Miracle-dz/UniFAR-FTUs huggingface dataset:
Albanian Language Identification in Text Documents

In this work we investigate the accuracy of standard and state-of-the-art language identification methods in identifying Albanian in written text documents. A dataset consisting of news articles written in Albanian has been constructed for this purpose. We noticed a considerable decrease of accuracy when using test documents that miss the Albanian alphabet letters " E " and " C " and created a custom training corpus that solved this problem by achieving an accuracy of more than 99%. Based on our experiments, the most performing language identification methods for Albanian use a naive Bayes classifier and n-gram based classification features.
Complexity Bounds for Relational Algebra over Document Spanners

We investigate the complexity of evaluating queries in Relational Algebra (RA) over the relations extracted by regex formulas (i.e., regular expressions with capture variables) over text documents. Such queries, also known as the regular document spanners, were shown to have an evaluation with polynomial delay for every positive RA expression (i.e., consisting of only natural joins, projections and unions); here, the RA expression is fixed and the input consists of both the regex formulas and the document. In this work, we explore the implication of two fundamental generalizations. The first is adopting the "schemaless'' semantics for spanners, as proposed and studied by Maturana et al. The second is going beyond the positive RA to allowing the difference operator. We show that each of the two generalizations introduces computational hardness: it is intractable to compute the natural join of two regex formulas under the schemaless semantics, and the difference between two regex formulas under both the ordinary and schemaless semantics. Nevertheless, we propose and analyze syntactic constraints, on the RA expression and the regex formulas at hand, such that the expressive power is fully preserved and, yet, evaluation can be done with polynomial delay. Unlike the previous work on RA over regex formulas, our technique is not (and provably cannot be) based on the static compilation of regex formulas, but rather on an ad-hoc compilation into an automaton that incorporates both the query and the document. This approach also allows us to include black-box extractors in the RA expression.
Low-Level Control of a Quadrotor With Deep Model-Based Reinforcement Learning

Designing effective low-level robot controllers often entail platform-specific implementations that require manual heuristic parameter tuning, significant system knowledge, or long design times. With the rising number of robotic and mechatronic systems deployed across areas ranging from industrial automation to intelligent toys, the need for a general approach to generating low-level controllers is increasing. To address the challenge of rapidly generating low-level controllers, we argue for using model-based reinforcement learning (MBRL) trained on relatively small amounts of automatically generated (i.e., without system simulation) data. In this letter, we explore the capabilities of MBRL on a Crazyflie centimeter-scale quadrotor with rapid dynamics to predict and control at $\leq$50 Hz. To our knowledge, this is the first use of MBRL for controlled hover of a quadrotor using only on-board sensors, direct motor input signals, and no initial dynamics knowledge. Our controller leverages rapid simulation of a neural network forward dynamics model on a graphic processing unit enabled base station, which then transmits the best current action to the quadrotor firmware via radio. In our experiments, the quadrotor achieved hovering capability of up to 6 s with 3 min of experimental training data.
Fast Green Function Evaluation for Method of Moment

In this letter, an approach to accelerate the matrix filling in the method of moment (MOM) is presented. Based on the fact that the Green function is dependent on the Euclidean distance between the source and the observation points, we constructed an efficient adaptive one-dimensional interpolation approach to fast calculate the $Exp$-type function values. In the proposed method, several adaptive interpolation tables are constructed based on the maximum and minimum distances between any two integration points with local refinement near zero function values to minimize the relative error. An efficient approach to obtain the sampling points used in the interpolation phase is carefully designed. Then, any function values can be efficiently calculated through a linear interpolation method for $Exp$ and a Lagrange polynomial interpolation method for the Green function. In addition, the error bound of the proposed method is rigorously investigated. The proposed method can be quite easily integrated into the available MOM codes for different integration equation (IE) formulations with few efforts. Comprehensive numerical experiments validate its accuracy and efficiency through several IE formulations. Results show that over 20% efficiency improvement can be achieved without sacrificing the accuracy.
Predictive Modeling of LTE User Throughput Via Crowd-Sourced Mobile Spectrum Data

In this paper, crowd-sourced mobile speed-test measurements from users of Long-Term Evolution (LTE) cellular networks are fused with other information about user’s context and radio access network (RAN) deployment to train a data-driven predictive model of LTE download and upload throughputs. The predictive model is built using a state-of-the-art gradient boosted decision tree algorithm capable of capturing the complex relationships among the various attributes of our dataset. Our results indicate that leveraging both contextual and RAN deployment information improves the prediction accuracy between 14 − 19%, when compared to a baseline relying solely on the LTE signal metrics such as received power and signal-to-noise-ratio.In order to interpret the predictions produced by the model, we further employ a recently-introduced framework using Shapley values from game theory. Results highlight the fundamental variables affecting the model’s prediction of user throughput while also quantifying the level of each variable’s contribution, thereby offering insights to network operators as well as end-users on how they might improve their performance in practice.
Fusion-Based Multimodal Detection of Hoaxes in Social Networks

Social networks make it possible to share information rapidly and massively. Yet, one of their major drawback comes from the absence of verification of the piece of information, especially with viral messages. This is the issue addressed by the participants of the Verification Multimedia Use task of Mediaeval 2016. They used several approaches and clues from different modalities (text, image, social information). In this paper, we explore the interest of combining and merging these approaches in order to evaluate the predictive power of each modality and to make the most of their potential complementarity.
Methods for Big Data Integration in Distributed Computation Environments

One of the main challenges in data integration is to create an extensible end-to-end system, which will perform not only extraction, but also schema alignment and entity resolution techniques. This is even more challenging in a world of the big data, when we have to deal with the large number of heterogeneous data sources. In this case the system has to be automatic and less user depended. This paper aims to overview and analyze the modern approaches and systems to successfully perform big data integration. This work is performed as a master thesis, which is aimed to propose an architecture of the system to perform integration of heterogenous sources in a distributed computation environment, implement and apply it to a real-world problem of e-commerce domain as a part of master thesis.
HEvaS: Headline Evaluation System

Automatic headline generation is a sub-task of oneline summarization with many reported applications. Evaluation of systems generating headlines is a very challenging and undeveloped area. In this paper, we introduce a system that performs automatic evaluation of systems in terms of a quality of the generated headlines. The evaluation is performed using multiple metrics for comparing evaluated headlines with the gold standard ones or measuring their coverage of main document topics. Both types of metrics evaluate headline's content and informativeness, but not grammatical structure. The only input required by our system is a set of documents with gold standard and automatically generated headlines. The Headline Evaluation System (HEvaS) provides a user with a choice from multiple (10) metrics, then calculates the chosen metrics, performs statistical analysis of the evaluated systems and visualizes the results. Multiple headline generation systems can be evaluated at the same run. This paper describes all evaluation metrics and architecture, utilized by our system. As an evaluation of the HEvaS, we perform a case study with a couple of baseline systems and report the results. Although we tested the system on English content only, the multilingual content can also be supported.
A Non-intrusive Fault Diagnosis System for Robotic Platforms

The increasing complexity of tasks that are required to be executed by robots demands higher reliability of robotic platforms. For this, it is crucial for robot developers to consider fault diagnosis. In this study, a general non-intrusive fault diagnosis system for robotic platforms is proposed. A mini-PC is non-intrusively attached to a robot that is used to detect and diagnose faults. The health data and diagnosis produced by the mini-PC is then standardized and transmitted to a remote-PC. A storage device is also attached to the mini-PC for data logging of health data in case of loss of communication with the remote-PC. In this study, a hybrid fault diagnosis method is compared to consistency-based diagnosis (CBD), and CBD is selected to be deployed on the system. The proposed system is modular and can be deployed on different robotic platforms with minimum setup.
FACE - Face At Classroom Environment: Dataset and Exploration

The rapid development in face detection study has been greatly supported by the availability of large image datasets, which provide detailed annotations of faces on images. However, among a number of publicly accessible datasets, to our best knowledge, none of them are specifically created for academic applications. In this paper, we propose a systematic method in forming an image dataset tailored for classroom environment. We also made our dataset and its exploratory analyses publicly available. Studies in computer vision for academic application, such as an automated student attendance system, would benefit from our dataset.
Self-Deprecating Sarcasm Detection: An Amalgamation of Rule-Based and Machine Learning Approach

Sarcasm is a special category of figurative language, which is mainly used in online social media to convey messages with implicit semantics and criticism. Such messages are used for sarcastic remarks using contemptuous, ridicule, bitter, taunt, and mock related words or phrases. Though sarcasm detection is a well-considered problem by the researchers, to the best of our knowledge, none of them has considered the problem of self-deprecating sarcasm, which is a special category of sarcasm, mainly used by the users to deprecate or criticize themselves using sarcastic phrases. In this paper, we propose a novel self-deprecating sarcasm detection approach using an amalgamation of rule-based and machine learning techniques. The rule-based techniques aim to identify candidate self-around tweets, whereas machine learning techniques are used for feature extraction and classification. A total number of 11 features, including six self-deprecating features and five hyperbolic features are identified to train three different classifiers - decision tree, naïve Bayes, and bagging. The proposed approach is evaluated over a Twitter dataset containing 107536 tweets, and compared with some state-of-the-art methods for sarcasm detection.
Live Traffic Data Analysis Using Stream Processing

The increasing digitalization in the traffic infrastructure offers a great potential to optimize traffic flows, to save costs, and to improve the CO2 balance. Achieving this requires the use of scalable, high-performance software environments that process live traffic data with minimal latency. However, there are no standard solutions that work out of the box. Instead, technology stacks of complex components must be assembled, configured, and deployed from a large and heterogeneous set of available building blocks. Since there are no guidelines on which particular software to use in which configuration for specific use cases, it is extremely difficult to build such complex architectures from scratch. Nevertheless, many application areas, including traffic data analysis, have domain-specific requirements, which makes it possible to close these gaps on the basis of further research. Following this idea, we analyze how typical applications of traffic data analysis can be implemented using stream processing technologies in order to find reusable solutions that can be used as blueprints for the design of applications with similar requirements. Therefore, a number of typical use cases will be analyzed, implemented and benchmarked on the basis of various stream processing architectures. This way, specific levers are to be found to systematically increase the performance. Our first results show significant performance differences between different software solutions and architectures.
Data and Deep Models Applied to Cyber Security Data Analysis

We present initial work that applies big data and deep models to a cyber security data analysis with a use case approach. We explored new technologies such as BDP (Big Data Plat-form) as a service on the Amazon AWS system and Lexical Link Analysis (LLA). BDP provides various analytics in near real-time to help decision makers respond to threats and in a timely manner. We also used LLA as an example of deep models and a data-driven unsupervised ML method that can improve cyber decision making.
Towards Building a Human Perception Knowledge for Social Sensation Analysis

With the development of social network services, various phenomena can be shared easily and rapidly through human natural language, including not only natural, but also social-cultural phenomena. Consequently, analyses of social media have appreciated in value for understanding human behaviors to grasp public interests or sentiments, as both the medium and outcome of human experiences. From the state of the art psychology and neuroscience, human behaviors, regarding both physical and linguistic aspects, are mostly dependent on sensory perceptions under the realm of the subconscious. Even though sensation is the most fundamental element to understand human behaviors, the rack of background resources make it hard to study the social sensation comparing with the sentimental or opinion mining. This paper focuses on building sensation knowledges to obtain useful human perceptual experiences in natural language expressions, as a requisite for the social sensation analysis. We try to approach the constructing lexicons as a sensation knowledge from two viewpoints, such as a deep learning and lexicon based methods. Then we classify social media text based on the lexicons with considering a part of speech as well as semantic meanings of each word. Finally, we identify which knowledge has a good performance to distinguish sensation expressions from social media data in terms of accuracy and and F-score.
A Query Anonymization Approach using Ontology Mappings

In this paper, we propose an ontology matching mechanism for SPARQL query anonymization in a cooperative SPARQL query editing system. To provide a privacy-protection of SPARQL queries on the system, we proposed a mapping-based conversion of a query to a “semantically equivalent or very similar” query by using another ontologies. The mapping-based conversion uses ontology mappings to anonymize what the user is investigating in the query. Our mechanism uses two measures such as graph distance and semantic similarity, and also allows us to check anonymized queries whether it is a query according to user’s preference by selecting the mappings from mapping candidates.
Remote sensing image restoration: adaptive reciprocal cell recovery technique

This study aims to study the reason of acquisition and degradation of remote sensing image system and select the most appropriate de-blurring method in the process of repairing the adaptive reciprocal cell. was used to find out the adaptive reciprocal cell in the image system and regularization method was used to perform image restoration. This paper applied non-local model, TV model and ARCTV model to perform de-blurring and the Matlab software experimental platform for result analysis and comparison. It was found that the SNR value of the ARCNLM model was higher than that of the other models and the MSE value of the ARCNLM model was lower than that of the other models, suggesting that the de-blurring effect of the ARCNLM model was better, leaving images with clear edges and no folding phenomenon. The improved ARCNLM model had better de-blurring effects and could be promoted for wide application.DOI: http://dx.doi. org/10.5755/j01.itc. 47.4. 20939
On the Collaborative Governance of Decentralized Edge Microclouds with Blockchain-Based Distributed Ledgers

Today's commercial model for edge computing services consists in lightweight devices at the network edge connected through the Internet to remote cloud data centers. Microclouds are an alternative vision of edge computing, where the cloud infrastructure runs at the network edge leveraging decentralized resource contributions of a community. But current attempts to build such microclouds lack a collaborative governance system to operate successfully. In this paper we discuss the opportunity to implement with blockchain technologies key services to enable the decentralized collaborative governance of microclouds. A multiagent approach could further contribute to improve the efficiency in the decision making in the collaborative governance service.
Secure communications with untrusted relays: a multi-pair two-way relaying approach

Physical-layer security is usually ensured by the injection of artificial noise (AN) at the legitimate source or destination when relays are cooperative but untrusted. In this work, in order to avoid the power-consuming AN at the legitimate user pairs, the authors investigate multi-pair two-way relaying as a solution for secure transmission in wireless relay networks with untrusted relays. They formulate a generalised problem for the joint design of cooperative beamforming (CB) and AN at relays to maximise the secrecy sum-rate in wireless networks with multi-pair legal users, multiple untrusted relays as well as multiple non-colluding eavesdroppers. Further, they reformulate, approximate and relax the original non-convex problem into a sequence of convex sub-problems based on semi-definite relaxation (SDR) and Taylor series approximation. Particularly, for the reformulated problem they prove that the SDR is in fact tight, and hence propose an iterative algorithm for CB and AN design at the relays. In the simulations, they demonstrate the fast convergence and high achievable secrecy sum-rate of the proposed algorithm. Also, they simulate and discuss the impact of the number of user pairs on the secrecy sum-rate, and reveal the change of performance gain with the number of multiple user pairs.
On the use of contextual information for robust colour-based particle filter tracking

Color-based particle filters have emerged as an appealing method for targets tracking. As the target may undergo rapid and significant appearance changes, the template (i.e. scale of the target, color distribution histogram) also needs to be updated. Traditional updates without learning contextual information may imply a high risk of distorting the model and losing the target. In this paper, a new algorithm utilizing the environmental information to update both the scale of the tracker and the reference appearance model for the purpose of object tracking in video sequences has been put forward. The proposal makes use of the well-established color-based particle filter tracking while differentiating the foreground and background particles according to their matching score. A roaming phenomenon that yields the estimation to shrink and diverge is investigated. The proposed solution is tested using publicly available benchmark datasets where a comparison with six state-of-the-art trackers has been carried out. The results demonstrate the feasibility of the proposal and lie down foundations for further research of complex tracking problems.
Cooperative Resource Allocation in Cognitive Wireless Powered Communication Networks with Minimum Rate Requirements

This paper considers an uplink overlay cognitive wireless power communication network (CWPCN) with one primary base station (PBS), one secondary base station (SBS), one primary user (PU) and one wireless powered secondary user (SU). The transmission time is assumed to consist of two phases. During the first phase, the PBS transmits data to the PU with the help from the SBS, and the SU harvests energy using the received RF signals. During the second phase, the PBS keeps transmitting data to the PU while the SU transmits data to the SBS using the harvested energy. The quality of service of the PU is guaranteed by satisfying the minimum rate constraint which requires that the PU rate is higher than that without the help from the SBS. The problem of resource allocation to maximize the SU rate under the PU minimum rate requirement is investigated and an iterative scheme is proposed to solve the problem. It is shown that our proposed scheme greatly improves the SU rate compared to the reference schemes.
Sensor Data Preprocessing, Feature Engineering and Equipment Remaining Lifetime Forecasting for Predictive Maintenance

Analytics based on sensor data is gradually becoming an industry standard in equipment maintenance. However, it involves several challenges, such as sensor data preprocessing, feature engineering and forecasting model development. Due to work in progress, this paper is mainly focused on sensor data preprocessing, which plays a crucial role in predictive maintenance due to the fact, that real-world sensing equipment usually provides data with missing values and a considerable amount of noise. Obviously, poor data quality can render practically useless all the following steps of data analysis. Thus, many missing data imputation, outlier filtering, and noise reduction algorithms were introduced in the literature. Streaming sensor data can be represented in a form of univariate time series. This paper provides an overview of common univariate time series preprocessing steps and the most appropriate methods, with consideration of the field of application. Sensor data from different sources comes in different scales and should be normalized. Thus, the comparison of univariate time series normalization techniques is given. Conventional algorithm quality metrics for each of the preprocessing steps are described. Basic sensor data quality assessment approach is suggested. Moreover, the architecture of a sensor data preprocessing module is proposed. The overview of time series-specific feature engineering techniques is given. The brief enumeration of considered forecasting approaches is provided.