Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentially Private Top- $k$ Frequent Columns Publication for High-Dimensional Data

Differential privacy (DP) is a promising scheme for releasing the results of statistical queries on sensitive data. This paper focuses on top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> frequent columns publication on sensitive data, with high result utility under differential privacy. Existing works directly select frequent columns from all columns (called one-phase scheme), which is far from ideal due to the large privacy consumption or misjudgments for columns with frequencies close to the frequency of the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>th frequent column (called near-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-fluctuation-column). This paper presents a new solution Two Phase Selection (TPS) to carefully choose frequent columns in two phases. The main idea is to classify columns into two distinct categories based on whether it is one near-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-fluctuation-column or not. Frequent columns are chosen from the two categories using different techniques, which is totally different from existing solutions without classifying. Furthermore, by analyzing the distribution of near-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-fluctuation-columns, we introduce a block-centric column-choosing method privacy-free-mechanism (PFM). By partitioning columns into blocks, PFM makes the privacy consumption proportional to the number of blocks, instead of frequent columns. Extensive experiments on real datasets show that our proposals outperform the state-of-the-art techniques for top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> column publication.
Restoration of the Network for Next Generation (5G) Optical Communication Network

In modern communication network unwanted failures occurred. A design of robust network is required to handle the failures, restore the failed part and maintain the traffic flow of the network within a permissible time frame, at the lowest cost. The next-generation (5G) communication and computing networks, software-defined networks and internet-of-things networks assures to provide high speeds, impressive data rates and remarkable reliability. We need to design complex and robust next generation networks that must be built with minimum failure possibilities, quick failure recovery capabilities and high network restoration capacity. Therefore, improved network restoration models are needs to be developed and incorporated. In this paper, a comprehensive study on network restoration mechanisms that are being developed for addressing network failures in current and next generation (5G) networks is carried out. Open-ended problems are identified, while invaluable ideas for better adaptation of network restoration to evolving 5G communication and computing models are discussed.
A Novel Approach to Routing Technique for Mobile Sink in Wireless Sensor Network to Enhance the Network Lifetime

The increasing complexity of Wireless Sensor Networks (WSNs) demand efficient techniques to fulfill the strict constraints imposed by the sensor nodes to achieve system performance. Interconnection between sensor nodes via wireless relays is another important requirement since one hop communication from source to base station may not be feasible due to two reasons; the distance between the sensor node and sink and poor communication link. While computing the path during data transmission, these sensor nodes consume major portions of energy. To conquer these issues, suitable routing techniques must be designed to intensify the network lifetime. Many researchers have discussed these issues in the current literature. LEACH and Power-Efficient GAthering in Sensor Information Systems (PEGASIS) is proven to be most efficient routing protocol provides a basis on top of which many routing protocols are proposed. This paper presents a chain-based routing protocol where the chain formation algorithm considers both distance and energy to select the neighbor node for formation of a chain. Further, sink mobility makes the protocol attractive and extends the wider applicability. Simulation results obtained from the protocol reveals that the superiority of our proposed system over the existing approaches in the literature.
Smart Transducers in Distributed and Model-Driven Control Applications: Empowering Seamless Internet of Things Integration

Smart transducers based on ISO/IEC/IEEE 21450 provide standardized access to sensors and actuators. Hence, no centralized database is needed for the calibration and interpretation of the data collected by transducers. The smart transducer stores and maintains its individual transducer electronic data sheet (TEDS). Whenever transducers are replaced in an automation system, the correct description, calibration, and data stream interpretation come along with the newly connected transducer. The domain-specific modeling language defined by IEC 61499 improves the software development process for automation systems. However, it lacks a clear transducer interaction model.
A Randomized Algorithm for Joint Power and Channel Allocation in 5G D2D Communication

We formulate the joint power and channel allocation problem (JPCAP) for device to device (D2D) communication as a cost minimization problem, where cost is defined as a linear combination of the number of channels used and total power requirement. We first show that JPCAP is NP-hard and then propose a greedy channel and power allocation (GCPA) algorithm to assign channels and powers to the links. We design GCPA in such a fashion that there exists an order of the links for which it produces optimum solution. Finally using GCPA we develop a randomized algorithm (RA) that increase the optimum hitting probability by an exponential factor of total number of links. Through simulation, we evaluate the performance of RA and show that RA outperforms an existing approach.
Sentiment Analysis of Bengali Texts on Online Restaurant Reviews Using Multinomial Naïve Bayes

Recently, determining the customer impression is considered one of the prominent factors on the success of the restaurant businesses. Due to the rapid growth of digital contents related to restaurant or foods in the web, people are more inclined on reviews before going to any restaurant so the significance of customer review is inevitable. In order to selects a restaurant customer needs to check thousands of feedback’s to understand the restaurant quality or services. Therefore, classification of a significant amount of reviews into a sentimental category is required to attain meaningful insights so that the customer can choose restaurants based on their preferences. This classification can be done by sentiment analysis. This paper proposes a system that can classify customer reviews into positive and negative classes based on their sentimental feedback. We have tested the proposed system with 1000 restaurant reviews text written in Bengali. The experimental result shows that the proposed the system can classify restaurant reviews with 80.48% accuracy using multinomial Naïve Bayes.
CAnDoR: Consistency Aware Dynamic data Replication

Data management has become crucial. More and more distributed data management solutions arise, e.g. Casandra or Cosmos DB. Some of them propose multiple consistency protocols. Thus, for each piece of data, the developer can choose a consistency protocol adapted to his needs. In this paper we explain why taking the consistency protocol into account is important while replicating data; and we propose CAnDoR, an approach that dynamically adapts the replication according to the data usage (read/write frequencies and locations) and the consistency protocol used to manage the piece of data. Our simulations show that using CAnDoR to place and move data copies can improve the global average access latency by up to 40%.
Design of Automated Control System Supporting Temperature Field in

In the conditions of industrial automation, more and more importance is given to robotic control systems. Such systems operate in automatic mode and perform the functions of maintaining a predetermined pressure, temperature, humidity, etc. Many industrial machines use pulsed elements as control actions. Such elements in mathematics are usually expressed as a Green function. Such machines have a point effect. But the problem arises of finding the optimal location of the heating elements. This article discusses the issue of finding the place and time of switching on sectional heaters, while stabilizing the temperature field based on the Green function. Mathematical models are presented and the temperature field is studied for a three-dimensional control object. The materials of the article underwent the procedure of introduction to industrial enterprises All received software systems have patents registered in the manner prescribed by law.
Deep Belief Neural Network Model for Prediction of Diabetes Mellitus

Diabetes Mellitus is metabolic chronic disease in which blood glucose levels are too high. In India nearly 8.7% of population suffers from diabetes in age range from 20 to 70. Unidentified and untreated diabetes leads to so many health difficulties such as damage of heart, kidneys, eyes, nerves and blood vessels. There are already several methods exists to support clinical decision making but still need improvements to solve the issues and challenges. In this research work, deep belief network model is designed for providing computational intelligence for prediction of patient affected by diabetes mellitus with maximum accuracy. Pima Indians Diabetes Dataset is used to analyze and experiment this prediction model. Firstly, the dataset is pre-processed by applying normalization technique. Secondly, the prediction model using deep belief neural network is designed. At the end, an experimental results proved that the comparison of overall performance of deep belief networks method is better than familiar classifiers namely naïve Bayes, Decision Tree. , Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM).
Spam Review Detection Using Deep Learning

A robust and reliable system of detecting spam reviews is a crying need in todays world in order to purchase products without being cheated from online sites. In many online sites, there are options for posting reviews, and thus creating scopes for fake paid reviews or untruthful reviews. These concocted reviews can mislead the general public and put them in a perplexity whether to believe the review or not. Prominent machine learning techniques have been introduced to solve the problem of spam review detection. The majority of current research has concentrated on supervised learning methods, which require labeled data - an inadequacy when it comes to online review. Our focus in this article is to detect any deceptive text reviews. In order to achieve that we have worked with both labeled and unlabeled data and proposed deep learning methods for spam review detection which includes Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN) and a variant of Recurrent Neural Network (RNN) that is Long Short-Term Memory (LSTM). We have also applied some traditional machine learning classifiers such as Nave Bayes (NB), K Nearest Neighbor (KNN) and Support Vector Machine (SVM) to detect spam reviews and finally, we have shown the performance comparison for both traditional and deep learning classifiers.
Visualization of Symmetries in Fourth-Order Stiffness Tensors

Many materials like wood, biological tissue, composites or rock have anisotropic mechanical properties. They become increasingly important in modern material, earth, and life sciences. The stress-strain response of such materials can be characterized (to first-order) by the three-dimensional fourth-order stiffness tensor. There are different anisotropy classes, i.e. material symmetries, that differ in the number and orientation of symmetry planes characteristic of the material. A three-dimensional fourth-order stiffness tensor of a hyperelastic material has up to 21 independent coefficients representing both moduli and orientation information which challenges any visualization method. Therefore, we use a fourth-order tensor decomposition to compute the anisotropy classes and the position of the corresponding symmetry planes. To facilitate judgment of the significance of the amount of anisotropy, we construct an isotropic material. Based on these computations, we design a glyph that represents the stiffness tensor. We demonstrate our method in a finite deformation setting of an initially isotropic hyperelastic material of Ogden class which is often modeling biological tissue. Upon deformation, the stiffness tensor can evolve along with its symmetry creating an inhomogeneous, unsteady fourth-order tensor field in three dimensions.
Early Intrusion Detection for OS Scan Attacks

Network Intrusion Detection Systems (NIDS) are concerned with the discovery of unauthorized accesses to computer networks by analyzing the traffic in order to detect malicious activity. In the event of an intrusion, the time elapsed until the detection is a key factor to break the Cyber Kill Chain. State-of-the-art studies use a traditional evaluation based on standard accuracy metrics (e.g. precision or F-measure) without taking into account the time required to detect a threat. In this paper, we formally define the early intrusion detection problem. We perform a thorough evaluation adapting existing time-aware metrics to the early detection of threats on a computer network and we also propose a new metric (i.e. NormERDE). Our results show how a good performance on standard metrics may not correspond to good results on early detection metrics. For instance, a technique with a high level of precision could need too much time to detect a threat. Therefore, in this paper we propose taking into account time-aware metrics in NIDS evaluations due to the importance of this factor in a real world environment.
Novel Printed Leaky-Wave Antenna with Suppressed OSB for Broad Angle Scanning

This article presents a novel printed leaky-wave antenna used for broad angle scanning. This antenna can scan continuously from backward to forward through broadside. The unit cell is realized by placing two stubs at the same side of a microstrip line. The stubs have been used for reflection cancellation to remove open-stopband (OSB). An array of 11 such elements are made. The proposed leaky wave antenna is operated in the frequency range 8.5 GHz to 14 GHz with scanning angle from -32° through broadside to +20°. The proposed antenna provides almost consistent gain of 15 dBi in the whole operating band.
A Fuzzy-COPRAS Model for Analysis of Renewable Energy Sources in West Bengal, India

With an enhanced progressive outlook towards development of a sustainable environment in the society, there has been a number of pollution free, environment-friendly, alternate renewable energy sources that have sprung up, the most common ones being Solar Energy Systems, Hydel Energy Systems, Wind Energy Systems, Biomass Energy Systems, Geothermal Energy Systems and Tidal Energy Systems. The increasing choice of alternate energy sources gives the investors a larger scope to channelize their investments at the right energy system for suitable location. The preferential choice of a renewable energy source for a location is dependent on a number of technical, economic, social and environmental factors associated with that energy supply system. A decisive action for the correct investment would require an intricate study of the energy systems based on those factors. This study proposes an integrated Fuzzy Complex Proportional Assessment (COPRAS) model, a hybrid Multi-Criteria Decision Making (MCDM) analogy to analyse the aforementioned six renewable energy sources in the state of West Bengal, India based on eight multi-dimensional criteria. The aim is to arrange the energy systems in the hierarchy of their preferential establishment to aid the government or any investor taking interest in a project of similar nature. The proposed model in this study can also be applied to any problem revolving around MCDM procedures in the same genre.
A Super Wideband Circular Monopole Antenna

A super wideband circular monopole antenna with microstrip feedline and half-elliptical ground plane with a notch is proposed. Bandwidth not less than 18.3 GHz from 1.7 GHz to beyond 20 GHz and a bandwidth ratio more than 11.8:1 for VSWR less than 2 are observed. The proposed radiator provides a peak gain of 7.24 dB at 18.25 GHz. Nearly omni-directional far field radiation patterns in H-plane and a dipole like radiation patterns in E-plane at lower frequencies are observed. Due to its simple and compact structure, it is useful for different wireless applications.
Multilayer Backsheet Characterization Using Diffusion Experiments and Optimization Method for Water Diffusion Simulation Inside the Photovoltaic Module

Photovoltaic (PV) modules are expected to be durable against exposure to outdoor environments for prolonged time durations. One of the primary causes for the loss of durability is water exposure. Water concentration prediction within the PV module is challenging under the varying environmental conditions as well as the multilayer nature of the backsheet. Therefore, there is a need for systematic multilayer water diffusion characterization of backsheet as well as to incorporate the varying environments in the water diffusion simulations of the PV module. In this article, backsheet is considered as a two-layer laminate whose water diffusion parameters were determined using both water diffusion experiments (without separation of each layer) and an optimization technique. The gravimetric technique was used to measure experimental water mass uptake of the backsheet, whereas Fick's second law based diffusion model was used to predict numerical water mass uptake. An optimization technique was used to determine the water diffusion and solubility coefficients that minimized the difference between the numerical and experimental water mass uptake. The optimized water diffusion and solubility coefficients in the test range of 24–50 °C reasonably fitted the Arrhenius rate equations. Backsheet was also considered as a homogeneous configuration that was used to determine the effective water diffusion parameters. These effective parameters were found to be significantly different from the optimized parameters of the two-layer backsheet model. Water diffusion simulations with a commercial PV module exposed to outdoor environment showed significant differences in the water concentration between the two-layer and homogeneous backsheet models.
On Reducing Effect of Acoustic Feedback in Hearing Aids By Employing Lattice Prediction and Hybrid Adaptive Filtering

This paper investigates an efficient method for acoustic feedback cancellation (AFC) in digital hearing aids. The conventional approach based on the normalized least mean square (NLMS) algorithm suffers from biased and slow convergence. The prediction error method (PEM)-based AFC (PEM-AFC) significantly improves upon the biased convergence issue, however, the convergence speed may still be slow. The main structure of the proposed method is based on a pervious work and comprises two adaptive filters. The hearing aid receiver signal is used as an input to the first adaptive AFC filter, whereas the second AFC filter is excited by a gain-controlled (internally generated) probe signal. The key proposal is to use a lattice filtering-based pre-processing in the update equation of the first AFC filter. This results in a Newton-like adaptive algorithm with an improved convergence speed. Furthermore, the lattice filtering is performed on sample-by-sample basis in contrast to the frame-based processing in the PEM-AFC. The level of the probe signal exciting the second AFC filter is reduced as AFC system converges. This makes the second adaptive filter being sluggish particulary at the steady-state. In order to improve the convergence speed, a maximum Versoria-criterion (MVC)-based gradient information is incorporated into the update algorithm of the second AFC filter. As in the previous method, the coefficients between the two adaptive filters are mutually transferred to ensure that both adaptive filters estimate the acoustic feedback path. Simulations demonstrate the effective performance of the proposed approach for a variety of speech signals with different gain settings of hearing aids.
Analytical Method of 2.5d Exterior Sound Field Synthesis By Using Multipole Loudspeaker Array

We propose an analytical method of 2.5-dimensional exterior sound field reproduction by using a multipole loudspeaker array. The method reproduces the sound field modeled by expansion coefficients of spherical harmonics based on multipole superposition. We also present an analytical method for converting the expansion coefficients of spherical harmonics to weighting coefficients for multipole superposition. In contrast to pressure-matching methods, which tend to be ill-conditioned problems, the proposed method gives stable solutions based on an analytical conversion from the expansion coefficients of spherical harmonics. We derive the analytical method by converting the sound field modeled by expansion coefficients of spherical harmonics to a linear combination of ’basic multipoles’ for obtaining the weighting coefficients of each multipole. Computer simulation results indicate that the proposed method reproduces the sound field more accurately than existing pressure-matching based method.
A Surface-Potential Model for N-Polar GaN/AlN/AlGaN MIS-HEMTs

In this paper, we introduce an analytical surface potential based model for sheet carrier density and current in N-polar GaN/AlN/AlGaN metal insulator semiconductor high electron mobility transistors (MIS-HEMTs). The proposed sheet carrier density model is derived in terms of Fermi-level $(E_{f})$, gate potential $(V_{g})$ and channel potential $(V_{c})$. The surface potential current model using charge control equations and high field saturation Canali mobility model are in close agreement with simulation and experimental data over a wide range of applied gate biases.
The Architecture of Kaligreen V2: A Middleware Aware of Hardware Opportunities to Save Energy

Nowadays, energy saving in the use of information technologies is a very important issue both from the economic and sustainability point of view. Many scientists investigate methods to save energy at different application levels (cloud: i.e., architectures, grid: i.e., middlewares and frameworks and hardware management: i.e., operating systems) and many of them agree on the strategy of executing programs, processes or virtual machines only using the time and resources that are strictly necessary. For this, it is necessary to plan strategies for deployment and relocation of processes; but always taking into account hardware repercussions and the knowledge of the architecture and applications behavior. On the other hand, it has already been demonstrated that the use of microservices brings numerous advantages in availability and efficiency; but we do not find many jobs that exploit this technique on the energy level. In this article, we present the architecture of a middleware for distributed microservices-based applications, which allows any negotiation-based scheduling algorithm to duplicate or move microservices from one device to another in a non-centralized way for energy savings, taking into account the consumption characteristics of the microservices and the capabilities that the hardware components offer.