Miracle-dz/UniFAR-FTUs huggingface dataset:
Segmentation of Background and Foreground for Ancient Lanna Archaic from Palm Leaf Manuscripts using Deep Learning

This research aimed to present a method of image segmentation of background and foreground in the image processing of ancient Lanna archaic that have been recorded on palm leaf manuscripts. The proposed method began by randomising 2,500 pixels from 10 palm leaf manuscript images in a RGB colour space, followed by clustering the randomised pixels (trained dataset) using K-means, when k=(2,3,4,5,6,7, 8). Each k cluster applied was required to provide the accuracy of more than 85 percent. A deep learning algorithm was selected as a predictive model. Then, the results from the segmentation of background and foreground of archaic were compared with Otsu’s method, a widely renowned image segmentation method. Consequently, the proposed method did not result in satisfactory background and foreground segmentation. When analysing the causes of inadequate results, it was found that it could be due to the inadequacy of 2,500 pixels as dataset and the similarities of 10 sampled palm leaf manuscript images in terms of light intensity and colour, whereas the palm leaf manuscripts used for verifying the proposed method were different from the trained dataset in terms of colour intensity and light intensity, hence resulting in the inefficient image segmentation.
HashMTI: Scalable Mutation-based Taint Inference with Hash Records

Mutation-based taint inference (MTI) is a novel technique for taint analysis. Compared with traditional techniques that track propagations of taint tags, MTI infers a variable is tainted if its values change due to input mutations, which is lightweight and conceptually sound. However, there are 3 challenges to its efficiency and scalability: (1) it cannot efficiently record variable values to monitor their changes; (2) it consumes a large amount of memory monitoring variable values, especially on complex programs; and (3) its excessive memory overhead leads to a low hit ratio of CPU cache, which slows down the speed of taint inference. This paper presents an efficient and scalable solution named HashMTI. We first explain the above challenges based on 4 observations. Motivated by these challenges, we propose a hash record scheme to efficiently monitor changes in variable values and significantly reduce the memory overhead. The scheme is based on our specially selected and optimized hash functions that possess 3 crucial properties. Moreover, we propose the DoubleMutation strategy, which applies additional mutations to mitigate the limitation of the hash record and detect more taint information. We implemented a prototype of HashMTI and evaluated it on 18 real-world programs and 4 LAVA-M programs. Compared with the baseline OrigMTI, HashMTI significantly reduces the overhead while having similar accuracy. It achieves a speedup of 2.5X to 23.5X and consumes little memory which is on average 70.4 times less than that of OrigMTI.
Distributed Asymptotic Consensus in Directed Networks of Nonaffine Systems With Nonvanishing Disturbance

In this paper the distributed asymptotic consensus problem is addressed for a group of high-order nonaffine agents with uncertain dynamics, nonvanishing disturbances and unknown control directions under directed networks. A class of auxiliary variables are first introduced which forms second-order filters and induces all measurable signals of agents' states. In view of this property, a distributed robust integral of the sign of the error (DRISE) design combined with the Nussbaum-type function is presented that guarantees not only the desired asymptotic consensus, but also the uniform boundedness of all closed-loop variables. Compared with the traditional sliding mode control (SMC) technique, the main feature of our approach is that the integral operation in the proposed control algorithm is designed to be adopted in a continuous manner and ensures less chattering behavior. Simulation results for a group of Duffing-Holmes chaotic systems are employed to verify our theoretical analysis.
Deep Learning Model for Image-Based Plant Diseases Detection on Edge Devices

Plant diseases are a major threat to global food security. Infection of plants by pathogens can have serious consequences on plant health which inherently affects human health. With more than 80 percent of agricultural production done by smallholder farmers who depend on healthy crop yield for their livelihood, yield loss of more than 50 percent due to pests and diseases is reported. Currently, infectious diseases reduce the potential yield by an average of 40% with many farmers in the developing world experiencing yield losses as high as 100%. The widespread distribution of smartphones among crop growers around the world with an expected 5 billion smartphones by 2020 offers the potential of turning the smartphone into a valuable tool for diverse communities growing food. One potential application is the development of mobile disease diagnostics through machine learning and crowdsourcing. With the help of state of art technologies like deep learning and cloud computing, the same can be achieved on a real-time basis. With the help of Convolutional Neural Network architectures, a system is proposed that focuses on detection and identification of the plant disease with a mere click of leaf picture and provides solutions.
Customer Knowledge Management Framework For SME Herbal Cosmetics Using Social Media Strategy

The small and medium sized enterprises (SMEs) in the north of Thailand lack the knowledge of marketing and selling products online to China. This paper aims at designing a framework using customer knowledge management (CKM) based on social media strategy so as to improve customer experiences. With the use of text mining and Kano model analysis in supporting knowledge management process, knowledge-about and knowledge-from customers can be extracted. The output of the study is a prototype of social media application so that knowledge-for customers is generated and delivered to the target customer of the SMEs.
Regression Test Selection Tool for Python in Continuous Integration Process

In this paper, we present a coverage-based regression test selection (RTS) approach and a developed tool for Python. The tool can be used either on a developer’s machine or on build servers. A special characteristic of the tool is the attention to easy integration to continuous integration and deployment. To evaluate the performance of the proposed approach, mutation testing is applied to three open-source projects, and the results of the execution of full test suites are compared to the execution of a set of tests selected by the tool. The missed fault rate of the test selection varies between 0–2% at file-level granularity and 16–24% at line-level granularity. The high missed fault rate at the line-level granularity is related to the selected basic mutation approach and the result could be improved with advanced mutation techniques. Depending on the target optimization metric (time or precision) in DevOps/MLOps process the error rate could be acceptable or further improved by using file-level granularity based test selection.
Augmented Multidimensional Convolutional Neural Network for Industrial Soft Sensing

In the era of industrial big data, data-driven soft-sensor models have become an important method to guide production and optimize control. However, due to the limitation of data acquisition methods, the industrial data obtained in practice have unavoidable defects that affect the performance of soft sensors. Aiming at the unbalanced sampling, inaccurate matching, and partial missing of industrial process data, this article proposes an augmented multidimensional convolutional neural network (CNN) for industrial soft sensing. For complete process data information, we stitch the fine-grained data to obtain coarse-grained data and then use a CNN to extract deep features. On the basis of analyzing the physical meaning of data, the structure of multidimensional convolution is designed to focus on different details of process information. In this framework, the problem of partial missing data is emphasized. Then, mix-grained data augmentation strategies are invented to solve this problem and improve the performance of multidimensional CNN (MDCNN) soft sensors. The proposed augmented MDCNN is applied to the f-CaO prediction of the cement clinker production process, results of which show superiority compared to existing methods.
Virtual Reality Games based on Brain Computer Interface

This article provides a systematic review of games designed based on the brain-computer interface (BCI). This article aims twofold: The first is to review different configuration types for the controlling system based on current research; the second one is about the main challenges in the research progress, as well as the corresponding feasible solutions. This article contains the research progress of BCI in the game field in recent years and how BCI technology is applied to the virtual reality (VR) game field. A brief summary of this technology is given in the last section.
Smart System for Vehicle Periodic Roadworthiness Tests

The article presents the current state of the periodic technical inspection topic, both local (Romania) and European Union level. Then it is described a practical approach addressing the issues, namely a smart system for monitoring the plays that are present in the steering system and the axles of a vehicle. The main goal is to assist the inspection personnel to perform an enhanced and efficient periodic technical inspection procedure, knowing that the malfunction of the mechanical joints system of a vehicle leads to hazardous events involving human casualties.
Design and Analysis of Secure Quasi-Adiabatic Tristate Physical Unclonable Function

Hardware security modules have become quintessential as the digital systems continue evolving. Physical Unclonable Function (PUF) is a hardware security module that exploits the intrinsic variations in manufacturability of integrated circuits. Proposed Quasi-adiabatic Tristate PUF is a weak PUF which makes use of the inherent mismatches found in the threshold voltage values of PMOS transistors, which inadvertently is introduced due to manufacturing process variations of ICs. The proposed design demonstrates lower energy consumption than those configured using comparable quasi-adiabatic logic counterparts, under varying temperature and peak voltage conditions. The design is compared using SRAM PUF design based on adiabatic logic and it is shown that the proposed one is having better uniqueness and reliability at different environmental conditions. The proposed PUF can generate 128 CRPs which can be well suited for IoT and RFID applications that require less energy to operate.
Development of A Guided Drone Powered by Radio Frequency Energy Harvesting

This paper developed a guided drone or quadcopter or quadrotors energized by Radio Frequency Energy Harvesting, one type of energy scavenging that notwithstanding its least yield power among any other energy scavenging types, its all the time accessibility, batteryless and availability wherever make its own points of interest no other sort of energy scavenging has. A 32bit Acro Naze32 Brushed Flight Controller was used in this work as it was easier to program with the BetaFlight software and able to operate with minimal supply of 3.7 Volts. A 3 Watt independent power transmitter with frequency of 915 Mhz used to test the model to supplant the encompassing accessible radio frequency resources. The outcomes depicted that the drone was successfully flew in indoor and outdoor environment, able to take off from ground, hover in the sky and land, the maximum range of the drone is around 200 metre as a micro receiver was used for the drone construction, While the battery cycle of the constructed drone is around 7 minutes up to 12 minutes.
Coordinated Converter-Inverter PI based Grid Tied Photovoltaic System

In the distribution (dispersed) generation the problems of grid stability, maintaining the user friendly tariff, optimizing the non-renewable energy resources utilization and reducing the faults magnitude and time of clearance, reduction of the peak load faced by the conventional power plant has to be solved in an effective way. For achieving this, integration of grid with the solar panel are to be made more stable with lower harmonics and maximizing the transient state stability limit. This paper deals with the novel configuration of coordinated “MPPT controller controlling the boost converter” with “PI controller controlling the PWM signal of the inverter”. Modified Perturb with Observe (P with O) algorithm is used as it has better performance than normal P & O when dealing with tracking of voltage. The switching of the boost converter is based on this voltage. Simulation results show that by proper tuning of PI controller the transient disturbance can be reduced.
Comparison of Feature Selection and Classification for Human Activity and Fall Recognition using Smartphone Sensors

Human activity and fall recognition have been applied into many applications, especially, daily activity and healthcare systems. The recognition performance is up to the features used in the classification. Applying all features might degrade the performance since irrelevant features are used. In this paper, we study and compare four filter methods of feature selection: Chi square, mutual information, ANOVA, and Pearson’s coefficient. Six supervised classifiers are used to evaluate the classification performance according to the sets of features obtained from these methods. The dataset UniMiB SHAR, which consists of nine basic daily activities and eight types of falls, is investigated in the experiment. As a result, among all of the considered feature-selection methods and classifiers, the XGBoost incorporating with the mutual information method gives the best performance whose accuracy, precision, recall, and F1 scores are 0.9122, 0.8727, 0.8629, and 0.8640, respectively.
AI Support Marketing: Understanding the Customer Journey towards the Business Development

Artificial Intelligence (AI) fundamentally works on the antecedents of business management’s exploration, systematical incorporation of businesses details, and focus on business extension. The main purpose of this study is to know the morals of services related to AI in order to develop the marketing and businesses. This study shows the opinions of marketers to know the value of Artificial Intelligence. We propose the research model so that study can be seen in one frame. Study indicates the observations of marketers regarding AI which is composed of 12 services of marketing, the 4Ps (Product, Price, Place, Promotion), the 4Cs (Consumer, Cost, Convenience, Communication), and the 4Es (Experience, Exchange, Everyplace, Evangelism). Using Cronbach’s alpha to analyze the data collected from 508 samples. We showed the reliability and validity of the data so that it can be used for further analysis. We proposed the hypothesis which showed the relationship of each marketing service with AI for developing the business. Consequently, results indicates that all the services, except Evangelism, have positive relationship with AI. Additionally, study also showed that AI highly works on the business development. And marketing also shows significant effect on Business development. Study also offers some important implications for business development that further research should be done on different services, area and audience.
Numerically stable Digital Predistortion Model for Over-the-Air MIMO transmission

Digital Predistortion technique is the most economical and reliable technique among all the linearization techniques available. This paper adduces the idea of linearization based on piecewise polynomial or essential spline functions as an integral solution to the MIMO nonlinearity and compares with the Crossover Memory polynomial model. MIMO nonlinearity is strongly coupled together due to the combined effect of PA nonlinearity and crosstalk. For the proof of concept, the method is implemented over the air on 2X2 MIMO, and simulation is performed for 4X4, 8X8, 16X16 MIMO System. Numerical evaluation of the technique is exhibited in terms of NMSE, BER, and EVM. , while numerical stability is presented in terms of Condition number and Dispersion coefficient.
A Benchmark for Visual Positioning from Depth Images

In the last years, there has been an increasing interest in research on visual positioning and localization. Several datasets have been published recently. However, little attention has been paid to architectural aspects of scenes that have the advantage of being not influenced by scenery changes. Based on depth videos recorded using depth sensor, we propose in this paper a new dataset composed by descriptors of 2D maps associated to computed 3D structures. We show also how to interrogate the dataset using a set of depth frames acquired in visited places. Conducted experiments show the accuracy of the obtained 2D maps and feasibility of the proposed framework.
Designing Narratively Driven Learning Activities for Blended Learning Experiences

University of Naples Federico II (Italy) conducts a formative software development training program for the Apple technology ecosystem using the Challenge Based Learning (CBL) methodology. To overcome shortcomings of spiral curriculum principles, the course design adopts a Narrative Experience Design approach to let the learning progression be driven by learners as co-authors and support unscripted, emergent narratives for experiential education to foster learners' engagement, agency, and creativity. In consideration for synchronous and asynchronous learning modalities, the authors developed a systematic approach for narratively driven learning activity design for application in blended or remote learning scenarios. In this paper, the authors present their activity design process to compose learning activities with narratively sequenced atomic elements to address different learning dimensions. Through a collaboration tool-based Learning Activity Design Canvas for blended or remote learning, the authors further contribute towards a comprehensive framework for designing educational experiences for project-based education.
RobUSt–An Autonomous Robotic Ultrasound System for Medical Imaging

Medical ultrasound (US) systems are widely used for the diagnosis of internal tissues. However, there are challenges associated with acquiring and interpreting US images, such as incorrect US probe placement and limited available spatial information. In this study, we expand the capabilities of medical US imaging using a robotic framework with a high level of autonomy. A 3D camera is used to capture the surface of an anthropomorphic phantom as a point cloud, which is then used for path planning and navigation of the US probe. Robotic positioning of the probe is realised using an impedance controller, which maintains stable contact with the surface during US scanning and compensates for uneven and moving surfaces. Robotic US positioning accuracy is measured to be 1.19± 0.76mm. The mean force along US probe ${z}$ –direction is measured to be 6.11±1.18N on static surfaces and 6.63±2.18N on moving surfaces. Overall lowest measured force of 1.58N demonstrates constant probe-to-surface contact during scanning. Acquired US images are used for the 3D reconstruction and multi-modal visualization of the surface and the inner anatomical structures of the phantom. Finally, K-means clustering is used to segment different tissues. Best segmentation accuracy of the jugular vein according to Jaccard similarity coefficient is measured to be 0.89. With such an accuracy, this system could substantially improve autonomous US acquisition and enhance the diagnostic confidence of clinicians.
A RRAM based Max-Pooling Scheme for Convolutional Neural Network

In this paper, a novel max-pooling scheme for the convolutional neural network (CNN) is proposed and implemented by a simple analog RRAM based circuit. By taking advantage of the RRAM multilevel characteristics in the current driven mode, the proposed scheme can automatically detect and memorize the largest accumulative current in the crossbar during pooling operation. The simulation result of MNIST is studied and proved that this scheme can save 90.0% energy and increase 51.5% in speed. The impacts of typical non-ideal effects on accuracy are also investigated.
Passing a Non-verbal Turing Test: Evaluating Gesture Animations Generated from Speech

In real life, people communicate using both speech and non-verbal signals such as gestures, face expression or body pose. Non-verbal signals impact the meaning of the spoken utterance in an abundance of ways. An absence of non-verbal signals impoverishes the process of communication. Yet, when users are represented as avatars, it is difficult to translate non-verbal signals along with the speech into the virtual world without specialized motion-capture hardware. In this paper, we propose a novel, data-driven technique for generating gestures directly from speech. Our approach is based on the application of Generative Adversarial Neural Networks (GANs) to model the correlation rather than causation between speech and gestures. This approach approximates neuroscience findings on how non-verbal communication and speech are correlated. We create a large dataset which consists of speech and corresponding gestures in a 3D human pose format from which our model learns the speaker-specific correlation. We evaluate the proposed technique in a user study that is inspired by the Turing test. For the study, we animate the generated gestures on a virtual character. We find that users are not able to distinguish between the generated and the recorded gestures. Moreover, users are able to identify our synthesized gestures as related or not related to a given utterance.