vehicular communications are mobile, the UAV that acts as a relay should move dynamically as a response to moving ground vehicles. In this paper, we propose a mobility model to manage how UAVs should move to maintain good coverage levels in terms of communication with moving ground vehicles. The results showed that our proposed model can extend the times with connectivity between the UAV and the cars compared to the static model, which maintains its position from the beginning to the end.
Footsteps and inertial data-based road surface condition recognition method

We propose a method to recognize road surface conditions using footsteps and inertial data. In areas where the road surface conditions change significantly with the seasons and weather, bad road conditions cause dangerous such as falls. If the road surface condition can be determined in advance, danger can be averted by selecting safe routes and suitable shoes. In this study, we focus on the footsteps and inertial data that change depending on road surface conditions, such as dry pavement, puddle, soil, and mud. We implemented the prototype device and evaluated the proposed method on six road surface conditions with eight participants. The evaluation results confirmed that the recognition accuracy was 83.0% in a low-noise environment. When there was noise, we compared the standard approach, which combines footsteps and inertial data, and the revised method, which changes the confidence of the result of footstep recognition by the signal-noise ratio (SNR). The evaluation results also confirmed that the recognition rate increased by a maximum of 16.4% using the revised method (when the SNR was 1 dB, the average accuracy was improved from 37.5% to 53.9%).
A SIMD-aware pruning technique for convolutional neural networks with multi-sparsity levels: work-in-progress

Designs of electronic systems often require considering multiple design concerns. In this paper, we propose a novel multi-phase pruning technique for convolutional neural networks (CNNs) that is capable of efficient exploration of multiple design objectives and constraints. To truly take advantage of the sparsity obtained by pruning, we present two different levels of pruning granularity, fine- and coarse-grain, and show how they are combined in the design space exploration. In particular, we propose to take the SIMD architecture into account in the fine-grain pruning. By iteratively pruning to a single CNN, multiple candidates can be obtained from the trade-off between the given design concerns. Experiments with existing CNNs verify that the proposed technique enables more efficient design space exploration over the accuracy-speed trade-off.
IDS-DL: A description language for detection system in cloud computing

When designing cybersecurity measures in the cloud, many decisions need to be made and conditions must come together with such as the purpose of security, the set of deployment and the actors used. Several cloud detection systems are similar in their operation but their proposals are different in their objectives and their integrated concepts. This leads to the fact that the effective implementation of an intrusion detection system is a difficult goal to achieve. In this work, we contribute to an unified framework by a conceptual meta-model of cloud-based detection systems, called meta-IDS, while helping the community to identify work on this topic and making it easier for researchers to find, identify and use existing solutions. This meta-IDS can later be used to extend the library reuse process to help the detection process to accurately describe the problems encountered for different IDS (intrusion detection system).
SRFuzzer: an automatic fuzzing framework for physical SOHO router devices to discover multi-type vulnerabilities

SOHO (small office/home office) routers provide services for end devices to connect to the Internet, playing an important role in the cyberspace. Unfortunately, security vulnerabilities pervasively exist in these routers, especially in the web server modules, greatly endangering end users. To discover these vulnerabilities, fuzzing web server modules of SOHO routers is the most popular solution. However, its effectiveness is limited, due to the lack of input specification, lack of routers' internal running states, and lack of testing environment recovery mechanisms. Moreover, fuzzing in general only reports memory corruption vulnerabilities, and fails to discover other vulnerabilities, e.g., web-based vulnerabilities. In this paper, we propose a solution SRFuzzer to address these issues. It is a fully automated fuzzing framework for testing physical SOHO devices. It continuously and effectively generates test cases by leveraging two input semantic models, i.e., KEY-VALUE data model and CONF-READ communication model, and automatically recovers testing environment with power management. It also coordinates diversified mutation rules with multiple monitoring mechanisms to trigger multi-type vulnerabilities. To the best of our knowledge, it is the first whole-process fully automated fuzzing framework for SOHO routers. We ran SRFuzzer on 10 popular routers across five vendors. In total, it discovered 208 unique exceptional behaviors, 97 of which have been confirmed as 0-day vulnerabilities. The experimental results show that SRFuzzer outperforms state-of-the-art solutions in terms of types and number of vulnerabilities found.
Premadoma: an operational solution for DNS registries to prevent malicious domain registrations

DNS is one of the most essential components of the Internet, mapping domain names to the IP addresses behind almost every online service. Domain names are therefore also a fundamental tool for attackers to quickly locate and relocate their malicious activities on the Internet. In this paper, we design and evaluate Premadoma, a solution for DNS registries to predict malicious intent well before a domain name becomes operational. In contrast to blacklists, which only offer protection after some harm has already been done, this system can prevent domain names from being used before they can pose any threats. We advance the state of the art by leveraging recent insights into the ecosystem of malicious domain registrations, focusing explicitly on facilitators employed for bulk registration and similarity patterns in registrant information. We thoroughly evaluate the proposed prediction model's performance and adaptability on an 11 month testing set, and address complex and domain-specific dataset challenges. Moreover, we have successfully deployed Premadoma in the production environment of the . eu ccTLD registry to detect and prevent malicious registrations, and have contributed to the take down of 58,966 registrations in 2018.
Modeling of low-rate DDoS-attacks

The paper presents the results of the analysis and simulation study of Denial of Service attacks for a variety of services that store, process and transmit data to the Internet. Focuses on the simulation of low-rate Distributed Denial of Service attacks on the infrastructure of the global dynamic routing Border Gateway Protocol (BGP) and HyperText Transfer Protocol (HTTP). We choose the following software for the set-up: Apache2, Nginx, Quagga, Debian, VMware and Citrix XenServer with the most popular configuration options. The study analyzed the vulnerability of data communication protocols of the transport and application layer, leading to the possibility of the threat of low-rate DDoS-attacks. This study demonstrates the implementation of such an attack on real computer systems and computer networks.