Miracle-dz/UniFAR-FTUs huggingface dataset:
Progressive Top-k Subarray Query Processing in Array Databases

Unprecedented amounts of multidimensional array data are currently being generated in many fields. These multidimensional array data naturally and efficiently fit into the array data model, and many array management systems based on the array data model have appeared. Accordingly, the requirement for data exploration methods for large multidimensional array data has also increased. In this paper, we propose a method for efficient top- k subarray query processing in array databases, which is one of the most important query types for exploring multidimensional data. First, we define novel top- k query models for array databases: overlap-allowing and disjoint top- k subarray queries. Second, we propose a suite of top- k subarray query processing methods, called PPTS and extend them to distributed processing. Finally, we present the results of extensive experiments using real datasets from an array database, which show that our proposed methods outperform existing naïve methods.
Optimizing a Distributed Wind-Storage System Under Critical Uncertainties Using Benders Decomposition

A method for calculating the optimal size of an energy storage system (ESS) under wind uncertainties is presented based on Benders decomposition for stochastic programming in this paper. The ESSs are becoming essential components in microgrids due to significantly higher penetration of renewable energy sources. Integrating renewable energy sources coupled with ESSs in a power system enhances the power system reliability by increasing its availability and reducing its total cost of operation and maintenance. In addition, the ESS connected to a microgrid should be optimally sized to be able to provide the necessary power and minimize the total cost of investment and operation. In order to optimally size a storage system, a constrained optimization problem is solved using a probabilistic optimization method because the forecast of their output power cannot be determined accurately. In this paper, a probabilistic optimization problem is solved using the Benders decomposition for stochastic programming method to optimally size an ESS. This ESS will be integrated and connected to a grid-connected microgrid that has wind power generation. The simulation results prove the effectiveness of the proposed optimal sizing methodology.
Comparative Analysis of Stairways Detection Based on RGB and RGB-D Image

Stairways detection from RGB and RGB-D stair image is the challenging work in the computer vision research area. The detection system provides topnotch solution with greater portability in assisting visually impaired people and guiding the autonomous navigation system at the smart environments in the real world. In this paper, a framework is introduced to compare the stairways detection performance based on the RGB and RGB-D image. Here, the stairways candidate region is detected through the geometrical feature of a stair, i.e., stair steps are appeared in the concurrent sorted order. This feature is extracted from the concurrent parallel horizontal edges. The concurrent parallel horizontal edges are extracted from the canny edge image. For that, an edge linking and non-candidate edge elimination procedure is utilized in this work. Those are the key contributions of this paper. The stairway region of interest (ROI) is detected by aforementioned unique geometric feature of a stair and recognized the up, down, and negative stair by the support vector machine (SVM). For that, LBP and One-dimensional depth features are extracted from the RGB and RGB-D image respectively and sent to the SVM for classify. The stair images captured under different lighting conditions have been used to test the proposed framework to evaluate the resultant accuracy of the system.
Coalitional Game Framework for Content Distribution Using Device-to-Device Communication

We consider a set of cellular users associated with a base station (BS) in a cellular network that employs Device-to-device (D2D) communication. A subset of the users request for some files from the BS. Now, some of the users can potentially act as relays and forward the requested files, or partitions of files, from the BS to the requesting users (destination nodes) over D2D links. However, this requires cooperation among the cellular users. Also, when cellular users cooperate with each other, the total amount of energy consumed in transferring the requested files from the BS to the destination nodes can usually be considerably reduced compared to the case when each user separately downloads the file it needs from the BS. In this paper, we seek conditions under which users have an incentive to cooperate with each other. To this end, we model the above scenario using the framework of cooperative game theory. We are particularly interested in conditions under which it is beneficial for all the cellular users to cooperate, i.e., the grand coalition is stable. For this we use the solution concept of core from cooperative game theory. We consider two different models: (i) Model A, in which the BS can split a file into multiple partitions and send these partitions to different relays, which multicast the partitions to the destination nodes, and (ii) Model B, in which for each file, the BS sends the entire file to a single relay, which multicasts it to the destination nodes. First, we show that, in general, the above coalitional game under Model A may have an empty core, i.e., it may not be possible to stabilize the grand coalition. However, we show that in an important special case of this game, wherein all D2D and BS-cellular user communication links are symmetric across cellular users and the D2D data rates are much higher than the BS-cellular user data rates, the core is always non-empty. Next, we show that under Model B, the problem of assigning relays to destination nodes so as to maximize the sum of utilities of all the users is NP-Complete. Finally, we design heuristics to solve this problem and evaluate their performance via numerical computations.
Prioritizing JUnit Test Cases Without Coverage Information: An Optimization Heuristics Based Approach

Regression testing is an expensive activity and Test Case Prioritization (TCP) acts as an improvement mechanism for it. TCP techniques for object oriented programs need attention and in our study, we explored prioritization of JUnit test cases. Ten benchmark Java programs with their several mutated versions were studied. As collecting coverage information is a costly effort, we bypassed these steps and used optimization heuristics for ordering JUnit test cases at test method level. Our approach formulated a novel fitness objective which depends on the number of modified lines executed per unit of execution time. As regression testing is performed after some modification is done on an existing program, maximizing the execution of number of modified lines is highly lucrative. The test case prioritization problem was replicated in context of 0/1 Knapsack problem and then it was solved using Genetic Algorithm (GA). Our exploration also included application of Simulated Annealing and Ant Colony Optimization method for determining the best execution ordering of test cases. We examined the usage of Multi-objective GA by building another new fitness metric which aims to maximize the number of inheritance edges covered by a test case. Results indicate the superiority of optimization heuristics over other existing approaches. It appeared that multi-objective GA yielded better result than single objective prioritization. Among the single objective techniques, ACO performed best. To the best of our knowledge, this is the first study which explored all the above mentioned optimization heuristics for ordering JUnit test cases with the newly coined fitness intents.
A Neural Network based Vietnamese Chatbot

Nowadays, chatbot is a hot topic, chatbots are built from generative models are gaining success. The purpose of this article is to build a Vietnamese chatbot based on the seq2seq model incorporating the attention mechanism. We have built the model and tested on deep learning framework Pytorch using GPU. The model was trained end-to-end with no hand-crafted rules. Model is built from a small dataset and can generate responses to a user. However, generated responses still need to be improved to get a meaningful conversation.
Vector Space Model based Topic Retrieval from Bengali Documents

This work attempts to find the topic of a Bengali text document based on a traditional similarity based retrieval model named Vector Space Model. This fascinating model has traditionally obtained much fame in the research community, but to the best of our knowledge, was never tried for Bengali topic retrieval. In this work, therefore, we have used four different settings of the vector space model which are TF-IDF weighting scheme with Euclidean distance, TF-IDF weighting scheme with Manhattan distance, TF-IDF weighting scheme with Cosine similarity and Improved document scoring scheme. The K-nearest neighbor algorithm is then used to retrieve the topic of a query document. For training and testing purpose, we have also created a large corpus of Bengali text documents. On this corpus, our result shows the best retrieval accuracy of 93.33%.
Electric Vehicles Assisted Multi-Household Cooperative Demand Response Strategy

The recent ongoing development of electrical vehicles (EVs) offers vast benefits not only in environmental protection and economics, but also in demand response (DR) management on consumer side. Adopting EVs in DR enables householders to alleviate the load burden while reducing electric bill simultaneously. In this paper, we utilize EVs as temporary energy storage facilities to assist the power transaction, which ensures the flexibility and economic benefit. An innovative EVs assisted DR strategy including a neighbor energy sharing (NES) model is proposed, to jointly optimize the load distribution via vehicle to home (V2H) and vehicle to neighbor (V2N) connections, and economic cost for a residential network with multi-household. The effectiveness of the proposed DR strategy is verified by numerical results in terms of load balancing and cost reduction. It also significantly outperforms the previous DR approaches.
Content determination in the generation of referring expressions

In this paper, we describe a general framework for the generation of referring expressions. Given an intended referent in the domain, domain‐dependent mapping rules build a domain‐independent level of recoverable semantic structure, which encodes the semantic content to be recovered by the hearer; a second set of mapping rules then builds an abstract syntactic structure, which is unified with a grammar and lexicon to produce a surface noun phrase. Within this framework, (0 we present a detailed description of the algorithms required to build the recoverable semantic structure, and discuss how the problem of infinite recursion can be handled when using relations in descriptions; and (/;') we show how the two levels of representation facilitate the generation of one‐anaphoric noun phrases.
The Relationship between Entrepreneurial Self-Efficacy and Entrepreneurial Intention

This study examines the effects of the relationship between entrepreneurial self-efficacy and entrepreneurial intention. The empirical results from a survey of 644 Chinese students show that (1) entrepreneurial self-efficacy has a significant positive effect on entrepreneurial intention; (2) entrepreneurial attitude has a significant positive impact on entrepreneurial intention; and (3) entrepreneurial attitude fully mediates the relationship between entrepreneurial self-efficacy and entrepreneurial intention. The results challenge several findings of the existing research and provide a theoretical basis for enhancing university students’ entrepreneurial intentions.
Face Recognition System in Unconstrained Environment through Convolution Neural Network

Compared with traditional machine learning methods, the learning characteristics of deep learning-based methods have received more and more attention in the past few years and have recently gained widespread popularity. This paper presents a novel method of face recognition in unconstrained environment based on deep residual ResNet50, standard convolution neural network architecture. The network is modified and fine-tuned for different facial recognition scenarios in an unconstrained environment. The goal of this Automatic Face Recognition (AFR) is to improving recognition performance with reducing the False Acceptance Rate (FAR) and False Rejection Rate (FRR). The proposed framework was trained and tested on the publicly available challenging datasets face94, face95, face96, Grimace and LFW and shows that it outperforms previous state-of-the-art methods without the need for additional resources.
A survey of Static Android Malware Detection Techniques

Nowadays android is the most popular operating system. Android apps suffer from more and more malware attacks. So more and more research is done to get a reliable malware detection approaches. Android malware detection is mainly divided into dynamic approach and static approach. In this paper, we present a survey of the two major approaches of the static Android malware detection: the permission based Detection technique and the signature based detection technique. It is a comparative study, that should be helpful for researchers in this topic. Based on clear parameters the study illustrates the similarity and differences in addition to the accuracy in important published researches.
A 340GHz 20W Staggered Double Vane Traveling Wave Tube

Based on the staggered double vane (SDV) slow wave structure (SWS), a 340 GHz traveling wave tube (TWT) amplifier is designed in this work. The simulation results show that the interaction impedance of the optimized SWS is greater than 4 Ohms in the frequency range from 320 GHz to 350 GHz. And the amplification property of the TWT is that the output power is more than 13 W, corresponding output gain is more than 31.5 dB in the whole working frequency band.
The Mystery Curve-Part 2: Local Symmetry and Frieze Groups [Lecture Notes]

In this article, we continue to study the mystery curve, which was originally named by Farris [1] and discussed in the first part of this two-part article in IEEE Signal Processing Magazine [2]. The mystery curve has an N-fold property with additional local symmetry as shown in Figure 1. In particular, we focus on symmetric patterns, including translation, reflection, rotation, and glide reflection. This class of patterns is called a frieze group. We show how to construct frieze groups with trigonometric functions and extend this method to the mystery-curve design.
Development and research of temporal RVTI-grammar for workflow diagrams processing

This paper describes the development and study of temporal RVTI-grammar, which allows to take into account time parameters in the process of document flow analysis. Methods of neutralization of the revealed errors, translation of conceptual schematic models of the automated systems presented in widely used visual languages into the diagram models based on formal languages are considered.
A User-Oriented Collaborative Filtering Algorithm for Recommender Systems

Recommender systems (RSs) are one of the emerging applications in electronic commerce companies, such as Amazon, Flipkart, eBay, Levi's and many more. It generates a list of probable recommendations for the customers or users of the companies in one of the two categories, namely collaborative filtering-based (CF) and content-based. In CF, the recommendation is based on a user's past behavior and other similar users' behavior. Many algorithms have been developed for finding the recommendation in CF recommender systems. One of the popular algorithms is $k$-nearest neighbor ($k\text{NN}$), in which the recommendation depends on the behavior of $k$ similar users. More specifically, the user rating of a non-rated or unpurchased item is the aggregate value of $k$ similar users. However, the user rating is unknown iff the $k$ similar users have not rated the corresponding item. In this paper, we propose a user-oriented CF algorithm for RSs. The proposed algorithm selects $k$ similar users by finding the similarity count with other users for a given item. We implement the proposed algorithm and compare with $k\text{NN}$ algorithm. Simulations using four generated datasets show the supremacy of the proposed algorithm in terms of the mean absolute error (MAE), root mean square error (RMSE), precision, recall and $F$-score.
Checking Structural Stability of BDC-Decomposable Systems via Convex Optimisation

In this letter we show how the problem of assessing structural local stability of BDC-decomposable systems, left open in recent literature, can be solved via convex optimisation. First we give a simple test, based on a sufficient condition, that requires checking the strict co-positivity of a multivariate polynomial. Then we provide a stronger test, based on a necessary and sufficient condition, which can be numerically implemented via LMI-based convex optimisation. The proposed approach certifies the structural stability of non-trivial systems, including a biological network discussed in the literature.
Desensitized Ensemble Kalman Filtering for Induction Motor Estimation

The ensemble Kalman filter (EnKF) is a random sampling method based on Monte Carlo and Kalman filter for the extremely high-dimensional nonlinear system. However, the uncertain parameters may be have unexpected effects on the state estimate problem. This paper introduces the desensitized optimal control methodology into the EnKF, and derives the algorithm formula of the proposed desensitized EnKF (DEnKF). A new desensitized cost function is designed by combing the mean square error cost function with the penalty cost function, and the optimal gain is obtained by minimizing the new cost function. The sensitivity of the state errors is propagated by the ensemble error matrix. To demonstrate the effectiveness of the proposed DEnKF, two numerical simulations for a falling body model and induction motor model are designed. The simulation results show that the proposed DEnKF could weaken the sensitivity of state estimate errors to the uncertain parameters and improves the state estimation accuracy, comparing with the standard EnKF.
Design of Dual-band Dual-polarized MIMO Antenna for mm-wave 5G Base Stations with Octagonal Prism Structure

This paper presents design of 5G multiple input multiple output (MIMO) antenna for 5th generation (5G) base stations. The proposed antenna is dual band and it operates at millimeter-wave (mm-wave) frequencies 28 GHz and 38 GHz. It is also dual-polarized and it can switch between vertical and horizontal polarizations. The sub-array is formed of four gap-coupled microstrip slotted dual-band patch antennas with corporate microstrip feed network. The sub-array exhibits realized gain greater than 12 dBi at 28 GHz and 38 GHz and 10-dB bandwidth of around 1 GHz at 28 GHz and 38 GHz. The antenna sub-arrays are arranged on the sides of an octagonal prism to synthesize multi-beam radiation patterns with twelve sub-arrays distributed on each side of the prism. The proposed antenna has the capability of steering the pattern to particular directions by switching between antenna sub-arrays placed on either side of the prism. The proposed antenna is a good candidate for 5G base stations as it achieves polarization and pattern diversity and fulfills 5G system requirements in terms of high gain, adequate bandwidth, MIMO configuration and fixed beam-switching capability.
Performance Analysis and Optimization for the LDPC-Coded Multi-Carrier LDS System

With the common property of the sparse structure, multi-carrier low density signature (MC-LDS) and low density parity-check (LDPC) codes can be combined into a joint sparse factor graph (JSFG) while the joint detection and decoding (JDD) algorithm could bring significant gains in terms of the bit error rate (BER). In this paper, considering the coupling character of the soft information interaction in the elaborate JSFG, we carve the distribution of the merged decoding soft information from both sides and theoretically analyze the error performance of the uplink LDPC-coded MC-LDS system. Correspondingly, not only the average BER but also the practical decoding threshold could be effectively derived for any given number of iterations. Furthermore, based on the theoretical analysis, we optimize the degree distribution of the JSFG by leveraging the differential evolution method. Finally, simulation results show that the practical threshold of the JDD receiver with the optimized code approaches the signal to noise ratio of the theoretical capacity.
NMF Hyperspectral Unmixing Of The Sea Bottom: Influence Of The Adjacency Effects, Model and Method

Sea bottom unmixing is a challenging task for the analysis of coastal zones. Actually the upward photons are attenuated and diffused by the water column layer, giving low signal to noise hyperspectral data. A classical approach is to perform inversion of the water column using semi-analytical parametric models and estimation process, and obtain the water column constituents (chlorophyll, suspended matter, dissolved organic matter and bathymetry), and the coefficients of pure materials reflectance spectra (endmembers), given in spectral libraries, for each pixel. We consider here the case of unknown endmembers, and we suppose that the water column components have been obtained by a classical inversion method or in-situ measurements. For each observed pixel the upward luminance is analysed and decomposed into three terms, respectively issued after interaction with the target bottom pixel, its neighbours, and the water column. We show that in some conditions the adjacent pixels effect is not negligible, due to the diffusion in the water column, and we develop in accordance a new mixing model for the sea bottom. We propose a non-negative matrix factorisation based unmixing method to solve the problem, and present results for hyperspectral data simulations.
Novel Cold Cathode Design for mm-wave (THz) Spatial Harmonic Magnetrons

Cathode Emission for effective electron cloud/ plasma expansion is one of the prime physical mechanism influencing the performance of a magnetron with reference to efficiency and power levels. It essentially requires enhanced secondary electron emission by cold cathodes in mm/sub-mm wave (THz) spatial harmonic magnetrons (SHMs). The manuscript presents a novel cold cathode design named as interdigitated thermally assisted - secondary electron emission (T-SEE) cathode. CST-Particle Studio (CST-PIC) simulations of SHM with the proposed novel configuration cold cathode has resulted in 3kW power enhancement with 3.46% efficiency enhancement with reference to the conventional cold SEE cathode used in SHMs.
Part-of-Speech Tagging for Azerbaijani Language

The paper describes the process of implementing a HMM PoS tagger for Azerbaijani language to tag given text based on the tagged corpus. Different methodologies for part-of speech tagging have been studied, and after analysis of these methodologies, Hidden Markov Model has been chosen for implementation. For Azerbaijani language, the paper demonstrates the steps taken to build a stemmer as an essential part of PoS tagger. A thorough examination of possible word groups and exceptions has been conducted and most of such cases have been successfully handled. As of now, a tagged corpus of large enough size does not exist for Azerbaijani language and it hinders the testing process of HMM tagger. For this reason, a small corpus has been created for testing. However, as HMM shows remarkable performance when run on English corpus, it is expected that it will produce decent results for Azerbaijani language too.