Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Method for Comparing Test Suites for Developing Reliable Software Applications

Test suites play an important role in developing reliable software applications. Generally, the behaviour of software applications is verified by executing test suites to find defects. The reliability of software correlates with the quality of the test suite used to test it. Therefore, the quality of a test suite needs to be evaluated and enriched (if needed) especially for testing critical systems, such as plane-navigation system.This paper presents a novel method for comparing concrete and executable test suites using equivalence classes. This comparison identifies gaps in test suites with respect to each other. These gaps indicate potential weaknesses in the test suites. Further, this method provides a mechanism to enrich the test suites using these gaps. In this method, we devise equivalence classes, and associate each test case to an equivalence class. We, then, simulate the comparison of test suites by comparing sets of equivalence classes.The method compares test suites in a platform independent manner. The test suites, which are actually compared, are smaller than the original test suites because the redundant test cases are removed from the test suites, which makes it efficient. Further, it allows us to use any arbitrary criterion for devising equivalence classes which makes this method viable in various situations.We exercise our method over a couple of case studies to demonstrate its viability and effectiveness. The effectiveness of the method is validated using mutation and coverage analyses.
Prototype of Application Multi-Objective Genetic Algorithm Using Multi- Threading Strategy with Thinking Design Approach Method for Optimization Design DNA Primer and DNA Probe

An application prototype with design thinking approach method using 5 stages Empathize, Define, Ideate, Prototype and Test. The design thinking approach involves several experts. Expert of Genetic Algorithm, Molecular Biology and some Experts from East West Seed Indonesia company (EWINDO) who is user of this application. The application used by EWINDO at this time, can only design 20 - 40 primers and probes in a day or 8 working hours. The result of design thinking approach with some experts is the application can design some Deoxyribonucleic Acid (DNA) primers and DNA probes in 1 time process for each sequence, and sequences are inserted into the application will be more than 1. Design DNA primer and DNA probe have many parameters. The parameters are Product Length (amplicon), DNA Length, Repetition, Temperature Melting, GC Content, GC Clamp, Primary Secondary Structure, Self and Pair Complementary, Specificity and Mismatch. Multi Objective Genetic Algorithms (MOGA) with a multi-threading strategy can solve this problem. MOGA in this case is the number of objective functions possessed Genetic Algorithm (GA). The objective function is the parameter used for the design DNA primer and DNA probe. Multi objective is used to calculate the fitness value, where each objective function will be summed to be the fitness value. The design process for Any DNA sequence inserted into the application will be given 1 thread, or $n$ sequence = $n$ thread. This multi-threading strategy can accelerate the design with many DNA sequences for a one-time process. Currently development of application prototype has been completed. The next stage of testing, improvement and recommendations. Testing and improving the design of the DNA primer and DNA probe based on the parameters, for recommendation is experiment result of variation of combination of GA parameter.
Under-Actuated Back-Stepping: An Introduction

The stabilization problem for a class of under-actuated systems is solved. This is achieved via a novel backstepping based method that we call under-actuated backstepping. The method is developed for linear under-actuated systems first and then extended to nonlinear systems via an example. Numerical simulations are given to demonstrate the effectiveness of the proposed under-actuated back-stepping method.
A GaN-on-Si MMIC Doherty Power Amplifier for 5G Applications

This contribution presents the design of a two-stage Monolithic Microwave Integrated Circuit (MMIC) Doherty power amplifier (DPA) for 28 GHz 5G applications. The circuit is realized on a novel Gallium Nitride on Silicon (GaN-Si) technology with 100 nm gate length. Accounting for the peculiarities of the selected technology, especially the lossy substrate, effective circuital solutions to optimize the overall DPA performance are proposed and applied to finalize the chip layout. Expected performances at 28GHz are about 13 dB, 35% and 2W of gain, efficiency and saturated output power, respectively. To the best of authors' knowledge, this is the first implementation of a DPA at millimetre wave on GaN-Si technology.
SADI: A Novel Model to Study the Propagation of Social Worms in Hierarchical Networks

As more and more people rely on social networks for business and life, social worms constitute one of the major security threats to our society. Modern social worms exhibit two new features, message notification and the temporal characteristic of human mobility. Message notification indicates a user will get a reminder once a new message comes to a social account. The temporal characteristic of human mobility indicates a user can operate corresponding computer in different locations with different resting time. Previous scholars have proposed some analytical models for the propagation dynamics of social worms. However, they did not consider the above two features and there is one critical problem unrealized, which is structural imperfection of network topology. Previous models have not taken into account the hierarchical topology structure, which results from a many-to-many relationship between users and hosts. To address these problems, we model propagation dynamics of social worms oriented hierarchical networks in this paper, and the proposed model accurately describes the propagation behavior of social worms. We conduct both a theoretical analyses and extensive simulations to show our model can overcome inaccuracy in the number of infected nodes and provide a stronger approximation for the worm propagation. The results show that our model presented in this paper achieves a greater accuracy in characterizing the propagation of modern social worms.
Design and Deployment of an Open Management and Orchestration Platform for Multi-Site NFV Experimentation

Management and orchestration of virtual resources and functions, commonly referred to as MANO, are key functionalities of NFV environments. This article describes the design and deployment of the NFV MANO platform of 5TONIC, the open research and innovation laboratory on 5G technologies founded by Telefonica and IMDEA Networks. This NFV MANO platform provides 5TONIC trials and experiments with access to a functional production-like NFV environment, enabling experimentation with novel NFV products and services. As a relevant feature, the platform is capable of incorporating external sites to complement the portfolio of software and hardware resources that can be made available for experimentation activities. The 5TONIC MANO platform has been designed and built using open source technologies. The research carried out during its design and deployment has resulted in a contribution already made to its upstream projects regarding the automated configuration of virtualized network functions. Finally, we explore the scalability properties of the 5TONIC MANO platform, and we experimentally validate its functional capacity to orchestrate multi-site experiments.
Deep Reinforcement Learning Apply in Electromyography Data Classification

In this paper, we try a novel approach to detect human movement intentions based on electromyography(EMG) signals. We use the Convolutional neural network(CNN) to extract EMG features automatically, then use dueling deep Q-learning, a reinforcement learning technique, to learn a classification policy which with an ability to select most helpful subset features and filter the irrelevant or redundant features from the deep learning features. We show that the deep learning method outperforms the multi-layer perceptron in the several subjects EMG data classification situation and the reinforcement networks can use less features to reach a relatively high classification precision.
Multiagent Based System for Secondary Education Using Machine Learning

Artificial Intelligence has genuine imperative influence in our daily life rendering to the problem resolving. Many approaches were presented recently for developing TCBR applications. A classical and fundamental application is QA system with the assistance of textual cases, expertise acquaintance accumulation in the library full of past cases explanations and with the help of multi-agent. This challenge essentially needs an intelligent approach to overcome this problem. Many QA systems have been developed for English and other well-known languages however, no significant work was found for Urdu language. Despite spoken by millions of people, Urdu is an under-resourced language in terms of available computational resources. It inherits a lot of vocabulary from Arabic, Persian and the native languages of South Asia. Due to this influence, it has a complex morphology. In the proposed system, we meet the challenges in this domain by the assist of Question Answer system and utility assembly of Urdu language processing. Moreover, the solution shows the working of the knowledge system and deliberates good results.
Adaptive Observer for Motorcycle State Estimation and Tire Cornering Stiffness Identification

In this paper, a linear parameter varying (LPV) adaptive observer is designed for state estimation and tire cornering stiffness identification based on lateral motorcycle model. The estimation is based on a general Lipstchitz condition, Lyapunov function and is subjected to persistency of excitation conditions. Further, the LPV observer is transformed into Takagi-Sugeno (T-S) fuzzy observer and sufficient conditions, for the existence of the estimator, are given in terms of linear matrix inequalities (LMIs). This method is designed assuming that some of the states are not available, since parametric identification is generally developed assuming that all the system states are available (measured or estimated). Finally, the effectiveness of the proposed estimation method is illustrated through test scenarios performed with the well-known motorcycle simulator “BikeSim” and by field test using data measurement carried out on experimental motorcycle.
Deep MIMO Detection Based on Belief Propagation

The belief propagation (BP) algorithm exhibits outstanding detection performance for the multiple-input multiple-output (MIMO) transmission. However, this algorithm may fail to converge because of the fully connected factor graph under the MIMO settings. To address this issue, a novel deep learning detector based on the BP algorithm (DLBP detector) is proposed, which combines the BP algorithm with the deep learning methods. The log likelihood ratios (LLR) messages are passed on the MIMO factor graph to detect the signals from the transmitters. In addition, the weights are trained via the deep learning methods and further assigned to the messages updated in the DLBP detector. Finally, simulations show that, compared with the BP detector and the damped BP detector, the DLBP detector has lower complexity and better bit error rate(BER) performance.
A Real-Time Identification and Tracking Method for the Musculoskeletal Model of Human Arm

This paper aims at the development of a unified method for online identification and tracking of a kinematic musculoskeletal model of human arm to pave the way for related realtime applications, such as human-robot interaction, teleoperation and biomedical analysis. In order to decouple the identification of the joint angles of human arm kinematic model from a variety of motion capture (MoCap) setups, a generalized human arm triangle, which can be easily calculated by raw motion data, is introduced as an intermediate unified expression interface of human arm posture. An analytical solution to the Inverse Kinematics (IK) problem from the proposed human arm triangle to the joint angles of a commonly used OpenSim human right arm model is derived in detail. Once the human arm kinematic model is reconstructed, the involved muscles can be located correspondingly for related applications. Comparative simulation and experiment are conducted to validate the performance of the proposed IK and the whole tracking method. The results manifest that the calculation efficiency of the proposed IK can achieve an enormous speedup of 400-600 times with respect to the OpenSim built-in IK while maintaining comparable accuracy. Therefore, the proposed method can be an important tool to enable many online applications using human arm musculoskeletal model.
On Path Following Control of Port-Hamiltonian Systems by Bayesian Inference with Training Trajectory Data

This paper describes a procedure to design a path following controller of port-Hamiltonian systems based on training trajectory data. In order to calculate the reasonable design parameters for path following controller from the training data, Bayesian inference is adopted in this paper. By using Bayesian inference, not only the mean value of the trajectory but also the covariance matrix is acquired. By incorporating the covariance information into the control system design, it is expected to create a potential function that takes into account uncertainty at each position on the trajectory.
Mining Spatio-Temporal Semantic Trajectory for Groups Identification

Group identification refers to discovering groups with similar behaviors or preferences. The daily trajectories record the activities of moving objects, which reflect their behaviors. These mobile data provide us with a new data analysis approach for groups identification. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit behaviors patterns. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient two-phase discovering stay regions method (TPD) from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels based on POI information and LDA topic model. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on geographic and semantic similarity factor. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.
Color Quantization in Transfer Learning and Noisy Scenarios: An Empirical Analysis Using Convolutional Networks

Transfer learning is seen as one of the most promising areas of machine learning. Lately, features from pre-trained models have been used to achieve state-of-the-art results in several machine vision problems. Those models are usually employed when the problem of interest does not have enough supervised examples to support the network training from scratch. Most applications use networks pre-trained on noise-free RGB image datasets, what is observed even when the target domain counts on grayscale images or when data is degraded by noise. In this paper, we evaluate the use of Convolutional Neural Networks (CNNs) on such transfer learning scenarios and the impact of using RGB trained networks on grayscale image tasks. Our results confirm that the use of networks trained using colored images on grayscale tasks hinders the overall performance when compared to a similar network trained on a quantized version of the original dataset. Results also show that higher quantization levels (resulting in less colors) increase the robustness of CNN features in the presence of noise.
Approximate Computing Methods for Embedded Machine Learning

Embedding Machine Learning enables integrating intelligence in recent application domains such as Internet of Things, portable healthcare systems, and wearable devices. This paper presents an assessment of approximate computing methods at algorithmic, architecture, and circuit levels and draws perspectives for further developments and applications. The main goal is to investigate how approximate computing may reduce the complexity and enable the feasibility of embedded Machine Learning (ML) systems. Though ML is a powerful paradigm for applications in the perceptual domain (i.e. vision, touch, hearing, etc.), their computational complexity is very high and consequently real time operation and ultra-low power are still very challenging objectives. On the other hand, approximate computing has emerged as an effective solution to reduce hardware complexity, time latency and to increase energy efficiency.
Adaptive Patches for Mesh Denoising

The generation of triangular meshes typically introduces undesired noise which comes from different sources. Mesh denoising is a geometry processing task to remove this kind of distortion. To preserve the geometric fidelity of the desired mesh, a mesh denoising algorithm must maintain the object details while removing artificial high-frequencies from the surface. In this work, we propose a two-step algorithm which uses adaptive patches and bilateral filtering to denoise the normal vector field, and then update vertex positions fitting the faces to the denoised normals. The computation of the adaptive patches is our main contribution. We formulate this computation as local quadratic optimization problems that can be controlled by a set of parameters to obtain the desired behavior. We compared our proposal with several algorithms proposed in the literature using synthetic and real data. Our algorithm yields better results in general and is based on a formal mathematical formulation.
Analysis and Characterization of Ultra Low Power Branch Predictors

Branch predictors are widely used to boost the performance of microprocessors. However, this comes at the expense of power because accurate branch prediction requires simultaneous access to several large tables on every fetch. Consumed power can be drastically reduced by operating the predictor under sub-nomimal voltage levels (undervolting) using a separate voltage domain. Faulty behavior resulting from undervolting the predictor arrays impacts performance due to additional mispredictions but does not compromise system reliability or functional correctness. In this work, we explore how two well established branch predictors (Tournament and L-Tage) behave when aggressively undervolted below minimum fault-free supply voltage (Vmin). Our results based on fault injection and performance simulations show that both predictors significantly reduce their power consumption by more than 63% and can deliver a peak 6.4% energy savings in the overall system, without observable performance degradation. However, energy consumption can increase for both predictors due to extra mispredictions, if undervolting becomes too aggressive.
A Top-Down Approach to Teaching Web Development in the Cloud

The importance of web development education is increasingly gaining attention. However, the teaching of web development has a relatively undeveloped pedagogy. In this paper we propose a top-down approach to teaching app development. The proposed approach comprises three phases: preparation phase, web technology tutorial phase, and capstone project phase. The preparation phase requires instructor to select a sample web application, to extract the technological components of this application, and to select core technical topics for the tutorials. The tutorial phase consists of a series of lectures and hands-on tasks that help students learn web technologies used for implementing the sample application, with a focus on how disparate technologies were integrated and related to each other. The capstone project phase requires instructor to group students into teams to implement their own web applications. We present our web development course to exemplify the implementation of the proposed approach, which provides rich implications to both practitioners and researchers of web development education. We also discuss the strengths and limitations of the proposed approach within the landscape of previous studies and learning theories.
Online Newton Step for Portfolio Selection with Side Information

Online portfolio selection, as a research hotspot in financial signal processing, has been widely studied with machine learning perspective in recent years. Online Newton Step (ONS), which generates portfolio with low-complexity online convex optimization and achieves the same asymptotic wealth as the Best Constant-Rebalanced Portfolio in hindsight, is one promising algorithm among various portfolio selection strategies. However, ONS does not consider the downside risk, which leads to large investment loss in some market environments. To overcome this limitation, this paper proposes a novel portfolio selection method, namely ONS with Side Information (ONS-SI), which incorporates ONS with the side information derived from the market, to reduce the investment risk. The performance of ONS-SI is evaluated on the Chinese A-share market. Experiment results show that the proposed ONS-SI achieves higher wealth and lower downside risk than ONS.
Segmenting Retail Customers with an Enhanced RFM and a Hybrid Regression/Clustering Method

Targeted marketing strategies use market segmentation for insight into the heterogeneity of the customer purchase lifecycle, but this often ignores the evolution of customer behavior over time causing retailers focus on unprofitable customers. By pairing the Recency, Frequency, Monetary scores with the Customer Lifetime Value model to segment customers of a medium-sized clothing and fashion accessory retailer in Kuwait. A modified regression algorithm investigates the customer purchase curve gaining knowledge from point-of-sales data to help the retailer make informed decisions. Clustering is by K-means and by Expectation Maximization. Cluster quality analysis reveals the former outperforms the latter to discover and suggest relevant market segments and appropriate marketing strategies.
Social Networks Analysis in a Business Intelligence Context

Social networks provide enormous amounts of information. The analysis of these complex networks becomes increasingly approaching by the recent emergence of large graph databases. These databases study the topology of large and complex networks and keep the interaction structure between their entities. Business Intelligence (BI) proposes new simple solutions that are intended to offer an extensive range of analysis capabilities. BI tools use relational databases to build big data platforms and integrate information solutions that lead up to the decision making. Analyzing social networks through Business Intelligence tools requires putting data into relational databases that may lead to particular loss of information. Hence, we propose, in this paper, a new approach that benefits from the BI analysis process conveniences to apply them on graph databases that stores social networks data.