Miracle-dz/UniFAR-FTUs huggingface dataset:
A Task-Driven Updated Discrete Graph Assisted Minimum Delivery Delay Routing for Remote Sensing Disruption-Tolerant Networks

Benefiting from the extensive coverage, remote sensing satellites have been playing important roles in surveillance for regions on the earth surface. In order to accommodate with intermittent connected and partitioned characteristics of satellite topology, disruption-tolerant network (DTN) develops a feasible solution for networking among such satellites. However, utilized widely and frequently in the future, numerous image covering hundreds of spectral bands and kilometers width is supposed to be delivered simultaneously. Due to the timeliness constrain, efficient routing design with minimum delivery delay for bulk and concurrent image data has become the bottleneck of remote sensing application. Considering data transmission initiated as different tasks, therefore, the Task-driven Updated Discrete Graph (TUDG) is designed to depict the topology evolution, with task data and edge capacity model for BP/LTP incorporated Remote Sensing DTN Network (RS-DTNet). In particular, the multitask-based delivery delay analytical framework is proposed based on the TUDG graph model, by solving a mixed integer Max-Min optimization problem. The Multi-Task Minimum Delay Routing (MTMDR) is designed with the delay-optimal flow distribution, dispatching appropriate bundle data to edges in the TUDG. This flow-based routing avoids the path selection procedure, which may degenerate the delivery delay performance. Through numerical simulation based on the representative RS-DTNet scene, the proposed MTMDR routing strategy shows to advantage on delivery delay, compared with typical path-based routing.
Resource Allocation in Cognitive Radio Inspired Non-Orthogonal Multiple Access

In this paper, we investigate the resource allocation in an underlay cognitive radio network when multiple primary and secondary users are served via multi-carrier non-orthogonal multiple access (MC-NOMA) scheme. Our main objective is to maximize the sum rate subject to a minimum guaranteed rate for the primary users. The performance of the system is highly affected by the power allocation and subchannel assignment for all users. Hence, our optimization problem is formulated as a nonconvex mixed integer non-linear program whose global optimum can be found through a computationally infeasible exhaustive search. To overcome this challenge, we propose an efficient iterative algorithm to jointly optimize subchannel assignment and power allocation, which employs the Hungarian method for subchannel assignment and derives the optimal solution in a closed form for power allocation. Our final solution after convergence indicates that in some subchannels orthogonal multiple access (OMA) scheme is applied instead of NOMA. In other words, the special feature of our proposed solution is that it benefits from both OMA and NOMA schemes to maximize the performance of the system. Simulation results reveal that our proposed solution is very close to the global optimum with considerably less computational complexity and also outperforms the results when the OMA scheme is solely utilized.
Walking with Perception: Efficient Random Walk Sampling via Common Neighbor Awareness

Random walk is widely applied to sample large-scale graphs due to its simplicity of implementation and solid theoretical foundations of bias analysis. However, its computational efficiency is heavily limited by the slow convergence rate (a.k.a. long burn-in period). To address this issue, we propose a common neighbor aware random walk framework called CNARW, which leverages weighted walking by differentiating the next-hop candidate nodes to speed up the convergence. Specifically, CNARW takes into consideration the common neighbors between previously visited nodes and next-hop candidate nodes in each walking step. Based on CNARW, we further develop two efficient "unbiased sampling" schemes. Experimental results on real-world network datasets show that our approach converges remarkably faster than the state-of-the-art random walk sampling algorithms. Furthermore, to achieve the same estimation accuracy, our approach reduces the query cost (a measure of sampling budget) significantly. Lastly, we also use two case studies to demonstrate the effectiveness of our sampling framework in solving large-scale graph analysis tasks.
Multi-Objective Four-Dimensional Glider Path Planning using NSGA-II

Gliders have a big role in data collecting for multiple areas of interest. However, as these vehicles move slowly, in an unknown environment, a good mission planning is crucial, both for vehicle safety and mission accomplishment, therefore ocean currents need to be taken into account in order to generate valid navigation commands. Planning a glider mission in an uncertain environment, with multiple parameters at the same time is a hard 4D (longitude, latitude, depth, time) Multi-Objective Optimization Problem. Multiple approaches are being used to do Glider Path Planning. In this work, we present a new system for helping multi-objective glider path planning in real missions, composed by a path simulator, coupled with the genetic algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm II), producing a set of multiple Pareto-optimal solutions for the specified objectives: goal distance and trajectory safety. Different experiments have been carried out to obtain significant assessment of the proposal. Results show that the system can quickly find multiple Pareto-optimal solutions for a given scenario with fixed obstacles. The proposed approach is suitable to be used during real missions as it does not need high computer specifications. All things considered, the system was able to optimize a multi-objective mission, presenting Pareto-optimal solutions that respond to the specified objectives, thus being a useful tool to help glider pilots decide a priori on the best path.
Results of Experimental Study of System Maintaining Battery Optimal Temperature

The problem of increasing the performance of batteries operating under severe temperature conditions is of current interest for the development of stand-alone energy systems. The system maintaining battery optimal temperature is proposed. The system combines battery thermal insulation, heating and monitoring and allows maintaining battery temperature with minimum energy consumption. The paper describes the experimental study of the proposed system. The experimental results analysis indicates internal resistance decrease of 47.9% and discharge energy increase of 55.7% and proves the technical efficiency of the system.
Intelligent Data Engineering for Migration to NoSQL Based Secure Environments

In an era of super computing, data is increasing exponentially requiring more proficiency from the available technologies of data storage, data processing, and analysis. Such continuous massive growth of structured and unstructured data is referred to as a “Big data”. The processing and storage of big data through a conventional technique is not possible. Due to improved proficiency of Big Data solution in handling data, such as NoSQL caused the developers in the previous decade to start preferring big data databases, such as Apache Cassandra, Oracle, and NoSQL. NoSQL is a modern database technology that is designed to provide scalability to support voluminous data, leading to the rise of NoSQL as the most viable database solution. These modern databases aim to overcome the limitations of relational databases such as unlimited scalability, high performance, data modeling, data distribution, and continuous availability. These days, the larger enterprises need to shift NoSQL databases due to their more flexible models. It is a great challenge for business organizations and enterprises to transform their existing databases to NoSQL databases considering heterogeneity and complexity in relational data. In addition, with the emergence of big data, data cleansing has become a great challenge. In this paper, we proposed an approach that has two modules: data transformation and data cleansing module. The first phase is the transformation of a relational database to Oracle NoSQL database through model transformation. The second phase provides data cleansing ability to improve data quality and prepare it for big data analytics. The experiments show the proposed approach successfully transforms the relational database to a big data database and improve data quality.
G*-Tree: An Efficient Spatial Index on Road Networks

In this paper, we propose an efficient hierarchical index, G*-tree, to optimize spatial queries on road networks. Most existing graph indexes can only support one kind of query, and thus we need to build multiple indexes on a road network to handle various kinds of spatial queries, which is inefficient and unscalable for real-world applications. To address the problem, a recent study proposes G-tree to support multiple types of spatial queries on road networks within one framework. However, the assembly-based method on G-tree is not efficient enough to handle spatial queries when vertices, which are close in a road network, are distant in G-tree. To address the inefficiency problem of G-tree, in this paper, we propose a novel index structure on road networks, namely G*-tree, whose key idea is to build shortcuts between selected leaf nodes. Based on G*-tree, we propose three shortcut-based algorithms to answer distance queries, k-nearest neighbor queries and range queries, respectively, which are more efficient than the existing assembly-based algorithms on G-tree. Moreover, we propose a shortcut selection algorithm to optimize the performance of spatial queries on G*-tree. We conduct extensive experiments to compare our G*-tree and the state-of-the-art indexing methods on various large-scale road networks, where the results demonstrate that our G*-tree has better efficiency and scalability than the competitors to handle spatial queries.
Proximity Fed Broadband Equilateral Triangular Microstrip Antenna Using Parasitic Rectangular Patches

Gap coupled design of proximity fed equilateral triangular microstrip antenna along with variations of rectangular microstrip antennas are proposed. Design of equilateral triangular microstrip antenna provides a bandwidth of 302 MHz (25%) with broadside gain of 7.6 dBi. Enhancement in bandwidth and gain is realized by gap coupling rectangular patches. Optimum result with VSWR bandwidth of more than 670 MHz (57%), showing peak gain of 9.7 dBi is obtained in gap coupled configuration with two adjacent layers of two and four rectangular patches. Proposed design is simpler in implementation and measured results show close agreement.
Rumor proliferation and detection in Social Media: A Review

With the intrusion of social media into personal, social and political space of today’s world, the iniquities associated with it have also found a place to thrive. One such malevolent act is the spread of rumors through social networks. This survey analyses the various domains where social media finds its relevance and has a considerable impact. It also explores various studies done on the nature of rumors, the characteristics of rumors and the studies identifying the important features for the social media-based rumors. Further, the latest developments in the field of rumor detection through the approaches of Machine learning and Deep learning have also been discussed.
Power Quality Disturbance Feature Selection and Pattern Recognition Based on Image Enhancement Techniques

In the existing research of power quality disturbance (PQD) identification, the efficiency of signal processing is low and cannot meet the needs of practical application analysis. Furthermore, due to the lack of effective analysis of features, the complexity of classifiers is increased, and the efficiency of classification reduced by the redundant features. In this paper, in order to overcome these shortcomings, a PQD recognition method based on image enhancement techniques and feature importance analysis is proposed. First, PQD signals are converted into gray images, and three image enhancement techniques include gamma correction, edge detection, and peaks and valley detection are used to enhance the disturbance features. Then, the disturbance features are extracted from the binary images, and the original feature set is constructed, the classification ability of each feature is measured by Gini importance. Based on the descending order of the Gini importance, the sequence forward search (SFS) method is used for feature selection to determine the optimal feature subset. Finally, random forest (RF) classifier is constructed by the optimal feature subset to identify the PQD signals. The results of the simulation and contrast experiments show that the new method can determine the optimal classification subset, which recognizes the PQD signals effectively in different noise environments. Furthermore, the new method has higher signal processing efficiency compared with the EMD and ST methods.
Coloring Embedder: A Memory Efficient Data Structure for Answering Multi-set Query

Multi-set query is a fundamental issue in data science. When the sizes of multi-sets are large, exact matching methods like hash tables need too much memory, and they cannot achieve high query speed. Bloom filters are recently used to handle big data query, but they cannot achieve high accuracy when the memory space is tight. In this paper, we propose a new data structure named coloring embedder, which is fast, accurate as well as memory efficient. The insight is to first map elements to a high dimensional space to almost eliminate hashing collisions, and then use a dimensional reduction representation, which is similar to coloring a graph, to save memory. Theoretical proofs and experimental results show that compared to the state-of-the[1]art, the error rate of the coloring embedder is thousands of times smaller even with much less memory usage, and the query speed of the coloring embedder is about 2 times faster. The source code of coloring embedder is released on Github.
Survey for Secure IoT Group Communication

The Internet of Things (IoT) paradigm is progressing fast toward Internet adoption and reaching out to various industrial domains such as smart lighting. New IoT use cases leveraging multicast group communication increase the demand for security to protect a number of devices. Providing dedicated multicast security for constrained IoT environment is the key to make IoT service successful. In this paper, we survey the state of the art of secure IoT group communication among devices. We focus on the context of commercial smart lighting, as it has as several use cases that rely on secure group communication. The paper summarizes use cases and security requirements for the multicast group communication, state of the art approaches focusing on standardization activities. Furthermore, the paper shows the evaluation result of the new application layer security protocol, OSCORE. We concludes with a research outlook on open problems.
OSMAC: Optimizing Subgraph Matching Algorithms with Community Structure

Subgraph has gained increasing attention as it is an important query type on graphs. The efficiency of existing subgraph matching algorithms becomes unsatisfactory since graphs gradually get larger and more complex. This paper proposes an optimization method named OSMAC to accelerate subgraph matching algorithms with the community structure of data graphs. In essence, OSMAC changes the task of subgraph matching into dealing with all VC-mappings. An optimization method named community-structure-based boundary pruning is proposed to further improve the performance of OSMAC. It implements an efficient pruning method with the information of community structure and can reduce the search space. As a case study, we optimize TurboISO, one of the state-of-the-art subgraph matching algorithms, with OSMAC. The results of the experiments conducted on real-world data sets confirm that OSMAC is efficient and can improve the performance of subgraph matching algorithms significantly.
Research on the influence of system slot setting on the performance of three-node cooperative communication system

As a new communication mode, cooperative communication can amplify or decode the signal between source node and destination node by setting relay node between source node and destination node of communication system, so as to combat multipath fading and noise in complex channel, expand the coverage of communication system and improve the reliability of communication system. This paper uses LabVIEW and Universal Software Radio Peripheral Device (USRP) as the experimental platform to simulate the three-node cooperative communication based on amplification and forwarding, and analyse the performance of the system. By changing the system slot settings through software programming, simulation tests are carried out. It is found that the system slot settings have a certain effect on data transmission. If the slot length is too long, noise will be introduced and the transmission performance of the system will be reduced.
Error-Free Transmission for Rolling-Shutter-Based Optical Camera Communication

To achieve symbol synchronization for rolling-shutter-based optical camera communication, influence of exposure time of image sensor, spectrum of white LED, discrimination threshold in color space, and communication distance is experimentally investigated. Error-free transmission is achieved at more than 20 kilo symbols per second using 78 flame-per-second image sensor.
Forecasting stock prices in two ways based on LSTM neural network

Due to the extensive application of deep learning in processing time series and recent progress, LSTM (Long Short-Term Memory) neural network is the most commonly used and most powerful tool for time series models. The LSTM neural network is used to predict Apple stocks by using single feature input variables and multi-feature input variables to verify the prediction effect of the model on stock time series. The experimental results show that the model has a high accuracy of 0.033 for the multivariate input and is accurate, which is in line with the actual demand. For the univariate feature input, the predicted squared absolute error is 0.155, which is inferior to the multi-feature variable input.
Automated Function Assessment in Driving Scenarios

In recent years, numerous innovations in the automotive industry have addressed the field of driver assistance systems and automated driving. Therefore additional required sensors, as well as the need for digital maps and online services, lead to an ever-increasing system space, which must be covered. Established test approaches in the area of Hardware-in-the-Loop (HiL) use predefined and structured test cases to test the systems on the basis of requirements. In the approach of systematic testing, an evaluation is only carried out for a specific test case respectively the duration of a test step. This paper presents a concept for an automated quality assessment of driving scenarios or digital test drives. The aim is the analysis and subsequent evaluation of continuous function behavior during a realistic test drive within a simulated environment. Compared to conventional systematic test approaches, the presented concept allows a continuous evaluation of the test drive, whereby multiple evaluations of systems in similar scenarios with deviating boundary conditions is possible. For the first time, this enables a functional evaluation of a complete test drive comprising numerous scenarios and situations. The presented approach was prototypically implemented and demonstrated on a Hardware-in-the-Loop (HiL) test bench evaluating an adaptive cruise control (ACC) system.
The Scaling of Cu-Cu Hybrid Bonding For High Density 3D Chip Stacking

We have successfully improved the scaling of Cu-Cu hybrid bonding. In this study, 3 $\mu {\mathrm{ m}}$-pitch and 3M Cu-Cu connections with sufficient electrical properties and reliabilities were achieved. The ultra-fine pitch Cu-Cu connections correspond to the 0.75x scaling of conventional Cu-Cu hybrid bonding that we previously reported. Our high density 3D chip stacking technology is expected to enhance not only the function of back-illuminated CMOS image sensors (BI-CIS) but also that of coming 3D stacked semiconductor devices.
Data Management at Huawei: Recent Accomplishments and Future Challenges

Huawei is a leading global provider of information and communication technologies (ICT) infrastructure and smart devices. With integrated solutions across four key domains: telecommunication networks, IT, smart devices, and cloud services, Huawei is committed to bringing digital transformation to every person, home and organization for a fully connected and intelligent world. Founded in 1987, Huawei currently has more than 180,000 employees, and operates in more than 170 countries and regions with revenue over 100 billion USD in 2018. Data management plays a key role in all of the four key domains above. We have developed innovative products and solutions to support rapid business growth driven by customer requirements. While many data management problems are common, each domain also has its own special requirements and challenges. In this paper, we will go through recent advancements in Huawei data management technologies including a petabyte scale enterprise analytics platform (FusionInsight MPPDB) and a highly available in-memory database for telecommunication networks (GMDB). In addition, we discuss data management challenges that we are facing in the areas of autonomous databases and device-edge-cloud collaboration data platforms.
Detection, Localization and Classification of Fish and Fish Species in Poor Conditions using Convolutional Neural Networks

In this work the initial steps towards a system capable of parametrising fish schools in underwater images are presented. For this purpose a deep convolutional neural network called Optical Fish Detection Network (OFDNet) is introduced. This is based on state-of-the-art deep learning object detection architectures and carries out the task of fish detection, localization and species classification using visual data obtained by underwater cameras. This work is focused towards applications in the poorly conditioned North and Baltic Sea and is initially developed for the purpose of recognizing herring and mackerel. Based on experiments on a dataset obtained at sea, OFDNet is shown to successfully detect 66.7% of the fish included and furthermore classify 89.7% of these correctly.
Multiband Three-Layer GNSS Microstrip Antenna

There are various receiving antennas of satellite radio navigation systems. As a rule, the qualitative polarization characteristics (axial ratio) are achieved by efficiency degradation or frequency ranges decreasing. In this article, a circular polarization GNSS antenna, which have no disadvantages described above are presented. By using a new planar feeding network and a three layer design of patch elements, it is possible to provide a symmetrical radiation pattern with high axial ratio and efficiency in various GNSS frequency bands L1/L2/L5/G1/G2/G3/E1/E2/E5ab//B1/B2 et al. Moreover, in the proposed antenna the capacitive caps were used to feed microstrip patch. It allowed getting good reflection coefficient in the extended frequency range $(\sim 10$% for one patch element) and receiving of all GNSS signals by just two resonant elements. In the article, the breadboarding was carried out and the experimental results of electrodynamic characteristics were obtained. Perspective application of the proposed antenna design is demonstrated using these characteristics. It should be noted that the proposed antenna has low weight and low manufacturing cost due to the use of microstrip technology.