Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Membership and Non-membership Features on Classification Decision: An Empirical Study for Appraisal of Feature Selection Methods

In text categorization, the discriminative power of classifiers, dataset characteristics, and construction of the more representative feature set play an important role in classification decisions. Subsequently, in text categorization, filter based feature selection methods are used rather than wrapper and embedded methods. In terms of construction of an illustrative feature set, a number of global and local filter based feature selection methods are used with their respective pros and cons. The inclusion and exclusion of membership and non-membership features in a constructed feature set depends on the discriminative power of the feature selection method. Though, there are few studies which have reported the impact of non-membership features on the classification decision. However, to best of our knowledge, there is no detail study, which calibrates the effectiveness of the feature selection method in terms of inclusion of non-membership features to improve the classification decisions. Consequently, in this paper, we conduct an empirical study to investigate the effectiveness of four well-known filter based feature selection methods, namely IG, $\chi 2$, RF, and DF. Subsequently, we perform a case study in the context of classification of the Gang-of-Four software design patterns. The results show that the balance consideration of membership and non-membership features has a positive impact on the performance of the classifier and classification decision can be improved. It has also been concluded that random forest is best among existing methods in considering an equal number of membership and non-membership features and the classifiers show better performance with this method as compare to others.
LOW Power and Area Efficient Borrow Save adder Design

This paper quantitatively compares the impact disparity on the performance of the ripple carry adder(RCA) and borrow-save adder(BSA), measures the typical power reduction achieved by Borrow-save adder achieved at low voltages, at the rate of augmented delay variations, and propose a technique that enhances BSA tolerance to variations. And this paper introduces the concept called pipelining along with normal Borrow-Save addition operation to lowering the supply voltage in adders. Thus, the adaptation of pipelining will decreases the 60% of overall power dissipation in Borrow-Save adder compared to Ripple-Carry adder. And the delay occurred in the Borrow-Save adder is three times smaller than the Ripple Carry adder.
A Novel Position Measuring Method for Ros-Drill© Devices in Biomedical Applications

This paper presents the development of a novel position measuring method for rotational oscillating biomedical device Ros-Drill©. This micro driller device has a micro brushless motor that never takes a full turn, just rotationally oscillates. The measuring method basically depends on the principle that one phase of brushless motor is always non-fed and can be used to measure the back-EMF voltage whose value is proportional to motor speed. The measuring technique and control algorithm are implemented via a digital signal processor, dSPACE 1104. The experiments reveal that the method is suitable for biomedical applications needing piercing operations with rotational oscillations and it shows satisfactory performance in terms of wide range of operation frequency.
How Noise Radar Technology Brings Together Active Sensing and Modern Electronic Warfare Techniques in a Combined Sensor Concept

Research on modern EW algorithms follows a trend of increasing digital hardware implementations. The powerful features of these digital algorithms allow detection, location, identification and jamming of hostile radars. This paper presents a sensor concept in which mature EW features are expanded by an active sensing component using noise radar technology. It is shown that the flawless integration of noise radar into the EW functionality is accompanied with effective separation of all concurrent but different tasks of the combined sensor. Experimental results are presented which underline the basic idea of noise radar technology being the key enabler of this combined sensor concept.
Adapting an evolutionary algorithm with embedded simulation and pseudo-random number generation for the cell broadband engine

For the problem of optimizing inspection strategies in multi-stage production systems, a metaheuristic consisting of an evolutionary algorithm with embedded simulation was developed in Van Volsem et al. (2007), Van Volsem (2009) and Van Volsem (accepted for publication, 2009). The metaheuristic requires normally distributed pseudo-random numbers; the time needed for this random number generation is a substantial fraction of the total computation time. In an effort to reduce the computation time, the metaheuristic was adapted for computation on the Cell Broadband Engine. The proposed adaptation is twofold: we propose a way to make the metaheuristic suitable for fast multicore computation, and secondly, the potential of SIMD computation for speeding up the random number generation process and the metaheuristic is investigated.
Anomaly Detection for Autonomous Transfer Vehicles in Smart Factories

Autonomous robots are critical components of factories of futures. In this era, autonomous transfer vehicles are expected to play important role for flexible manufacturing. But the system should detect abnormal events itself. In this study, anomaly detection approach is proposed for autonomous transfer vehicles in the smart factories. Decision trees are used to detect stopping and slow down anomalies in internal transportation of the factories. The proposed approach is tested in simulation environment.
Resilience of Smart Power Grids to False Pricing Attacks in the Social Network

Communication through social media can help engage end users to improve the efficiency of demand-side management in smart power grids. However, this opens a channel between the social network and the power grid through which malicious attackers can publish false information that can actually cause problems to the power grid. In this paper, we analyze this new problem by modeling a social network-coupled smart grid and investigating its vulnerability to false pricing attacks in the social network. The energy consumption profile based on social information is modeled as a consumption rescheduling problem, which aims to maximize the benefit of demand-side management. The false price spreading process is described by a multi-level influence propagation model, which takes into account the personalities of the end users. Different attack strategies are considered and the power operator’s response is modeled. The residual ampacity of distribution lines and the expected energy not supplied are adopted to quantify the impacts of the attacks on the power system. To account for the stochastic characteristics of the influence propagation process, Monte Carlo simulation is utilized. The proposed modeling and analysis framework is applied on a modified IEEE 13 nodes test feeder and a notional social network. The vulnerability to attack is analyzed at both component and system levels.
Demo: Blockchain for the Simplification and Automation of KYC Result Sharing

Know Your Customer (KYC) processes performed by banks on their customers are redundant, cumbersome and costly. Therefore, a system is proposed to automate menial tasks and allow sharing of data related to KYC. A blockchain dictates the collaboration between different participants and several services are built around it to support the functionality of the system as a whole. An access control system is used to share data legitimately.
An Architecture to Support Interoperability in IoT Devices

The field of Internet of things has seen a rapid growth in the recent years and it is expected to keep growing as such in the coming years. In order to take advantage of this and to have their share of profit from the market the billion-dollar companies, as well as start-up manufacturers, are competing to make the best possible devices for various tasks as per demand in different IoT domains, hence a new kind of challenge arose i.e. interoperability. Since each manufacturer uses a different combination of technologies in its product, so there is no guarantee that every device in the market would be compatible with every other device, straight out of the box. Due to the heterogeneity of devices in hardware and software, the problem of interoperability has to be taken care of multiple layers of operation. A system is required which eliminates so-called, "closed" or "vertical" silos of information which are followed by each manufacturer only then we can obtain the true value of IoT residing in the data being created and transmitted when the devices interact with each other.
Personalized Check-in Prediction Model Based on User’s Dissimilarity and Regression

To solve the problem that the user check-in prediction model is difficult to provide personalized check-in services, this paper proposes a novel hybrid model, called personalized check-in prediction model based on user’s dissimilarity and regression (UDR). The UDR is mainly composed of two sub-models: user’s regression location prediction model (UR) and user’s dissimilarity location prediction model (UD). In UR, considering the personalization of user check-ins, we propose a hybrid weighted Markov model, which combines the whole check-in data and individual check-ins. Different from other methods, for the prediction of individual check-ins, we not only consider the preference of individual users, but also the influence of friend relationships. Meanwhile, the Hidden Markov model(HMM) is used to determine users’ next check-in location by using time series feature (week-hour) and location sequence. In addition, by improving the kernel density estimation, we propose a multi-level hybrid kernel density estimation model, which is built based on the individual, city and region layers, and smoothes the over-fitting phenomenon caused by few check-ins. In UD, we take into account the weather factors that most existing methods did not consider. By defining the “cold and hot spot transference” and weather similarity features, we explore the influence of weather on user’s check-ins and also propose a method used to calculate the similarity between user check-in weather preferences and location weathers. At the same time, the influence of social, time, and space factors are also considered. The experiments on two LBSN datasets demonstrate that the performance of UDR is superior to the state-of-the-art check-in prediction methods.
Qualitative analysis of financial statements for fraud detection

Fraudulent financial reporting has become a global phenomenon which is adversely affecting the economic and social growth of an organization and the nation they belong to. The financial statements published periodically by the companies contain quantitative and qualitative information. The quantitative information includes numbers, metrices, ratios etc. and qualitative information comprises of comments/notes given by the auditors, disclosures by the management in textual format. A number of analytical models have already been applied for finding the possible solution for financial statement fraud by using quantitative information. A little or no work has been done for detecting fraud by analyzing the qualitative information present in financial statements. In this paper, we propose a text mining framework for detecting financial statement fraud by identifying and analyzing the linguistic data available in the financial reports.
Wavelet-Based Facial recognition

To recognize and automatically identify the identities of individuals, there are several biometric identification systems based on physiological and behavioral characteristics, in our work we are interested in face recognition, which is a recent biometric authentication technology. This technology offers a reasonable level of precision. In this paper, we propose a method of biometric recognition of a person by their face using the wavelet transform. For our application, we have opted for different types of wavelets in order to decompose the region of interest. The evaluation and judgment of each type in relation to the other is given by the calculation of the parameters Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The experimental results were performed using the FEI database.
A Preliminary Study for Remote Healthcare System: Activity Classification for Elder People with on Body Sensors

Development of intelligent care system for elder people have been investigated in recent years. In this study, to detect emergency situations for elder people, activity classification was aimed using on body sensor data. Multi-layer perceptron, radial basis function networks, k- nearest neighbor and support vector machines were used in classification. In feature selection process principal component analysis and ReliefF were used. Accuracy of classification was above 85% for every classifier and the best performance was acquired with 3-NN with 99.8% accuracy. When feature selection was applied 5- NN was showed the highest performance with 99.4%. This study shows that it is possible to develop remote care system by using sensors and classifiers for a more secure life for elder people.
A Privacy Framework in Cloud Computing for Healthcare Data

In recent years, the cloud computing technology improved the healthcare system. It includes on-demand services like storage, computing power, servers, networking, applications and many other IT resources over the internet. With the advancement of healthcare technology, the transformation from offline computing to online computing uprises many security and privacy issues such as authentication, authorization, inference control, access control, data confidentiality, abuse use of cloud based healthcare data, and integrity. As a consequence, a cloud-based e-healthcare system requires a robust and effective security and privacy framework. So, in this paper, we proposed a privacy framework which maintains the data privacy and reliability. It also ensures that only authorized party can access their corresponding healthcare data. Moreover, it overcomes the privacy issues as well as maintains a secure healthcare system. The proposed privacy framework includes a data perturbation technique, which protects the sensitive data from inference attacks. Additionally, it also includes some rule set for data view control which protects the data from impersonation attack. The detail discussion and analysis of the privacy framework shows that it is secure and efficient for healthcare data stored in the cloud.
Performance Analysis of Snort using Network Function Virtualization

Snort is an intrusion detection system which operates together with a firewall and is an important part of the network security systems. Snort performance decreases as the traffic on the network increases. To handle packet loss, some load balancing techniques are used. In this paper, we implement Snort a network function virtualization (NFV). The objective is to reduce the packet loss in case of higher traffic flows. In our research we have implemented snort as Virtual Network Function (VNF). The proposed research is implemented, and different experiments are carried out. The results show that packet loss can be controlled by implementing Snort as VNF.
Collaborative supporting tool: Integrating social media and mobile groupware into an integrated learning environment

Online collaboration is currently emerging as one of the predominant technology trends in the collaborative learning atmosphere. However, the scarcity of collaborative devices combined with task-specific groupware tools may limit its full potential. In addition, there is a deficiency of investigations in the form of integrated settings between social media and mobile groupware. Therefore, this paper proposes the approach of mobile groupware (MobileMeeting) that can be integrated (i.e., embedded) into the popular social media posts that students currently use as a supplementary tool for collaborative learning. An investigation was conducted in real project-based scenarios with students, followed by post-session questionnaires including a log-report analysis from Google Analytics as supporting evidence. This study confirms that the proposed approach using MobileMeeting is capable of improving collective learning productivity and activities, including their satisfaction with the required groupware tools. The log-report analysis of students' case projects (social media marketing campaigns) also displays an increment in user engagement and retention by bringing the new user and a large number of returning user. In conclusion, these results have several practical implications: as collaborative learning activities become more complex, the setup of integrated social media and mobile groupware (MobileMeeting) can support a range of collective actions.
Day Ahead Forecasting and Peak Load Management using Multivariate Auto Regression Technique

One of the most important commodity of today’s world is energy. Energy utilization depends upon several factors such as variation in temperature, seasons, humidity, day of the week, special holidays etc. and the most efficient utilization of energy requires delivery of uninterrupted good quality power. Tariff plays a vital role here. Tariff can be used to create favorable electricity trading conditions in peak demand hours. Therefore, we must have a judicious and proper planning for daily load profile forecasting and in specific peak load demand forecasting. There are numerous methods of Load Forecasting and in specific peak load forecasting. In recent days, Auto Regression technique (ARIMAX) has proved itself a successful tool for future data prediction. In order to find the best application of ARIMAX to get optimum results in peak load forecasting and load management a case study has been done for an urban city in this paper.
Improved Fuzzy C-means and K-means Algorithms for Texture and Boundary Segmentation

Image segmentation is one of the most significant and inevitable task in variety areas ranging from face/object/character recognition and medical imaging applications to robotic control and self-driving vehicular systems. Accuracy and processing time of image segmentation processes are also prominent parameters for quality of such computer vision systems. The proposed method incorporates three main pre-processing techniques such as Down Scaling/Sampling, Gamma Correction and Edge Preserving Smoothing so as to achieve accuracy and robustness of the segmentation. Pre-processing techniques are performed for both Fuzzy C-means (FCM) and K-means algorithm and all RGB information of image are taken into consideration while segmenting the image rather than using only gray scale. Performance analysis are performed on real-world images. Experiments show that, our method achieve higher accuracy levels and feasible processing time results compared to conventional FCM and K-means algorithms.
Sliding Mode Control of a Two-link Robot Manipulator Using Adams & Matlab Software

This paper presents the design of a sliding mode controller (SMC) for trajectory tracking problem for a two-link planar robot manipulator. A virtual prototype of the manipulator has been built by using Adams software. Also, the controller works is achieved in Matlab/Simulink software. The system is simulated in both Matlab and Adams software together which is called co-simulation. The manipulator system has two inputs (torques of actuators) and four outputs (angle of 1st joint, angle of 2nd joint and x-y components of end effector position). The sliding mode controller (SMC) is designed with the constant variation reaching law. The simulation results show that the sliding mode controller (SMC) can successfully achieve trajectory tracking of a two-link planar robot manipulator according to the desired trajectory.
PHSCR: Particle Swarm Optimization Based Handoff Spectrum Using Cognitive Radio

Fixed licensed spectrum bands are limited in number, so a new technique is needed that will use the unlicensed available spectrum bands. This technique is known as cognitive radio network (CRN). A secondary user tries to use the licensed band of frequency spectrum with no interference of primary users. Handoff is a mechanism in which secondary users empties the licensed band if primary user arrives. The frequent switching of channels diminishes the throughput. In this paper, a new technique based on Particle swarm optimization is proposed which minimizes the handoff delay as well as maximizes the throughput. The simulation results prove that the proposed protocol performs better.
Forecasting Dengue Fever using Classification Techniques in Data Mining

Dengue is a mosquito borne endemic disease threatening the world now-a-days. If not treated on proper time, it will lead to death. World Health Organization (WHO) reported, dengue is prevalent in more than 80 countries all over the world. Globally, 50–100 million cases are affected with dengue every year. In order to reduce the mortality rate, there is an urgent need for early diagnosis. This research aims to develop a prediction framework that supports early diagnosis of disease. It summarizes the hidden causes for infection at an early stage, using decision tree classifiers. Decision tree classification is done using clinical dataset from a hospital at south TamilNadu is used for data analysis. The proposed system resulted in prediction accuracy of 86.13%. Number of true positives and true negatives are high. The proposed model identified critical values for symptoms that lead to illness.
Autonomous Vehicle Control for Lane and Vehicle Tracking by Using Deep Learning via Vision

Camera-based lane detection and vehicle tracking algorithms are one of the keystones for many autonomous systems. The navigational process of those systems is mainly focused on the output of detection algorithms. However, detection algorithms for lane detection need more pre-processing time and computational effort. They are also affected by environmental conditions and must regularly be improved. In this paper machine learning techniques and computer vision algorithms are utilized for the tasks of the lane and vehicle tracking of an autonomous vehicle control scenario. With the nature of used learning algorithm, the proposed system can handle complex image problems. The vehicle, on which we implement our algorithms, can manage to carry out the following tasks autonomously; tracking the lanes, following another vehicle, and stopping in necessary conditions. For that, one of the primary purposes is image-based lane tracking methodology by using learning algorithms. Data augmentation is applied to create diversity for the dataset. Application in this methodology has been discussed. For lane tracking Convolutional Neural Network architecture which is based on NVIDIA’s PilotNet is preferred. For detecting objects and vehicles, the system is trained on the faster region-based convolutional neural network (Faster R-CNN) to identify traffic light and stop sign are by Haar Cascade Classifier. All these learning models are trained on NVIDIA GTX 1070 Graphics Processing Unit (GPU) to reduce training time. Experimental results showed that the proposed system gives a favorable result to autonomously control vehicles for lane and vehicle tracking purposes by vision.