Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Feedforward Radix-2^2 FFT 4-Parallel Architecture

A novel approach has been presented to extend radix-feed forward architectures used for the FFT. The future architectures utilize idleness in computation of FFT observations to decrease the hardware intricacy. Different algorithms have been urbanized to decrease the computational intricacy. A novel method to design such architectures from underlying FFT flow graphs. This work presents radix-feed forward architectures which necessitate smaller number of hardware resources than parallel feedback ones. To convert the DFG, we necessitate a downfall set, which is being intended set of actions implemented by the similar functional part.
Universal Semantic Web Assistant based on Sequence to Sequence Model and Natural Language Understanding

Virtual Assistants are complex systems that try to mimic the conversation with a user through textual inputs or voice commands. Chatbots are subsets of virtual assistant and the names can be used interchangeably. Web based Virtual Assistants offer platform independency and requires a client such as, a browser to open the chatbot. Online virtual assistants or chatbots offer a lot of use-cases and can be employed in online Businesses to offer customer services and to improve customer experience. Web based virtual assistant can enable basic automations that are repetitive in nature. chatbots behave as a part of semantic search and learn through their past experiences. Chatbot can be built either as a specialist chatbot or a generalized one. Our Web based Virtual Assistant is both a generalist and a specialist in the retail and ecommerce domain.Our virtual assistant attempts to answer the users basic queries and applies efficient Natural Language Processing (NLP) techniques to extract the context of the user query. The virtual assistant scrapes the Web and queries the database through efficient web crawlers and through the Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) modules respectively. Each web crawlers are small programs written to crawl certain part of the web and extract useful information in unstructured data format. Efficient Machine Learning and Deep Learning techniques are applied to extract and analyze the data.
Weighted Bayesian Association Rule Mining Algorithm to Construct Bayesian Belief Network

Bayesian network is an appropriate tool to work with the unpredictability and causality which arises in Clinical Domain. A Bayesian network can learn from medical datasets without explicit access to the knowledge of human experts. Thus, to built Bayesian network by learning method, strong rules are needed from datasets. To express statistical dependence relationships, association rules can be considered. This proposal expects to incorporate two techniques to improve the shortcoming of single technique, so this proposal put forward a Weighted Bayesian Association rule Mining Algorithm (WBAR) for the generation of strong Bayesian association rules for the construction of Bayesian network which combines the weighted concept with Association Rule Mining (ARM) to generate Weighted Two-attributes association rules, Weighted Multi -attributes association rules and Weighted Class Association rules. Two interesting dimensions of association rules mining: Weighted Bayes confidence (WBC) and Weighted Bayes lift (WBL) that assess the relationship between different attributes using conditional dependence and independence based on the joint probabilities which are symbolized and then interpreted by the Weighted Bayesian networks using association rules. The proposed algorithm WBAR results the most significant rules according to WBC and WBL
An improved Spray and Wait routing protocol for Delay Tolerant Network

Delay Tolerant Network(DTN) is a wireless network where source cannot have a handshake with the destination and source has to use a routing known as opportunistic routing to deliver the message to the destination. One of the most widely used routing protocol in DTN is Spray and Wait(SnW) routing protocol and there are a lot of methods to complete the spray phase, most popular is Binary SnW. In this paper, we are proposing a modified version of Binary SnW protocol referred as Hexago SnW. In this approach, we used hexagonal series instead of binary series. The comparison of Hexago SnW with other existing approaches shows that Hexago SnW gives better performance than others in term of overhead ratio, delivery ratio etc. In simulation, two groups of moving objects are used to show the working and performance evaluation of our proposed routing technique.
A Novel Convolutional Neural Network Accelerator That Enables Fully-Pipelined Execution of Layers

In this paper, we propose a novel CNN accelerator, called MIDAP, aiming to maximize the utilization of MAC units by enabling fully-pipelined execution of layers. To this end, MIDAP adopts two-level pipelining, macro pipelining and micro pipelining, and large on-chip SRAMs. The macro pipeline consists of three modules for convolution, activation, and pooling layer and each module accesses separate memory units without access conflict. For micro-pipelining inside the convolution module, the datapath is designed to be free from dynamic resource contention. Also, the inter-layer feature map reuse is maximized by compile-time analysis. From the simulation results on 1GHz frequency, MIDAP shows the remarkable end-to-end performance of several well-known CNNs: for instance, 144 fps for Inception V3 model and 892 fps for Mobilenet V1 model with 1024 MACs. A high-level synthesis result reveals that our accelerator is able to achieve about 2.0 TOPs/W with a small area less than 2mm^2 with 8nm CMOS technology, thanks to its simple datapath.
An Efficient Design of Spiral Shaped MEMS Energy Harvester for Low Power Electronic Applications

This paper presents an efficient spiral cantilever structure for scavenging electric energy from ambient vibration using the piezoelectric principle. Most of the piezoelectric energy harvester are designed using variation in large tip masses and here we tried to optimized the shape in order to obtain higher power at lesser active volume. A bimorph square spiral shaped energy harvester operating at frequency <100 Hz is proposed using lead-free zinc oxide piezoelectric material. The simulation results demonstrate that the harvester resonates at a low frequency of 72 Hz. The vibration mode shapes and stress distribution was obtained using finite element simulation using COMSOL Multiphysics software. The peak harvested power of 0.27 nW is scavenged from seven turns harvester at an input acceleration of 1g, The proposed harvester exhibits volumetric power density of 66.6 nW /mm3.
Quadratic Approximation Method for the Limit Value of Magnetic Stiffness in a High Temperature Superconducting Levitation System

A quadratic approximation method for the limit value of magnetic stiffness in a high temperature superconducting levitation system is presented. The levitation configuration discussed is that of a cylindrical permanent magnet (PM) placed above a coaxial high temperature superconductor (HTS). The magnetic levitation force between the PM and the HTS is gained based on Kim's critical model and Ampère circulation theorem. The central issue of magnetic stiffness associated with the hysteresis of levitation force is discussed. Firstly, To a given levitation gap between the PM and the HTS, the approximate values of magnetic stiffness is obtained corresponding to different displacement increments from 0.05 mm to 3 mm. In the first approximation the least square method is used for curve fitting force-displacement table. Secondly, the limit value of magnetic stiffness is gained at the zero displacement increment in the polynomial fitting curve of the first approximate values. The results show that the limit value of magnetic stiffness is dependent on the large levitation gap and the movement direction of the levitation object. The given displacement increments, such as 0.5 mm or 1 mm, are more suitable in superconducting levitation experiments. The difference between experiment data of magnetic stiffness and the limit ones is investigated.
Classification of Exogenous Anomalies and Self-Diagnosis in Autonomous Robots

This paper presents methods for the autonomous identification and classification of disturbances that have negative effects on a robot's performance. The proposed methods have been implemented in a walking hexapod robot provided with a number of sensors. Both, the robot's sensorial information and a quantitative measure of the robot's performance are obtained. This information is used for detecting, identifying and classifying obstructive conditions that have a strong impact on the robot's performance. Once the cause of a lack of progress in the robot's mission has been identified, suitable compensatory actions are found, executed and recorded. Then, when previously experienced detrimental situations arise, the associated compensatory measures are immediately taken without involving a searching process. As a result, the recovery from abnormal conditions is accelerated and the robot can promptly continue with its mission. In order to evaluate the performance of the proposed methods, a number of different sets of experiments addressing the robot's hardware faults, abnormal situations generated in the robot's environment and a combination of both, were conducted. In this process, two indicators were utilized: the number of attempts before a correct identification of the robot's hardware fault was achieved, and a discrepancy measure. Results showed a good identification rate inside the range of considered abnormal situations.
Minimizing Monetary Costs for Deadline Constrained Workflows in Cloud Environments

As one of the latest market-oriented resource provisioning paradigms, cloud computing has been widely adopted by a growing number of consumers due to its powerful computing ability and storage ability. Although cloud computing can achieve effective cost reduction and convenience enhancement in the development of large-scale applications, it results in a complex cost optimization problem for data-dependent tasks represented by a workflow. All tasks in a workflow should be scheduled according to a proper strategy such that the cost is minimized and the precedence constraints and timing requirements are satisfied. In this paper, we study the cost optimization problem of deadline constrained workflows on cloud computing, and propose two list scheduling algorithms named Look-back Workflow Scheduling (LBWS) and Structure Aware Workflow Scheduling (SAWS) to solve the problem. LBWS distributes the deadline over the workflow as sub-deadlines to tasks in different levels, and schedules the tasks according to their priorities to the resources which meet their sub-deadlines and the best time-cost trade off requirements. Compared with LBWS, SAWS considers tasks allocated to the same level at a time and provisions resources with minimum cost to these tasks. Experiments on scientific workflow applications with different data and computational characteristics are conducted to show that, the proposed approaches can achieve better performance in terms of success rate and monetary cost.
Seg-Edge Bilateral Constraint Network for Iris Segmentation

Iris semantic segmentation in less-constrained scenarios is the basis of iris recognition. We propose an end-to-end trainable model for iris segmentation, namely Seg-Edge bilateral constraint network (SEN). The SEN uses the edge map and the coarse segmentation to constrain and optimize mutually to produce accurate iris segmentation results. The iris edge map generated from low level convolutional layers passes detailed edge information to iris segmentation, and the iris region generated by high level semantic segmentation constrains the edge filtering scope which makes the edge aware focusing on interesting objects. Moreover, we propose pruning filters and corresponding feature maps that are identified as useless by l1-norm, which results in a lightweight iris segmentation network while keeping the performance almost intact or even better. Experimental results suggest that the proposed method outperforms the state-of-the-art iris segmentation methods.
Context Aware School Zone Warning System in VANETs

School zones are areas close to schools in which crossing vehicles represent a threat to school children. Due to various reasons, drivers might not be aware that they are approaching school zone areas. Utilizing wireless technology in vehicular environments can improve road safety and reduce the number accidents through the development of vehicle safety applications. This paper proposes a context-aware school zone warning system in VANETs. A context aware On-Board Unit architecture is proposed to detect active school zones and provide timely notifications to drivers to safely reduce the speed of their vehicles. The architecture is divided into three layers (i.e. sensing, reasoning, and acting layers). The sensing layer is responsible for collecting the required information to identify active school zones. The reasoning layer identifies the active school zones and sends signals to the application layer, which is responsible for alerting drivers through in-vehicle alarms.
A Mobile Robot Visual SLAM System With Enhanced Semantics Segmentation

Traditional visual simultaneous localization and mapping (SLAM) systems mostly based on small-area static environments. In recent years, some studies focused on combining semantic information with visual SLAM. However, most of them are hard to obtain better performance in the large-scale dynamic environment. And the accuracy, rapidity of the system still needs to strengthen. In this paper, we develop a more efficient semantic SLAM system in the two-wheeled mobile robot by using semantic segmentation to recognize people, chairs, and other objects in every keyframe. With a preliminary understanding of the environment, fusing the RGB-D camera and encoders information, to localization and creating a dense colored octree map without dynamic objects. Besides, for the incomplete identification of movable objects, we used image processing algorithms to enhance the semantic segmentation effect. In the proposed method, enhanced semantic segmentation in keyframes dramatically increases the efficiency of the system. Moreover, fusing the different sensors can highly raise localization accuracy. We conducted experiments on various datasets and in some real environments and compared them with DRE-SLAM, DS-SLAM, to evaluate the performance of the proposed approach. The results suggest we significantly improve the processing efficiency, robustness, and quality of the map.
An intelligent applied Fuzzy Logic to prediction the Parts per Million (PPM) as hydroponic nutrition on the based Internet of Things (IoT)

Hydroponic plants require regular monitoring of plants that grow well and can even cause plants to die. Because too late in providing nutrition or getting too little or too little food or routine monitoring in the hydroponic system. The innovation proposed in the agricultural sector that we suggest is the process of monitoring hydroponic plants in real-time. This study aims to predict the excess or lack of nutrient content based on the results of routine monitoring and has the analytical ability to determine the prediction of the value of Parts per Million (PPM) to find the nutritional value of plant media. Measurement of nutritional value using Electrical Conductivity (EC) and the potential of Hydrogen (pH) in hydroponic plant media. In this research, using the NFT hydroponic system as the application of fuzzy logic with the Mamdani method to determine the predictions of PPM values in providing nutrients at plants to adjusting the nutritional at each plant (e.g., for water spinach plants are need 1050 until 1400 PPM). The implemented is method Internet of Things (IoT) to remote sensing on measure using a smartphone as the representation of the measurement results on the hydroponic system.
Gamification in e-Govemment: A Systematic Literature Review

Previous studies indicate that gamification in e- government potentially increases social interactions between citizens and government. As a result, it can lead to more active participation of citizens in government programs. However, there are many cases of e-government that are lack of citizens' involvement. To address this problem, a Systematic Literature Review (SLR) with Kitchenham method on gamification in e- government was employed. It analyzed lesson learned from gamification in e-government, which was implemented in 8 countries in Asia, Europe, America and Australia. The results show that citizens prefer accepting rewards such as money, better feeling, digital badges, and certificates of appreciation to take part in e-government. These findings become inputs for further study to develop a protype of gamification for e- government in Indonesia.
Residential Asset Pricing Prediction using Machine Learning

Residential asset price prediction and analysis are prevalent research topics in economy. Most researches focus on macroeconomy perspectives to explain the factors affecting residential asset prices. In this paper we examine some micro factors, like lot area, pool area, that can be used as features to predict house price. We fit a rather simple regression model which contains a few characteristics of a residential asset, and we are able to reach a fairly good result. Some machine learning algorithms such as random forest and support vector machine are also implemented to predict asset pricing. All regression models have a R squared over 0.9.
26.8 m 350 GHz Wireless Transmission of Beyond 100 Gbit/s Supported by THz Photonics

A THz photonic wireless communication system with a record transmission of 100.8 Gbit/s over 26.8 m distance operating at 350 GHz is experimentally demonstrated without any THz amplifications. The probabilistic shaped 16-QAM-OFDM and advanced nonlinear digital signal processing routine are employed.
Design of SAR ADC to Light Charging for Optical Sensors Applications

The proposed successive approximation register (SAR) analog-to-digital converter (ADC) for integrates optical sensor. The heart rate sensor including SAR ADC and OPVs are presented. To reduce DAC switching energy, a hybrid resistor-capacitor DAC is applied. The optimal design used charging near infrared laser driven (NIRLD) with thin Fresnel and OPVs light with VCSEL. The charging near infrared laser driven (NIRLD) with thin Fresnel might be a promising OPVs light power supplier and low consumption, asynchronous control logic to drive the ADC is used. A pre-amplifier-based comparator circuit is built to reduce the kickback noise from the dynamic latch designs for optical sensor applications.
A Novel Quantitative Stock Selection Model Based on Support Vector Regression

Facing the huge challenges brought by changing market environment, the academic community is constantly looking for factors and combinations that can obtain excess returns on basis of traditional multi-factor stock selection model. Compared with the traditional linear multi-factor model, the machine learning algorithm can capture the more granular market signal by the nonlinear expression of the factor. To mine the stock factor data and optimize the stock selection model, this paper uses the equal weight linear model, machine learning support vector machine and linear regression algorithm for factor analysis. Based on the theory of SVM machine learning algorithm and multi-factor stock selection, this paper establishes and solves the SVR stock market forecasting model by characterizing the data. Then, we give and analyze examples after combining relevant data. The results show that the factors such as PB, PE, ROE, NetProfitGrowRate, OperatingRevenue-GrowRate, EPS and NegMktValue are outstanding. After putting excellent factors into the SVR model, the return rate of the stock portfolio is far greater than that of the traditional equal weight linear model, which indicates that the stock selection model using the machine learning algorithm has higher returns and stable results. This paper provides some guidance for decision makers to formulate stock picking strategies by mining stock factor data.
Flip-OFDM Based Generalized Spatial MIMO for VLC

Today's visible light communication (VLC) is popular area in of optical wireless for indoor applications. In this paper, generalized spatial modulation multiple-input multiple-output (generalized SM MIMO) technique is analyzed using flip orthogonal frequency division multiplexing (F- OFDM) to optimize VLC performance. The F-OFDM is used to obtain unipolar signal in VLC. The F-OFDM is compared with DC offset optical OFDM (DCO OFDM) and asymmetrically clipped optical OFDM (ACO OFDM) for generalized SM MIMO technique. The performance of bit error rate (BER) is analyzed in regard with signal to noise ratio (SNR). Simulation results shows that F-OFDM gives better performance.
Plant leaf segmentation through connected pixel approach

Agricultural plays a significant role in human survival and it has become much more essential due to population increase and food demand, and hence the crop yield has to be produced according to the demand. However, one of the reason that quality and quantity of the crop gets compromised is the disease and in past various methodology has been proposed, however they lack on the various model metrics or the segmentation is achieved for the particular leaf,. In this paper, we have proposed a methodology named as SCPA (Segmentation through Connected Pixel Approach). The main objective of this paper is to achieve high accuracy segmentation. SCPA is the two step approach first we find the ROI(Region of Interest) of the particular leaf and in the second approach we find the instance based ROI i.e. for the whole plant, here both the step are performed simultaneously through incorporating one another. Moreover, SCPA is optimized iterative-based method and it is achieved through the approach of connected pixel approach. Connected pixels are the one where the edge of one pixel is connected to the other. When performed on the LSC dataset we achieve the accuracy of 95.10%. This methodology is compared with the various state of art model and existing system by considering the model metric such as SBD, the results shows that SCPA model performs better than the other exiting method also the pictorial comparison of segmented leaf are shown and it shows our model identify it well when compared to others.
Test Case Generation Based on Client-Server of Web Applications by Memetic Algorithm

Currently, more than 90% web applications are potentially vulnerable to attacks from both the client side and server side. Test case generation plays a crucial role in testing web applications, where most existing studies focus on test case generation either from client-side or from server-side to detect vulnerabilities, regardless of the interactions between client and server. Consequently, it is difficult for those test cases to discover certain faults which involve both client and server. In this paper, the server-side sensitive paths are considered as vulnerable code paths due to insufficient or erroneous filtering mechanisms. An evolutionary testing approach based on the memetic algorithm is proposed to connect the server-side and client-side, in which test cases are generated from the client-side behavior model, while guided by the coverage of sensitive paths from server-side. The experiments are conducted on four open source web applications, and the results demonstrate that our approach can generate test cases from the client-side behavior model that can cover the server-side sensitive paths, on which the vulnerabilities can be detected more effectively.