Miracle-dz/UniFAR-FTUs huggingface dataset:
Damaged Lane Markings Detection Method with Label Propagation

We propose a damaged traffic lane detection method ensuring high accuracy in spite of only a few number of supervised data which are labeled traffic lane images. In general, supervised machine learning approach is very powerful for image classification. However, preparing a large amount of supervised data is time-consuming task because labeling damaged or not damaged is usually done manually through visual inspection of images. Thus, lowering the cost of labeling data is a great concern. To this end, we adopt a semi-supervised machine learning approach which learns from both labeled and unlabeled data by constructing graph based on the image similarity. We captured a large amount of the road lane images. Then, we constructed graph structure whose nodes are the road lane images and whose edges are the similarity between the images. In several nodes, we assigned labels which denote "damaged" or "not damaged. " Finally we utilized the label propagation, which made it possible to infer the labels of the unlabeled data from the labeled data. These estimation resulted in the accuracy rate over 85% from the supervised data, which accounted for only 1.8% of the total data.
A comparative analysis of evolutionary algorithms for the prediction of software change

Change-proneness prediction of software components has become a significant research area wherein the quest for the best classifier still persists. Although numerous statistical and Machine Learning (ML) techniques have been presented and employed in the past literature for an efficient generation of change-proneness prediction models, evolutionary algorithms, on the other hand, remain vastly unexamined and unaddressed for this purpose. Bearing this in mind, this research work targets to probe the potency of six evolutionary algorithms for developing such change prediction models, specifically for source code files. We employ apposite object oriented metrics to construct four software datasets from four consecutive releases of a software project. Furthermore, the prediction capability of the selected evolutionary algorithms is evaluated, ranked and compared against two statistical classifiers using the Wilcoxon signed rank test and Friedman statistical test. On the basis of the results obtained from the experiments conducted in this article, it can be ascertained that the evolutionary algorithms possess a capability for predicting change-prone files with high accuracies, sometimes even higher than the selected statistical classifiers.
Evaluation of the Existence of Initial Breakdown Process for Cloud-to-Ground Flashes

In this paper, we are motivated to evaluate the existence of Initial Breakdown (IB) process in Cloud-to-Ground (CG) flashes of a tropical storm by using both broadband E-field and B-field antenna systems. It is important to note here that all previous studies were using only broadband E-field antenna system. In this study, the evaluation of existence of IB process was done based on both broadband E-field and B-field antenna systems. The data of lightning flashes were collected on June 3rd 2016 by our station in Malacca, Malaysia. We analyzed 50 CG flashes from a tropical storm happened in Kuala Lumpur area. The data recorded by broadband E-field antenna system shows that 94% of CG flashes were preceded by IB process. On the other hand, by using B-field antenna system, all CG flashes have been observed to be preceded by IB process. This finding is very significant because we can conclude that all CG flashes are initiated by IB process. The average pulse duration of the first, second, third, and the largest IB pulses are $33.52 {\mu } \mathrm {s} , 36.08 {\mu } \mathrm {s}$, 37.76 μs, 43.02 μs, respectively. The average peak amplitude of the first, second, third, and the largest IB pulses are 179.60 mV, 261.90 mV, 272.94 mV, 609.57 mV, respectively.
Contract-Based Incentive Mechanism for Cooperative NOMA Systems

In the emerging cooperative non-orthogonal multiple access (NOMA) systems, an important design issue is to incentivize mobile relays (MRs) to participate in the cooperative process and achieve a win–win situation to both the base station (BS) and MRs under asymmetric information, where the BS does not know the channel state information of MRs and NOMA users. As a promising remedy, we apply the adverse selection model from contract theory and derive the optimal feasible contract set for MRs with different wireless characteristics (types). Based on the contracts accepted by all the MRs, we further propose an efficient MR selection scheme for the original combinatorial optimization. The efficiency of the proposed incentive mechanism is verified through the simulation results.
Sensor Networks based Water Quality Monitoring Systems for Intensive Fish Culture -A Review

Intensive fish culture involves fostering fish in farms, tanks or some enclosures, usually for food. Water quality monitoring systems play an important role in modern intensive fish culture. Various water quality parameters such as temperature, pH, dissolved oxygen, electrical conductivity, etc need to be measured and controlled from time to time in order to ensure proper growth of fish. Real time monitoring of fish farm allows the farmers to be aware of the various water quality parameters and helps to ensure the health of fishes. This paper presents the review of various sensor networks based real-time water quality monitoring systems with an insight of advanced researches in the field of fish culture.
Automatic Detection of Airline Ticket Price and Demand: A review

Prediction of airline ticket prices and or demand is very challenging as it depends on various internal and external factors that can dynamically vary within short period of time. Researchers have proposed different types of ticket price/demand prediction models with the aim of either assisting the customer forecast ticket prices or aid the airline to predict the demand. In this paper, we present a review of customer side and airlines side prediction models. Our review analysis shows that models on both sides rely on limited set of features such as historical ticket price data, ticket purchase date and departure date. A combination of external factors such as social media data and search engine query in conjunction with advanced machine learning techniques are not considered.
AUV navigation with seabed acoustic sensing*

Autonomous Underwater Vehicle (AUV) being a powerful tool for exploring and investigating ocean resources can be used in a large variety of oceanographic, industry and defense applications. AUV navigation is still a challenging task and it is one of the fundamental elements in the modern robotics, because the ability of AUV to correctly understand its position and attitude within the underwater environment is determinant for success in different applications. Due to the absence of external reference sources, AUV navigation is usually based only on the information obtained from Doppler Velocity Loggers (DVL), Inertial Navigation Systems (INS), etc. But this type of navigation is subjected to a continuously growing error because of the absence of absolute position measurements (for example, received from the GPS or GLONASS). These measurements might be provided by observation of so-called feature points like in the case of the Unmanned Aerial Vehicles (UAV). But the big difference between acoustical and optical images makes this problem much more difficult in the AUV case, and to solve it one needs the detailed preliminary mapping of the operational seabed area. The modern advances in the acoustic imaging give rise to AUV navigation approaches based on the absolute velocity measurements. The one we propose in the present paper is analogous to the optical flow techniques for UAV navigation. It is based on the extraction of information related to the AUV absolute motion from seabed map evolution measurements. The principal advantage of the proposed method is that the fusion of the acoustic mapping and the INS data makes it possible to estimate the absolute velocity of the vehicle with respect to the seabed. In this sense the suggested method is close to the multi-beam DVL measurement, but it is based on another physical principles and thus operates better in different environment. While DVL by design operates perfectly over the flat surface [1], the appropriate environment for the suggested method implicates the seabed relief, because it extracts the velocity information from the evolution of the measured distance between the sensor and the seabed.
Modeling Battery under Discharge Using Improved Thevenin-Shepherd Electrical Battery Model

This paper performs the modeling of a Lithium-ion (Li-ion) commercial battery. The battery model and the method of obtaining its parameters come from the available literature. The improved Thévenin- Shepherd electrical battery model is used with a second-order resistor-capacitor (RC) network. The Time-domain Parameter Extraction Method(TPEM) is used to obtain its values RC. The discharge voltage profiles obtained in the simulations of the model are compared with the experimental results obtained during real battery discharges on the test bench. Error calculations are presented which are obtained by comparing the data generated by the model with the real voltage curves during discharge conditions.
Do We Really Need Cloud? Estimating the Fog Computing Capacities in the City of Barcelona

Fog computing has emerged as a novel and challenging technology intended to complement the features of the cloud computing paradigm by exploiting the opportunities of locality. Moving some computation to the edge of the network is especially attractive in a smart city scenario, where a vast network of heterogeneous resources is widely distributed all over the city. In such scenario, using the available fog resources allows the deployment of a rich family of new services that combine the advantages of both, cloud and fog technologies. In fact, the amount of computing load that can be moved down to the edge will actually depend on the edge devices capacity. But, in this context, some key questions arise; for example, how much computing capacity is there at the edge? Are there enough resources to execute efficiently some deep computing applications? And the most relevant, could the cloud layer be ignored in case of sufficient fog resources? In this paper, we analyze and estimate the fog computing capacity in a major European smart city: Barcelona. To this end, we analyze the available information about Barcelona and estimate the number of computing devices in the city that could be part of a fog environment, including personal computers, smartphones, on-board car computers, WiFi routers, and some other embedded computers deployed as part of city services. Then, we estimate an average computing capacity for each type of computing device, and obtain the potential computing peak performance for an eventual fog cluster of Barcelona. We believe the results of this work will be useful to provide insights into the aforementioned open questions, but also for fog technology researchers and developers to understand the order of magnitude of a real fog computing scenario.
Integrating an Attention Mechanism and Convolution Collaborative Filtering for Document Context-Aware Rating Prediction

Deep learning has become a recent, modern technique for big data processing, with promising results and large potential. For recommender systems, user and item information can be used as input vectors to perform prediction tasks. However, augmenting the number of layers to improve feature extraction will increase the computational complexity considerably and may not achieve the desired results. This paper proposes a method called attention convolution collaborative filtering (Att-ConvCF), which integrates an attention mechanism with a collaborative filtering model to improve the effectiveness of the feature extraction by reassigning the weights of feature vectors. Descriptive documents for the items are used to enrich the background information through a convolutional neural network. Finally, extensive experiments with real-world datasets were performed, and the results showed that Att-ConvCF could effectively extract the feature values of the data and significantly outperform the existing recommendation models.
Fabrication of Integrated Power Divider and Filter for X Band Radar Applications

This paper presents the prototype of power divider that integrated with filter for X-band radar applications. The prototype was designed and fabricated on a single board using substrate duroid RT5880 which has a relative permittivity of 2.2 and the thickness of 0.78 mm. In this research, Wilkinson power divider and coupled line filter are used base on their benefit. The simulation and measurement show that the prototype has a high performance to be applied in 9.3 GHz radar antenna system. Improvement of return loss achieves by integrating power divider and filter. Comparing with the conventional type, the prototype was proven to reduce loss power by 8%.
Security Path Verification Through Joint Information Flow Analysis

Security path verification is an effective measure for identifying design paths that can lead to security violations. However, existing techniques in this realm typically lack the flexibility in formal models for verification performance-precision tradeoffs and rely on new design languages and tools. In this paper, we propose a security path verification technique through joint information flow analysis. We formalize qualitative information flow models of different precision and complexity to enable verification performance tradeoffs. We further employ quantitative information flow metrics to assess the severity of identified security path violations. Our information flow models and security properties are described in standard HDL and property specification languages respectively, allowing verification to be performed using tools familiar to hardware designers. Experimental results show that our method is effective in identifying security vulnerabilities caused by design flaw, timing channel and hardware Trojan with verification performance benefits.
Proposals on IoT Communication through MQTT over L2 Network and their Performance Evaluation

The development of the Internet of Things (IoT) has been accelerated. The Internet is based on the internet protocol (IP), which is applied to most Internet communication. Communication through IP requires the physical location of the connected devices. In IoT, communication based on the IP cannot be optimized because data blocks in the IoT are transferred from various locations. Moreover, if IoT communication is invoked on the current Internet, it may be inefficient because large numbers of connections must be established and communication sessions released. Therefore, IoT requires a new concept of a communication system, which is referred to as an Information Centric Network (ICN). An ICN should be co-operated with IP-based communication. Therefore, we propose a new simplified communication scheme using Message Queuing telemetry transport (MQTT), which is a type of ICN, and evaluate its performance.
An 11b 80MS/s SAR ADC With Speed-Enhanced SAR Logic and High-Linearity CDAC

This paper presents an 11b 80MS/s successive approximation register (SAR) analog-to-digital converter (ADC) in 65nm CMOS with a speed-enhanced control logic. The accelerated SAR logic leads to more settling time of the capacitive digital-to-analog converter (CDAC) and reduces decoupling capacitance area. A high-linearity CDAC using custom-designed unit capacitor with small interconnection parasitic capacitance is also adopted in this design. With the two techniques, the ADC achieves small die area and good linearity. Measurement results show that the ADC achieves SNDR/SFDR of 60.50/80.93 dB and 55.81/76.10 dB at low and Nyquist input frequency respectively. The ADC core consume s 1.41 mW from 1.2V supply, translating into 20.36 fJ/conversion-step.
Vietnamese Keyword Extraction Using Hybrid Deep Learning Methods

Keywords provide a short way of reflecting a main idea of the document, making it easier for the readers in reading. Extracting keyword is the main task in natural language processing. Since it is not only time consuming but also requires lots of efforts to extract the keywords manually, it arises the need for the automated approaches. This paper has proposed a solution for the automatic keyword extraction in Vietnamese language using hybrid deep learning approaches. Every existing deep learning approach has its own advantages; and the hybrid deep learning model we are introducing is the combination of the superior features of CNN and LSTM models. The proposed model shows enhanced accuracy and f1-score over another approach.
Towards Cloud Native Continuous Delivery: An Industrial Experience Report

Several companies suffer from long delays between implementing and delivering software features. Continuous Integration, Delivery, and Deployment tools and practices are increasing their popularity since they can help companies decrease delivery costs and delays, and reduce bugs and errors in the delivery processes thanks to automation. Companies have difficulties in implementing Continuous Integration, Delivery, and Deployment. There is information available on the subject, but collecting, studying, and distributing that information can be very costly. The CI and CD pipelines are examples of well-founded Cloud Native applications that run in a variety of different configurations and provide flexibility and speed for companies, but require elastic execution platforms and knowledge of modern cloud technologies for optimal use. In this experience report, we present the lessons learned during the implementation of a cloud-based Continuous Delivery tool stack. Our experience includes the design and implementation? of a pure cloud and hybrid cloud-based Continuous Integration and Delivery solutions at Vincit.
Game Complexity Factor: A Collaborative Study of LeBlanc Taxonomy and Function Points Method

In 2015, the President of Indonesia established Bekraf—stands for Badan Ekonomi Kreatif (Creative Economy Association of Indonesia)—through Presidential Regulation (Perpres) number 6. The purpose of establishing Bekraf is to enable creative business actors to collaborate with government in developing this nation’s entertainment industries. Nowadays, the game is accepted as an alternative form of education. Therefore, the researchers aim to assist the business of digital creative field to estimate computer game development effort. This research is based on Function Points (FP) method, which is better known as cost calculation of software application development project. The result of this study shows the need to modify the definition of computer games’ parameters, including input, output, inquiry, internal file logic, and external file logic. Besides that, the complexity factors should be redefined and synchronized with 8 items of LeBlanc Taxonomy. Then, its collaboration is named Game Complexity Factors (GCF), consists of 22 items of complexity factors—8 items from LeBlanc Taxonomy and the rest from technical complexity.
Throughput Analysis of the System of Random Multiple Access with Separation by Signal Power

The article is devoted to a random multiple access algorithm using orthogonal preambles and separation of users by signal power. In the case of superimposing user signals with random access, the algorithm uses preamble information to temporarily switch to signal power splitting mode. This allows you to resolve the “conflict” between users without re-using random access in the next temporary resource. For the given algorithm the numerical analysis of throughput is resulted. We compared this algorithm with the optimal algorithm ALOHA. We show that this algorithm allows to increase the throughput with respect to the algorithm optimal ALOHA. With the use of 8 orthogonal preambles, the algorithm considered achieves a gain of 39% compared to the optimal algorithm of ALOHA. We showed the effect of the number of orthogonal preambles and the probability of sending a message to the channel to improve the efficiency of the algorithm.
QoS-enabled underwater acoustic communications

For enabling efficient operation of untethered underwater vehicles, error-free underwater communication of command-and-control and telemetry data is a necessity. In contrast, the communication requirements for the sensor data of these vehicles are often less stringent, i.e., a few bad pixels in a SONAR image might be tolerated. To maximize bandwidth utilization of underwater acoustic communication links, the MSFRSS (Multi-Stream Frequency-Repetition Spread Spectrum) modulation that supports transmission of data with distinct Quality-of-Service (QoS) demands within a single transmission is explained in detail. Real-time MSFRSS performance is assessed for remote-monitoring applications in two challenging environments (Port of Scheveningen and Trondheim Fjord). Recordings and channel soundings in the Port of Scheveningen were used to improve the equalization of the MSFRSS modulation scheme. Eventually, the updated transmitter and receiver were used in Trondheim Fjord to demonstrate real-time transmission of telemetry and image data from a ship to a bottom node.
Efficient Mining of Frequent Patterns on Uncertain Graphs

Uncertainty is intrinsic to a wide spectrum of real-life applications, which inevitably applies to graph data. Representative uncertain graphs are seen in bio-informatics, social networks, etc. This paper motivates the problem of frequent subgraph mining on single uncertain graphs, and investigates two different - probabilistic and expected - semantics in terms of support definitions. First, we present an enumeration-evaluation algorithm to solve the problem under probabilistic semantics. By showing the support computation under probabilistic semantics is #P-complete, we develop an approximation algorithm with accuracy guarantee for efficient problem-solving. To enhance the solution, we devise computation sharing techniques to achieve better mining performance. Afterwards, the algorithm is extended in a similar flavor to handle the problem under expected semantics, where checkpoint-based pruning and validation techniques are integrated. Experiment results on real-life datasets confirm the practical usability of the mining algorithms.
AP Selection Scheme Based on Achievable Throughputs in SDN-Enabled WLANs

To ensure an efficient coverage and allow transparent mobility, it is common to find a number of access points (APs) deployed in the neighborhood of each other in current WLANs. A mobile terminal (MT) often has multiple potential APs that it can connect to, and the selection of an appropriate AP becomes an important factor affecting both the network performance and user experience. This paper proposes a new AP selection scheme, which is based on the analysis of achievable normalized throughputs (ANTs) that the MT could obtain in the network. Specifically, we first compute the normalized throughput that characterizes the channel competition among multiple basic service sets (BSSs) as well as MTs within the same BSS. By evaluating ANTs in situations where the MT associates with different APs, the most appropriate AP for the MT can then be selected. Then, we present the implementation of the proposed AP selection scheme under the SDN-WiFi framework, in which the AP selection process can be performed in a software manner. The experimental results show that our computations on ANTs are of high accuracy and the proposed AP selection scheme can achieve a better performance than the existing schemes in terms of both the quality of service of MTs and the fairness among MTs.