Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating a Malay Sentiment Lexicon Based on WordNet

Sentiment lexicon is a list of vocabularies that consists of positive and negative words. In opinion mining, sentiment lexicon is one of the important source in text polarity classification task in sentiment analysis model. Studies in Malay sentiment analysis is increasing since the volume of sentiment data is growing on social media. Therefore, requirement in Malay sentiment lexicon is high. However, Malay sentiment lexicon development is a difficult task due to the scarcity of Malay language resource. Thus, various approaches and techniques are used to generate sentiment lexicon. The objective of this paper is to develop Malay sentiment lexicon generation algorithm based on WordNet. In this study, the method is to map the WordNet Bahasa with English WordNet to get the offset value of a seed set of sentiment words. The seed set is used to generate the synonym and antonym semantic relation in English WordNet. The highest result achives 86.58% agreement with human annotators and 91.31% F1-measure in word polarity classification. The result shows the effectiveness of the proposed algorithm to generate Malay sentiment lexicon based on WordNet.
Increased Pinch Force of Body Powered Prosthetic Hand with Pneumatic Force Servo

In the Netherlands, there are around 3750 patients suffering from an upper limb deficiency. Many different prosthetic hands have been designed to give these people the function of their hand back. Of the people who own such a prosthetic hand, about forty percent does not wear their hand and about half of the wearers does not use all of the functionalities the hand offers. The main reasons for this is that the hands have too much weight, are too complex to control and offer no feedback. The goal of this study is to create a low-weight prosthetic hand that is easy to control and provides feedback to the user. Therefore, this study presents a Force Demand Valve (FDV) that can be used with the Delft Cylinder Hand (DCH). This FDV increases the pinch force to operation force ratio by more than 200% with the use of pressurized CO2. This mechanism does not change the size or the weight of the hand itself, because it is attached near the elbow. Also, both the closing speed and the feedback to the user have not noticeably changed with the use of the FDV, compared to the DCH being used without FDV. The main disadvantage of the mechanism, is that the gas consumption is high. This consumption, however, can be decreased by making the FDV smaller, by increasing the activation force of the FDV and by providing a threshold in which the FDV will not exhaust gas when constant forces are applied for a longer period. Concluding from a user experiment, the mechanism designed in this study will make the DCH available for daily use to a wider range of people compared to the DCH without FDV.
Unsupervised Segmentation and Texture Visualization of 3D Medical Images using Textural Features

In this paper we implement and compare textural analysis methods, for 3D medical image segmentation and texture visualization. We implement and evaluate an unsupervised system that makes use of these features in order to automatically segment 3D medical images. There are many algorithms that try to extract features that represent the texture of an image. Moreover due to technological advancement, the use of 3D imaging for medical purposes is wide spread. Thus there is a need for robust automated methods that segment those images. Due to the different nature of different imaging techniques (MRI, Ultra Sound, CT et cetera) the resulted images have different characteristics and produce different textures. Thus for every application and image acquiring technique, different features work. In this paper, we use features that describe 3D texture and feature selection techniques in order to create a general method to segment 3D medical images. The algorithms developed in this project are evaluated on MRI and Ultra Sound images. Moreover, we show the importance of the produced clustering in the visualization of the different textures in an image.
A Security-Oriented Analysis of Web Inclusions in the Italian Public Administration

Abstract Modern web sites serve content that browsers fetch automatically from a number of different web servers that may be placed anywhere in the world. Such content is essential for defining the appearance and behavior of a web site and is thus a potential target for attacks. Many public administrations offer services on the web, thus we have entered a world in which web sites of public interest are continuously and systematically depending on web servers that may be located anywhere in the world and are potentially under control of other governments. In this work we focus on these issues by investigating the content included by almost 10000 web sites of the Italian Public Administration. We analyse the nature of such content, its quantity, its geographical location, the amount of dynamic variations over time. Our analyses demonstrate that the perimeter of trust of the Italian Public Administration collectively includes countries that are well beyond the control of the Italian government and provides several insights useful for implementing a centralized monitoring service aimed at detecting anomalies.
Solitor : runtime verification of smart contracts on the Ethereum network

Ethereum is a decentralized blockchain platform. On this platform smart contracts are deployed and executed. Smart contracts are also called "executable code on the blockchain". It can perform calculations, store information and send/receive transactions. Since these smart contracts control a virtual currency, vulnerabilities can be costly. The code of smart contracts is public and changes to it are impossible since they are committed to the blockchain. Many techniques and tools can be used to detect these vulnerabilities. Formal verification proves a specification against all possible input on a certain contract, while testing only guarantees correct output for a given input. This thesis introduces the tool Solitor. Solitor is short for Solidity (runtime) monitor, and uses runtime verification as a technique to make smart contracts more secure. It enables users to specify the behaviour of a contract using annotations. It is a tool developed specifically for smart contracts on the Ethereum network. We define an annotation language to specify the requirements on a smart contract. Solitor can parse and translate these annotations in Solidity contracts to Solidity code which checks the annotation at runtime.
Robust Hierarchical Deep Learning for Vehicular Management

Congestion detection is an important aspect of vehicular management. However, most of the existing algorithms are insufficient for real applications. Traditional features are not discriminative which results in rather poor performance under complex scenarios. The deep features can better represent high-level information, but the training of deep network for regression is difficult. To promote the congestion detection, a robust hierarchical deep learning is proposed for the task. In this method, a deep network is designed for hierarchical semantic feature extraction. Different from traditional deep regression networks, which usually directly utilize mean squared error as loss function, a robust metric learning is employed to effectively train the network. Based on this, multiple networks are combined together to further improve the generalization ability. Extensive experiments are conducted and the proposed model is confirmed to be effective.
Towards the Construction of a Gold Standard Biomedical Corpus for the Romanian Language

Gold standard corpora (GSCs) are essential for the supervised training and evaluation of systems that perform natural language processing (NLP) tasks. Currently, most of the resources used in biomedical NLP tasks are mainly in English. Little effort has been reported for other languages including Romanian and, thus, access to such language resources is poor. In this paper, we present the construction of the first morphologically and terminologically annotated biomedical corpus of the Romanian language (MoNERo), meant to serve as a gold standard for biomedical part-of-speech (POS) tagging and biomedical named entity recognition (bioNER). It contains 14,012 tokens distributed in three medical subdomains: cardiology, diabetes and endocrinology, extracted from books, journals and blogposts. In order to automatically annotate the corpus with POS tags, we used a Romanian tag set which has 715 labels, while diseases, anatomy, procedures and chemicals and drugs labels were manually annotated for bioNER with a Cohen Kappa coefficient of 92.8% and revealed the occurrence of 1877 medical named entities. The automatic annotation of the corpus has been manually checked. The corpus is publicly available and can be used to facilitate the development of NLP algorithms for the Romanian language.
Determination of Input Parameters of the Neural Network Model, Intended for Phoneme Recognition of a Voice Signal in the Systems of Distance Learning

The article is devoted to the problem of voice signals recognition means introduction in the system of distance learning. The results of the conducted research determine the prospects of neural network means of phoneme recognition. It is also shown that the main diculties of creation of the neural network model, intended for recognition of phonemes in the system of distance learning, are connected with the uncertain duration of a phoneme-like element. Due to this reason for recognition of phonemes, it is impossible to use the most eective type of neural network model on the basis of a multilayered perceptron, at which the number of input parameters is a xed value. To mitigate this shortcoming, the procedure, allowing to transform the non-stationary digitized voice signal to the xed quantity of mel-cepstral coecients, which are the basis for calculation of input parameters of the neural network model, is developed. In contrast to the known ones, the possibility of linear scaling of phoneme- like elements is available in the procedure. The number of computer experiments conrmed expediency of the fact that the use of the oered coding procedure of input parameters provides the acceptable accuracy of neural network recognition of phonemes under near-natural conditions of the distance learning system. Moreover, the prospects of further research in the eld of development of neural network means of phoneme recognition of a voice signal in the system of distance learning is connected with an increase in admissible noise level. Besides, the adaptation of the oered procedure to various natural languages, as well as to other applied tasks, for instance, a problem of biometric authentication in the banking sector, is also of great interest.
Optimization algorithms for data
 analysis

where f is as in (1.0.1), ψ : Rn → R is a function that is usually convex and 12 usually nonsmooth, and λ > 0 is a regularization parameter.1 We refer to (1.0.2) as 13 a regularized minimization problem because the presence of the term involving ψ 14 induces certain structural properties on the solution, that make it more desirable 15 or plausible in the context of the application. We describe iterative algorithms 16 that generate a sequence {x}k=0,1,2,. of points that, in the case of convex objective 17 functions, converges to the set of solutions. (Some algorithms also generate other 18 “auxiliary” sequences of iterates.) 19 We are motivated to study problems of the forms (1.0.1) and (1.0.2) by their 20 ubiquity in data analysis applications. Accordingly, Section 2 describes some 21 canonical problems in data analysis and their formulation as optimization prob22 lems. After some preliminaries in Section 3, we describe in Section 4 algorithms 23 that take step based on the gradients ∇f(xk). Extensions of these methods to 24 the case (1.0.2) of regularized objectives are described in Section 5. Section 6 de25 scribes accelerated gradient methods, which achieve better worst-case complexity 26 than basic gradient methods, while still only using first-derivative information. 27 We discuss Newton’s method in Section 7, outlining variants that can guarantee 28
Exploiting Full-Duplex Two-Way Relay Cooperative Non-Orthogonal Multiple Access

In this paper, a novel full-duplex cooperative non-orthogonal multiple access (FD CNOMA) system is proposed, where users intend to exchange messages with the assistance of a decode-and-forward relay. To characterize the potential performance gain brought by the proposed FD CNOMA scheme, the outage probability and ergodic rate are analyzed. Specifically, the closed-form expressions for the outage probabilities, diversity orders, ergodic rates, and system throughputs in delay-limited and delay-tolerant transmission modes are derived under the realistic assumption of imperfect self-interference cancellation. Furthermore, to present the comprehensive performance evaluation, both perfect and imperfect successive interference cancellations (SICs) are taken into consideration. Simulations are performed to validate the accuracy of the derivation results and to illustrate the outstanding performance of the proposed scheme in low signal-to-noise ratio region compared with half-duplex CNOMA system and cooperative orthogonal multiple access system. Our results show that under the conditions of both perfect and imperfect SICs, outage probability floors and ergodic rate ceilings exist for the proposed FD CNOMA scheme due to the inter-user interference among superimposed NOMA signals and the residual self-interference caused by the imperfect self-interference cancellation.
SYSTEM FOR WEB RESOURCES CONTENT STRUCTURING AND RECOGNIZING WITH THE MACHINE LEARNING ELEMENTS

Context. A large number of web resources of different organizations requires checking of relevance and correctness of the content, in particular, concerning characteristics of the organization, staff, etc. For this, it is necessary to develop a system of the automated content analysis. This task causes the need to develop a method and software for structuring and recognizing of web resources content. Existing parsing systems do not provide solving of the specified task, since they do not contain elements of machine learning. The object of the research is the process of automated analysis of the web resources content. Objective. The goal of the work is the creation of the system for web resources content structuring and recognizing. Method. The system of structuring and recognizing of text content of web resources with elements of machine learning is considered. Models of the system functioning are proposed. The architecture for realizing of software system for structuring and recognizing of text content of web resources is developed. Example of implementation of the model of developed system for structuring, recognizing and revealing of outdated and incorrect information about personnel on the web resource of educational institution is given. Results. The developed software may be used by support services in order to update and correct the information content. Conclusions. The system of structuring and recognizing of content of web resources with the machine learning elements has been considered. The proposed system compared with the known ones, ensures automated content structuring, recognizing of outdated, non-relevant or wrong information. Represented example of the structuring and recognizing of outdated and incorrect information on the website of educational institution confirms the effectiveness of the proposed system.
Simulation of Data Communication System taking into Account Dynamic Properties

This paper continues the study, presented at the 8th EUROSIM Congress on Modeling and Simulation and devoted to creation of algorithm and simulation model of network functioning, taking into account dynamic characteristics of the network in condition of variable relationship signal-to-noise. Simulation algorithm was augmented for adequately representation of the state of the real network i.e. possible changes of topology due to the link failures and disabling individual nodes. It is possible to expand the capabilities of the model presented in the 8th Congress as a simulation model of Information Flow on Transport Layer of Open System Interconnection Model. The current version of the model realizes input of the adjacency matrix describing the network topology, the algorithm of the path search by Dijkstra on the network level, and simulation of the loss of connection. So the main goal of the new paper is to bring the structure of the model to the structure of the real network and to check the possibility of transferring a given amount of information in conditions of interference by means of evaluation of coefficient of readiness for Data Communication System.
Automatic Droplet Sequence Generation for Microfluidic Networks With Passive Droplet Routing

Droplet-based microfluidic devices are a well-established and highly potential Labs-on-Chip technology as droplets are especially suited to encapsulate biological samples like cells, proteins, or DNA. These droplets are injected in a continuous phase and flow through closed microchannels to modules executing operations on the droplets—eventually realizing a (bio-)chemical experiment. Moreover, this technology even allows for the realization of multiple experiments on a single device by letting droplets take different paths through the microfluidic network. This requires, however, a mechanism to route the droplets along these paths. To this end, the concept of passive droplet routing has been suggested which entirely avoids complex valves or switches and, instead, realizes the routing by exploiting the hydrodynamic effect that a droplet will always flow along the path with the highest volumetric flow rate. Since droplets themselves affect the volumetric flow rate, a dedicated sequence of droplets can define what path is taken and, hence, what experiment is executed. However, determining such a droplet sequence is a nontrivial task, as it is nonobvious how much droplets are needed, when to inject them, and how they are interacting. In this paper, we are addressing this issue by providing, for the first time, an automatic method for the generation of droplet sequences realizing the desired experiments on a given network. Evaluations confirm the practicability of the proposed solution. Moreover, the suitability of the obtained droplet sequences is additionally validated through simulations on the 1D analysis model.
Analysis of collaboration networks in OpenStreetMap through weighted social multigraph mining

ABSTRACT This paper aims to qualify the behaviour of contributors to OpenStreetMap (OSM), a volunteered geographic information (VGI) project, through a multigraph approach. The main purpose is to reproduce contributor’s interactions in a more comprehensive way. First, we define a multigraph that combines existing spatial collaboration networks from the literature with new graphs that illustrate collaboration based on specific aspects of the VGI modes of contribution through semantics, geometry and topology. Indeed, the ways that contributors interact with one another through editing, completion, or even consumption may provide additional information on each user’s operation mode and therefore, on the quality of the contributed data. Social collaborations drawn from indirect criteria – for example, comparisons between contributors’ activity areas – can also be contemplated under another network. Second, the resulting multigraph is analysed using data mining approaches to characterise individuals and identify behavioural groups. The implementation of a multiplex network based on an OSM data sample and an initial analysis make it possible to identify useful behaviours for data qualification. The initial results characterise some contributors as pioneers, moderators and truthful contributors, according to their special roles in the graphs. Mapping elements that include these contributors’ participation are likely to be reliable data
Easily implementable time series forecasting techniques for resource provisioning in cloud computing

Workload predictions in cloud computing is obviously an important topic. Most of the existing publications employ various time series techniques, that might be difficult to implement. We suggest here another route, which has already been successfully used in financial engineering and photovoltaic energy. No mathematical modeling and machine learning procedures are needed. Our computer simulations via realistic data, which are quite convincing, show that a setting mixing algebraic estimation techniques and the daily seasonality behaves much better. An application to the computing resource allocation, via virtual machines, is sketched out.
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives

During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the robotics literature. One of their most prominent features is that, in addition to extracting a mean trajectory from task demonstrations, they provide a variance estimation. The intuitive meaning of this variance, however, changes across different techniques, indicating either variability or uncertainty. In this paper we leverage kernelized movement primitives (KMP) to provide a new perspective on imitation learning by predicting variability, correlations and uncertainty using a single model. This rich set of information is used in combination with the fusion of optimal controllers to learn robot actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task. We showcase our approach in a painting task, where a human user and a KUKA robot collaborate to paint a wooden board. The task is divided into two sub-tasks and we show that the robot becomes compliant (hence safe) outside the training regions and executes the two sub-tasks with optimal gains otherwise.
Gaps in Information Access in Social Networks?

The study of influence maximization in social networks has largely ignored disparate effects these algorithms might have on the individuals contained in the social network. Individuals may place a high value on receiving information, e.g. job openings or advertisements for loans. While well-connected individuals at the center of the network are likely to receive the information that is being distributed through the network, poorly connected individuals are systematically less likely to receive the information, producing a gap in access to the information between individuals. In this work, we study how best to spread information in a social network while minimizing this access gap. We propose to use the maximin social welfare function as an objective function, where we maximize the minimum probability of receiving the information under an intervention. We prove that in this setting this welfare function constrains the access gap whereas maximizing the expected number of nodes reached does not. We also investigate the difficulties of using the maximin, and present hardness results and analysis for standard greedy strategies. Finally, we investigate practical ways of optimizing for the maximin, and give empirical evidence that a simple greedy-based strategy works well in practice.