Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT and WSN Based Water Quality Monitoring System

SmartCity projects growing with the concept of IoT (Internet of Things) and WSN (Wireless Sensor Network) helps to manage assets and resources efficiently. Nowadays water is getting polluted due to industrialization, use of fertilizers and pesticides, urban development, etc. So we should have a reliable water quality monitoring system. The proposed system based on IoT and WSN gives real-time data of water quality so that the authority can manage and conserve the valuable water resources more effectively. With IoT & WSN, smart monitoring and management is attainable. It helps create more efficient city services and to keep citizens informed. The existing systems of water quality monitoring uses wireless technologies which are short range and much power consuming, which are not suited for sensors nodes in WSN. The proposed system presents a low cost, low power, long range and scalable approach for water quality monitoring using LoRa module based on LoRaWAN protocol which is Low-Power Wide Area Network (LPWAN) technology, which is much more efficient than many existing systems. The design of the proposed system includes integrating sensors to the microcontroller, wireless LoRa module for transmitting and receiving sensor values, and ThingSpeak IoT platform for examining and visualizing water quality sensor values which have been uploaded.
Incrementalization of Vertex-Centric Programs

As the graphs in our world become ever larger, the need for programmable, easy to use, and highly scalable graph processing has become ever greater. One such popular graph processing model—the vertex-centric computational model—does precisely this by distributing computations across the vertices of the graph being computed over. Due to this distribution of the program to the vertices of the graph, the programmer "thinks like a vertex" when writing their graph computation, with limited to no sense of shared memory and where almost all communication between each on-vertex computation must be sent over the network. Because of this inherent communication overhead in the computational model, reducing the number of messages sent while performing a given computation is a central aspect of any efforts to optimize vertex-centric programs. While previous work has focused on reducing communication overhead by directly changing communication patterns—by altering the way the graph is partitioned and distributed, or by altering the graph topology itself—in this paper we present a different optimization strategy based on a family of complementary compile-time program transformations in order to minimize communication overhead by changing both the messaging and computational structures of programs. Particularly, we present and formalize a method by which a compiler can automatically incrementalize a vertex-centric program through a series of compile-time program transformations—by modifying the on-vertex computation and messaging between vertices so that messages between vertices represent patches to be applied to the other vertex's local state. We empirically evaluate these transformations on a set of common vertex-centric algorithms and graphs and achieve an average reduction of 2.7X in total computational time, and 2.9X in the number of messages sent across all programs in the benchmark suite. Furthermore, since these are compile-time program transformations alone, other prior optimization strategies for vertex-centric programs can work with the resulting vertex-centric program just as they would a non-incrementalized program.
Helping Teachers Design Gamified Intelligent Tutoring Systems

Teachers are increasingly demanding to be active users of gamified intelligent tutoring systems (ITS). To effectively help teachers to use such systems, it is needed to provide simple and usable solutions for them without requiring advanced technical skills. In this work, we propose an authoring solution to help teachers in the design of gamified ITS. We evaluate our solution using a controlled experiment varying some features of the authoring proposal (template or scratch) with respect to ten metrics in the context of teachers and lecturers in Brazil. The results of this experiment allowed us to state that (i) authoring gamified ITS by using template requires much less time than from scratch; and (ii) there is no difference in the use or not of template for authoring gamified ITS with regards to the other metrics (e.g, perceived ease of use and usability, complexity, etc), but teachers have a high acceptance for all these metrics. The results found are of great importance since they might help teachers to design gamified ITS in a few minutes.
Smart Vision: Assistive Device for the Visually Impaired Community U sing Online Computer Vision Service

The visually impaired community face several difficulties in their daily life. With the increase of commercial assistive devices, it can greatly improve their lives, especially for navigation and orientation, which are their main obstacles. Recently, it has been introduced many powerful online image processing services, based on machine learning and deep learning. Microsoft is one of the main players in online image processing services. In this paper, we discussed a prototype development by utilizing the online image processing service, Microsoft Cognitive Service. We expect to further evaluate the accuracy and the usage analytics in order to understand long term system behavior.
Simulation Study on Latency-aware Network in Edge Computing

Attaining extremely low latency service in 5G cellular networks is an important challenge in the communication research field. A higher Quality of Service (QoS) in the next-generation network could enable several unprecedented services, such as Tactile Internet, Augmented Reality, and Virtual Reality. However, these services will all need support from powerful computational resources provided through cloud computing. Unfortunately, the geolocation of cloud data centers could be insufficient to satisfy the latency aimed for in 5G networks. Driven by these application requirements, there is a big paradigm shift in mobile computing, from cloud computing to edge computing by offloading the computing, application task, and storage to an edge server. Edge computing promises a dramatic reduction in latency by locating servers closer to the devices that will connect to them. This paper discusses the effects of number of end-users and task size on latency and presents quantitative latency evaluations using simulations of the edge architecture that demonstrate storage performance, memory capacity, and CPU utilization.
Sketch Image Classification Using Component Based k-NN

Recently, several researches on recognition of novice-users’ hand-drawn images have been conducted, specifically those on novel feature extraction techniques. Generation of a robust sketch descriptor is one of the most challenging problems in hand-drawn image recognition. Drawing or sketching is a common skill that everyone can do to a varying degree of success. Although an individual may or may not be able to create a beautifully drawn image, any human beings can recognize the shape of each part of a hand-drawn object and the entire object. In this work, we proposed a sketch image classification method that creates an image descriptor from its own components and uses k-NN algorithm for learning/classification. A sketch image can be created simply by drawing some simple geometric shapes. The drawing order, size, and number of the shapes are features that can be extracted and used to recognize an image. After an object in a drawn image has been classified as a car or a human being or anything else, the recognized object can be selected and paired with the motion associated with it. It was found that our proposed method was able to achieve a recognition accuracy rate of 92.33%. We also did a survey with children 7-12 years of age asking them whether they wanted an easy tool that can animate their hand-drawn objects and got almost unanimous affirmative responses from them.
QoS-Driven Coordinated Management of Resources to Save Energy in Multi-core Systems

Applications that are run on multicore systems without performance targets can waste significant energy. This paper considers, for the first time, a QoS-driven coordinated resource management algorithm (RMA) that dynamically adjusts the size of the per-core last-level cache partitions and the per-core voltage-frequency settings to save energy while respecting QoS requirements of individual applications in multi-programmed workloads run on multi-core systems. It does so by doing configuration-space exploration across the spectrum of LLC partition sizes and DVFS settings at runtime at negligible overhead. Compared to DVFS and cache partitioning alone, we show that our proposed coordinated RMA is capable of saving, on average, 20% energy as compared to 15% for DVFS alone and 7% for cache partitioning alone, when the performance target is set to 70% of the baseline system performance.
Hand-over-Face Gesture based Facial Emotion Recognition using Deep Learning

Facial emotion recognition is significant for several applications such as human-computer interaction, video surveillance and robotics. Occlusion covering the face is a barrier in clearly recognizing facial emotion. Hence, in early researches, the hand-over-face occlusion is either removed or ignored while recognizing emotion. However, hand-over-face (hand gesture) along with facial expression represents an emotion. The existing hand-over-face gesture based emotion recognition methods identify merely basic emotions due to coding schema with limited hand gestures. In this paper, we propose a new hand-over-face gesture based emotion recognition method that includes coding schema with more hand gestures and uses deep learning. Specifically, we use Convolutional Neural Network (CNN) that eliminates the need for manual feature extraction and extracts more class specific features automatically. Moreover, we use Recurrent Neural Network (RNN) to recursively learn the features and classify them into more advanced emotion categories. Hence, our proposed method identifies more advanced emotions such as confident, making decision, scared, ashamed and ok sign along with basic emotions.
Predictive Task Migration Modeling in Software Defined Vehicular Networks

Fog computing has promoted the rapid development of the Internet of Things (IoT), where task offloading is becoming one of the most achievable approaches to solve the limitation of on-board computing resources. However, in practical applications of intelligent transportation systems (ITS), the task offloading scheme lacks adaptability to rapidly changing dynamic network topologies. In this paper, leveraging the idea of logical centralized control in software defined networks (SDN), we investigate the optimization problem of fog node resource allocation in vehicular task offloading scenarios aiming to minimize the latency and energy usage. Specifically, we propose a resource migration method for real-time vehicle tasks to avoid redundant downloads of data through configuring fog node resources. Meanwhile, we build a predictive dynamic task migration model to evaluate our system by calculating the delay and energy consumption. Finally, the effectiveness of the model proposed in this paper is validated through a case study with processing of real-time road perception applications in a software defined vehicular network.
Finding best answer in community question answering sites: a review

Community question answering sites has seen a spectacular increase in popularity in the recent past. These are online communities in which user can interact on wide variety of topics The positive side is that one can freely ask questions and expect some good, honest answers from multiple experts across the world. But the negative side is that it takes effort to go through all possible answers and to make sense of them. There will be a list of answers for a question and we do not know which one is the best until a human based judgement is done over all possible answers. In this paper we are trying to analyze the different methods to find out the best answer for a question in community question answering sites automatically
Sharing Unused User IoT Devices in the Cloud

Internet of Things (IOT) is the way to dealing with technology to get the inter connection with any devices which is ubiquitous in nature as well as for the new generation devices which ultimately bring the various daily usage devices to make it more complex and integrated with network devices. The IOT socially provided network could allow the sharing of devices among the users which could combined provide the useful information which the sensors will capture through which the remote actions could be established. This research provides the cloud-based technology to embed to work regarding the IOT centred social device which provides the virtualized environment for any execution of further processes and being graceful to its non-centralized behaviour, highly reliable and accessible from anywhere and even at any time. It basically deals with the reusability of IOT resources by getting to action as a service, where the applications are made by the combination of various services which is deployed in a platform which could be hosts in cloud-platform which allows the secured accessibility of services as well as the data by the combining and compiling these IOT devices.
Object-based random forest classification for detecting plastic-mulched landcover from Gaofen-2 and Landsat-8 OLI fused data

Plastic-mulched landcover (PML) is an important type of agricultural landscape and remote sensing is an effective way for monitoring and mapping PML. Based on Gaofen-2 (GF-2) and Landsat-8 operational land imager (OLI) fused data, this study applied an object-based random forest classification (OBRFC) method, which combines object-based image analysis (OBIA) technology with random forest (RF) model, to map PML. The method consists of the following steps: (1) image segmentation with a multiresolution segmentation (MRS) algorithm; (2) selection of sample objects (or segments) and 50 features of index, texture, and shape based on prior knowledge and relevant references; and (3) determination of two particularly important parameters, the number of decision trees-T and the feature number of split nodes -F, by comparing classification accuracy of a series of experiments. The results from applying the OBRFC method on the study area show: 1) the best overall accuracy (OA) of OBARFC reaches 91.73%; 2) by setting T to 50, OA curve presents a downward trend with the highest value of 91.72% at F =5; and 3) by setting F to 5, OA reaches its best value at T = 50.
A Performance Evaluation of Data Protection Mechanisms for Resource Constrained IoT Devices

Data Protection is a major research topic concerning the Internet of Things (IoT). IoT systems continue to permeate deeper into our personal lives, where devices sense, process, and store all kinds of data. This poses various challenges to security and privacy aspects, especially to applications running on resource constrained devices. In this paper we evaluate selected, well established data protection mechanisms that enable confidentiality and integrity of data. Specifically, we look into the performance of different cryptographic block and stream ciphers, hashing algorithms, message authentication codes, signature mechanisms, and key exchange protocols executed on state-of-the-art resource constrained devices. By providing limitations and data throughput values, our obtained results ease the calculation of performance/data protection thresholds and facilitate the design and development of secure IoT systems.
Implementation of EV Charging Station with Renewable Energy Integration

Contactless Charger System is to charge the battery by utilizing remote charger. The innovation will supplant links and institutionalize on one interface, possibly having the option to charge battery. This is finished utilizing charging a resounding loop from AC and afterward transmitting consequent capacity to the resistive burden. The undertaking is intended to charge a low power gadget rapidly and effectively by inductive coupling without the assistance of wires. Remote charging framework depicted in this paper is by utilizing the strategy for inductive coupling. In this undertaking, wavering circuit changes over DC vitality to AC vitality (transmitter loop) to transmit attractive field by passing recurrence and afterward initiate the beneficiary curl. The properties of Induction coupling are wave (attractive field-wideband), run (very short~cm), proficiency (stature) and task recurrence. The undertaking appears as a little charging for 5V battery of telephone in this technique. The framework bases on coupling attractive field, at that point structured and developed as two sections. There are transmitter part and recipient part. The framework is alright for clients and neighboring electronic gadgets. To get increasingly precise remote charging framework, it needs to change the structure of the couple of watchwords.
Security Index of Linear Cyber-Physical Systems: A Geometric Perspective

This paper is mainly concerned with developing security indices for linear cyber-physical systems (CPS). The approaches for computing security (and consequently vulnerability analysis) of CPS in the literature are based on algebraic methods and system matrices. In this paper, for the first time in the literature we formally address the security index analysis and computation from a geometric system theory perspective. This point of view enables one to develop an algorithm for computing an upper bound on the security index having a linear time complexity with respect to dimension of the system (i.e., $O(n)$). This is a significant improvement compared to the currently available approaches in the literature that have polynomial time complexity. Unlike the approaches in the literature our methodology does not need any restriction on the representation of the system. Moreover, the geometric approach provides a tool to formally analyze the attack signals injected to the CPS by introducing a new type of attack that is more sophisticated than zero dynamic attacks. Finally, we illustrate our proposed methodology through a numerical example.
The Effectiveness of Parameter Tuning on Ant Colony Optimization for Solving the Travelling Salesman Problem

Ant Colony Optimization(ACO) is a metaheuristic approach which tackles the combinatorial optimization problems such as Traveling Salesman Problem(TSP). ACO is highly motivated by the foraging behavior of ants and the way they utilize pheromone trail to discover the food source. The performance of an ACO algorithm is directy dependent on the choice of its parameters. The bad selection of parameters increases the computation time and accuracy of the algorithm. In this paper we studied the impact of the basic parameters that are used in ACO algorithms such as, $\alpha$ which is the relative importance of pheromone, $\beta$ the relative importance of heuristics value and $\rho$ the evaporation rate. We conducted an experiment for the better choice of the parameter values inorder to increase the effectiveness of algorithm for solving travelling salesman problem.
Performance Analysis of Channel Coding Techniques for Cooperative Adhoc Network

-In wireless networks, Cooperative communication can be used to increase the strength of the communication by means of spatial diversity. Basic idea that exists behind Cooperative communication is, if the transmission from source to destination is not successful, a helping node called relay can be used to send the same information to the destination through independent paths. In order to improve the performance of such communication, channel coding techniques can be used which reduces the Bit Error Rate. Previous works on cooperative communication only concentrated on improving channel capacity through cooperation. Hence this paper presents different Channel coding methods such as Turbo coding, Convolutional coding, and low-density parity-check coding over Rayleigh fading channels in the presence of Additive white Gaussian noise. Performance of these Channel coding techniques are measured in terms of noise power spectral density (NO ) vs. Bit error rate.
FI-Net: A Lightweight Video Frame Interpolation Network Using Feature-Level Flow

Video frame interpolation is a classic computer vision task that aims to generate in-between frames given two consecutive frames. In this paper, a flow-based interpolation method (FI-Net) is proposed. FI-Net is a lightweight end-to-end neural network that takes two frames in arbitrary size as input and outputs the estimated intermediate frame. Novelly, it computes optical flow at feature level instead of image level. Such practice can increase the accuracy of estimated flow. Multi-scale technique is utilized to handle large motions. For training, a comprehensive loss function that contains a novel content loss (Sobolev loss) and a semantic loss is introduced. It forces the generated frame to be close to the ground truth one at both pixel level and semantic level. We compare FI-Net with previous methods and it achieves higher performance with less time consumption and much smaller model size.
Improving Power Quality of Distribution System Connected to Wind Power Systems

This paper deals with the enhancement of quality of electric power in distribution system with nonlinear load using UPQC connected with wind energy converter. Due to the use of hybrid renewable energy system, the power quality problems are very severe and also it is difficult to handle various resources of power. So here only wind energy converter is used to supply real power for the compensation. MATLAB is used for analysis. The controller and circuits are experimented with simulations.
A New Network Anomaly Detection Method Based on Header Information Using Greedy Algorithm

Network anomaly detection is an important and rapidly growing area. In this paper, we propose a new network anomaly detection method based on the probability distributions of header information. The distances between the distributions of packet headers are calculated to reflect the main characteristics of the network. These are calculated using the Greedy algorithm which eliminates some requirements associated with Kullback-Leibler divergence such as having the same rank of the probability distributions. Then, Support Vector Machine classifier is used in the detection phase to reduce false alarm rates and to make the system adaptive for different networks. This algorithm is tested on the real data collected from Boğaziçi University network and MIT Darpa 2000 dataset.
Feature Selection for Job Matching Application using Profile Matching Model

The advancement of technology has created big changes to speed up the job hiring process nowadays. With more tools developed, companies have embraced tools that help them recruits’ talents. This is a research-in-progress of developing a job matching application system for Job Recruitment Agency specifically in the province of Pangasinan, Philippines. This paper proposes an approach for the agency to extract the relevant information from resumes and analyze it based on the different attributes. With the identification of the attributes, the proposed system is directed to adopt a clustering algorithm to match the profile of the job seekers against the requirements of the job posted by the prospect employers. This helps the agency to find suitable candidates for a particular job in different companies and make more informed decisions.