Miracle-dz/UniFAR-FTUs huggingface dataset:
The Dynamic-FPM: An Approach for Identifying Events from Social Networks Using Frequent Pattern Mining and Dynamic Support Values

With the proliferation of social media data reporting on all aspects of human activity, being able to automatically identify events is becoming increasingly important. Identifying events from data streams is very challenging, as those streams are of unbounded size and their data elements arrive in real-time and at an unpredictable rate. Even more, it is not possible to backtrack over past arrived data elements or maintain and review the entire stream history. In this paper, we present an approach for detecting the daily occurring events in social network streams related to major events using a Frequent Pattern Mining method. In addition, we introduce dynamic support definition method to replace the fixed, a priori given one. As the number of text posts streamed each day is not fixed, a dynamic support, can adapt to the nature of data streams and can improve the identification of events. Experiments were employed on two streams related to two different major events. The first stream was related to the UK General Elections 2015, with more than 1. 1M tweets, and the other was related to the Greece Crisis 2015, with more than 150K tweets. Results showed that using a daily dynamic defined support can better identify events from social network streams rather than fixed ones, as it can cope with the dynamic and changing aspect of data streams.
Privacy Bargaining with Fairness: Privacy-Price Negotiation System for Applying Differential Privacy in Data Market Environments

Digital data are an essential resource for intelligent decision making. As the value of digital data increases, digital markets, where data owner and consumers can deal with data, have also been attracting attention as a means to obtain data. However, the collection of digital data can lead to privacy breaches, which are a substantial impediment that hinders an individual’s willingness to provide data. Differential privacy, which is a de facto standard for privacy protection in statistical databases, can be applied to solve the privacy violation problem. To apply differential privacy to the data market, the amount of noise and corresponding data price must be determined; however, this matter has not yet been studied. In this work, we propose a fair negotiation method that can set the appropriate price and noise parameter in the differentially private data market environment. We suggest a data market framework with a market manager that acts as a broker between the data provider and consumer. We also propose a negotiation technique to determine the data price and noise parameter $\varepsilon$ using Rubinstein bargaining considering social welfare to prevent unfair transactions. We validate that the proposed negotiation technique can determine an appropriate level of $\varepsilon$ and unit price without unfair trade to either the data provider and the consumer.
Natural Language Processing and Deep Learning Towards Security Requirements Classification

Security Requirements classification is an important area to the Software Engineering community in order to build software that is secure, robust and able to withstand attacks. This classification facilitates proper analysis of security requirements so that adequate security mechanisms are incorporated in the development process. Machine Learning techniques have been used in Security Requirements classification to aid in the process that lead to ensuring that correct security mechanisms are designed corresponding to the Security Requirements classifications made to eliminate the risk of security being incorporated in the late stages of development. However, these Machine Learning techniques have been found to have problems including, handcrafting of features, overfitting and failure to perform well with high dimensional data. In this paper we explore Natural Language Processing and Deep Learning to determine if this can be applied to Security Requirements classification.
Causal Relationship Analysis of Heart Rate Variability and Band Power Time Series of Electroencephalographic Signals

This study aimed to find whether there is a causal relationship between band power time series (BPts) extracted from EEG and heart rate variability (HRV). Such relationships were explored during spontaneous and a controlled breathing tasks. Data analyzed were recordings obtained from 14 healthy subjects using one ECG lead and 21 EEG channels. The RR intervals from the ECG were used to obtain the HRV signal, which was decomposed with Empirical Mode Decomposition into components of different spectral content known as intrinsic mode functions (IMFs). Granger causality tests were run for the BPts of alpha, beta and gamma frequency ranges of the EEG signal and the HRV signals IMFs. G-causality increased for three different conditions: slower IMFs (IMF4), BPts of higher frequency (gamma) band and during task realization. Meaning, gamma’s BPts G-caused HRV for a larger number of subjects and channels. Also there was a larger incidence on the number of channels that G-caused HRV during the controlled breathing task. The causal influence from the BPts of EEG signals to the HRV IMFs suggests there is an indirect or unobserved interaction between instantaneous changes on EEG band power and components of HRV which may explain changes in its dynamics.
Index Based Efficient Algorithms For Closest Community Search

The community search problem is defined as finding densely connected subgraphs in a large graph containing a given set of query nodes. One of the limitations of many current community search models is that detected communities may include irrelevant nodes, called the “free riders”. In this paper, we study the community search problem in the truss-based closest community model aimed to discover community for a given query set with avoiding the free rider effect. The Closest Truss Community (CTC) is a densely connected k-truss subgraph that contains the query nodes and has the smallest diameter. We use a greedy approach and propose a Truss EQuivalence based index graph (TEQ) that supports the search of closest trust community. To further improve the efficiency of the search, we create a maximum spanning tree of TEQ, as an index tree. We also propose an early pruning algorithm to improve the efficiency of free rider elimination. Extensive experiments on large real-world networks validate the efficiency and effectiveness of our algorithms over the state-of-the-art methods.
A Blockchain-Based Reward Mechanism for Mobile Crowdsensing

Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator’s utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS.
Optimization of arable land use towards meat-free and climate-smart agriculture: A case study in food self-sufficiency of Vietnam

UN Sustainable Development Goals and the Paris agreement for climate change indicate that a transition to sustainable and healthy diets is necessary. Additionally, the fact that agricultural sector is responsible for near a quarter of global greenhouse emissions (IPCC 2019 - special report on climate change), such transition will require substantial dietary shifts, including reduction of sugar and red meat consumption. Vietnam, with more than 95 millions of population, have a challenge to significantly reduce the rice consumption and convert some of the land used for it to production of more legumes. However, correct allocation of arable land for cultivation of particular crops’ combination that would ease the transition, and comply with recommendations for healthy nutritional intake, is a challenge of the society. We approached the problem of arable land allocation with mathematical optimization, in particular stochastic evolutionary computing. Arable land allocation to crops’ combination is evaluated through three objectives: food self-sufficiency, climate efficiency and crop diversity. Candidate solutions (crops’ combinations) were analysed through the non-dominated Pareto front with prioritizing the objective of food self-sufficiency of Vietnam. The results suggest significant change in production of certain crops. As such, sugar cane and rice are required to be reduced on expense of increased production of soybeans, maize, brassicas, and nuts. Therefore, the current surplus of produced carbohydrates would be reduced while proteins increased, which leads to balanced production of macronutrients.
Source Classification in Atrial Fibrillation Using a Machine Learning Approach

A precise analysis of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is necessary for a better understanding of the mechanisms behind atrial fibrillation (AF). Blind source separation (BSS) techniques have proven useful in extracting the AA source from ECG recordings. However, the automated selection of the AA source among the other sources after BSS is still an issue. In this scenario, the present work proposes two contributions: i) the use of the normalized mean square error of the TQ segment (NMSE-TQ) as a new feature to quantify the AA content of a source, and ii) an automated classification of AA and non-AA sources using three well-known machine learning algorithms. The tested classifiers outperform the techniques present in literature. A pattern in the mean and standard deviation of the used features, for AA and non-AA sources, is also observed.
G-Finder: Approximate Attributed Subgraph Matching

Subgraph matching is a core primitive across a number of disciplines, ranging from data mining, databases, information retrieval, computer vision to natural language processing. Despite decades of efforts, it is still highly challenging to balance between the matching accuracy and the computational efficiency, especially when the query graph and/or the data graph are large. In this paper, we propose an index-based algorithm (G-FINDER) to find the top-k approximate matching subgraphs. At the heart of the proposed algorithm are two techniques, including (1) a novel auxiliary data structure (LOOKUP-TABLE) in conjunction with a neighborhood expansion method to effectively and efficiently index candidate vertices, and (2) a dynamic filtering and refinement strategy to prune the false candidates at an early stage. The proposed G-FINDER bears some distinctive features, including (1) generality, being able to handle different types of inexact matching (e.g., missing nodes, missing edges, intermediate vertices) on node attributed and/or edge attributed graphs or multigraphs; (2) effectiveness, achieving up to 30% Fl-Score improvement over the best known competitor; and (3) efficiency, scaling near-linearly w.r. t. the size of the data graph as well as the query graph.
Multi-View Subspace Clustering based on Tensor Schatten-p Norm

In this paper, we focus on the multi-view clustering problem. A novel multi-view clustering framework, called multi-view subspace clustering based on tensor Schatten-p norm (MVSC-TSP), is proposed for clustering task. In our method, the tensor Schatten-p norm, which is based on tensor singular value decomposition, is utilized to explore the global low-rank structure of multi-view self-representations. Since 0<p< 1, using tensor Schatten-p norm to relax the tensor multi-rank is more effective than the commonly used tensor nuclear norm. Furthermore, we present a new generalized tensor soft thresholding algorithm to solve the tensor Schatten-p norm minimization problem. Based on this, the proposed non-convex optimal problem can be efficiently solved by the alternating direction method of multipliers. Experimental results on image clustering demonstrate that the proposed method is superior to the state-of-the-art methods in term of various evaluation metrics.
Mitigating popularity bias in Twitter- Recommending novel hashtags using pooled tweets

Twitter has grown as a popular microblogging site in the last decade. A tweet, is a small, crisp 140 characters-based text in Twitter. It lets people raise their opinions, convey ideas and to follow other events or people. Hashtags in the Twitter network allow spread of a particular idea, event and help in easy search in the voluminous amount of data. The hashtags used by twitter users may strengthen particular hashtag conversation leading to a number of tweets concerning the idea. Twitter-pooling phenomenon refers to collection of tweets based on hashtags or conversations. We use hashtag-based twitter pooling in this work.Recommender Systems (RSs) suggest user-oriented suggestions for items from a plethora of options available across the Internet. Recommendations may be generated keeping in view the other similar users or items across the recommendation environment. Research in recommendation for social networks has been worked upon lately. In this paper, we strive to suggest hashtags to be used by the twitter users. We propose a notion to recommend novel hashtags in Twitter to handle the prevalent challenge of popularity bias across RSs. The idea of novelty in hashtag recommendation has been utilized for this research motive.
Security and Privacy for Green IoT-Based Agriculture: Review, Blockchain Solutions, and Challenges

This paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture.
Multipath Routing and MPTCP-Based Data Delivery Over Manets

In some special circumstances (e.g., tsunamis, battlefields, and earthquakes), communication infrastructures are damaged or nonexistent. For communication among people, mobile smart devices (MSDs) can be used to construct mobile ad hoc networks (MANETs). This paper focuses on the problem of data delivery in MANETs aiming to improve the quality of service (QoS) and quality of experience (QoE) users receive. MANETs, however, have the well-known problems of frequent disconnections and high rates of failed transmissions as MSDs move in and out of network coverage areas, and the topology constantly changes. To solve these issues, the main contributions of this work are as follows: (1) we provide and investigate the QoE-driven multipath TCP (MPTCP)-based data delivery model in MANETs; (2) we present hidden Markov model-based optimal-start multipath routing, which can effectively predict a mobile node’s near future network connection state according to its past connection state; (3) we leverage MPTCP to simultaneously transmit data via multiple interfaces of MSDs and improve the establishment method for MPTCP subpaths; and (4) we study and improve the algorithm of multihop routing in MANETs. The test results show that our algorithms can offer more efficient use of multiple subpaths and better network traffic load balancing than using standard MPTCP alone.
Logistic Optimization for Multi Depots Loading Capacitated Electric Vehicle Routing Problem From Low Carbon Perspective

In this paper, a multi depots capacitated electric vehicle routing problem where client demand is composed of two-dimensional weighted items (2L-MDEVRP) is addressed. This problem calls for the minimization of the transportation distance required for the delivery of the items which are demanded by the clients, carried out by a fleet of electric vehicles in several depots. Since the 2L-MDEVRP is an NP-hard problem, a heuristic algorithm combined variable neighborhood search algorithm (VNS) and space saving heuristic algorithm (SSH) is proposed. The VNS algorithm is used to solve the vehicle routing problem (VRP) sub-problem, and the SSH algorithm is used to solve the bin packing problem (BPP) sub-problem. We propose the space saving heuristic to find the best matching solution between the next loading item and the feasible loading position. The SSH-VNS algorithm is tested by using benchmark instances available from the literature. The results show that the SSH-VNS algorithm has better performance compared with other published results for solving capacity vehicle routing problem (CVRP) and two-dimensional capacity vehicle routing problem (2L-CVRP). Some new best-known solutions of the benchmark problem are also found by SSH-VNS. Moreover, the effectiveness of the proposed algorithm on 2L-MDEVRP is demonstrated through numerical experiments and a practical logistic distribution case. In the last section, the managerial implications and suggestions for future research are also discussed.
Comparison of Two-Level and Three-Level NPC Inverter Topologies for a PMSM Drive for Electric Vehicle Applications

Multidimensional comparison of two-level and three-level DC/AC converters for a 120 kW permanent magnet synchronous machine (PMSM) drive is carried out in this study. Comparison of two topologies with Sinusoidal Pulse Width Modulation (SPWM) and Space Vector Pulse Width Modulation (SVPWM) by means of output current THD, conduction and switching losses, thermal stresses on semiconductors and switching frequency limitation are investigated at various operating points. Electro-thermal simulation of both topologies are achieved using PLECS tool with Silicon IGBT and diode pairs from SEMIKRON. Results of these analysis show the limitations of two-level inverter topology in terms of the switching frequency and point out the advantage of three-level inverter in high speed PMSM drives.
A Text Preprocessing Framework for Text Mining on Big Data Infrastructure

There are many people using social media to comment appearance of a various event and post own activity in every day. The business organizations and industries can use social media to improve a produce by using the technique of text mining. However, a single machine cannot perform to compute a large amount of data because it has the resource limitation such as CPU, main memory, storage and so on. Moreover, a data also consists of structured and unstructured data that must be prepared before a computation with text mining or machine learning. Therefore, the text preprocessing becomes one of the most very important tasks because it includes many steps. In this paper, we design and develop an efficient text preprocessing framework on a big data infrastructure, which is proposed to support the text preprocessing task to reduce the computation time. The framework consists of the main four modules: Data collection module, storage module, data cleaning module and feature extraction module. We collected the sentiment data from Facebook page, named “Tasty” that has more than 90 million members for the performance evaluation. The data was separated into different three sizes and each dataset was tested on different the cluster system environments. Moreover, we also compared the computation time between the proposed our framework and the single machine. As the result, our framework can reduce the computation time significantly. Thus, this framework can apply to solve the problem of the complex text preprocessing in text mining areas.
Density-based Clustering Method for Hardware Trojan Detection Based on Gate-level Structural Features

The outsourcing of IP cores raises serious concerns about the security of ICs due to the possible malicious modifications of circuits. The malicious modifications of ICs called hardware Trojans can change the system behavior, cause malfunctions of chips or leak information to a third party. This paper presents a hardware Trojan detection approach for gate-level netlists using a density-based clustering method. The unsupervised density-based clustering method can identify HTs based on the gate-level structural features, avoiding deliberate threshold setting for different features and feature information loss. We conducted experiments on the TrustHub benchmarks to verify the validity of this approach. The results demonstrate that our method can improve the accuracy rate up to 5 times compared to the existing state-of-the-art methods.
Advertiser-Assisted Behavioral Ad-Targeting via Denoised Distribution Induction

Nowadays, advertising (ad) deliveries are conducted in a targeted manner to improve their effectiveness and efficiency. However, human behavior data in ad-platforms such as Web browsing history is complex and contains a lot of “noise”. On the other hand, information in the advertiser’s domain (e.g. e-commerce sites) seems to contain less noise (e.g. product browsing history) with respect to ad-targeting. We introduce a new denoising method for behavioral ad-targeting based on the idea of feature distribution alignment induced by the advertiser’s domain. This denoised distribution induction can be achieved by employing domain adversarial training with stabilization techniques. We evaluate our model on real world data originating from an e-commerce site and an ad-platform. The results of an ablation study have demonstrated the advantage of utilizing an advertiser’s domain for denoising human behavior data of an ad-platform domain.