Miracle-dz/UniFAR-FTUs huggingface dataset:
Reviews

Back in 2011, I was researching the use of arc length as a measure of stock volatility. Having had a reasonable amount of experience with time series analysis, the stochastic nature of something like daily price changes was familiar territory, but financial mathematics was still somewhat of an unknown quantity. In an effort to become more savvy in such matters, I began perusing the literature and consequently found a number of texts that added to my general knowledge. Among these were [1], [2], and [3], but the one in particular that really captured my attention was Probability Theory in Finance: A Mathematical Guide to the Black–Scholes Formula by Seán Dineen. Over the next several months, I read various parts of the work, but ended up having to put it aside as the volatility investigations got superseded by other pressing projects. In 2013, however, I picked up the new second edition with a renewed interest (and a freer calendar) and set myself the strict goal of reading it cover to cover in a year and then writing a thorough review. As the title suggests, the central purpose of the book is to derive, interpret, and utilize the Black–Scholes formula for pricing a call option. The work is based on a onesemester undergraduate course given to economics and finance students at University College Dublin. While a certain familiarity with the basic principles of finance is certainly required of the readership, Dineen has in actuality created a very sophisticated mathematical text. Specifically, its main topics include measure theory, conditional expectation, martingales, stochastic processes, Brownian motion, and the Itô integral. These topics are handled with great care and are gradually developed throughout the chapters without automatically assuming that the reader’s background extends beyond integral calculus and differential equations. The first two chapters start off the book by introducing some basic ideas about money and fair games. Specifically, Chapter 1 discusses risk and interest and their relationship to both present and discounted values of money. Chapter 2 discusses both fair and zero-sum games and what it means to calculate their expected winnings. By introducing the idea of expected winnings early on, Dineen is laying the groundwork for the more formal concept of expected value which is used extensively in later parts of the book. Chapter 3 gets into σ -fields and how one can glean information from filtrations. Dineen’s motive here is to show how an increasing sequence of nested fields can model the growing information associated with the passage of time, which is very important in the financial world. The main example he uses is the measurable space ( ,F) where denotes the set of all possible future prices that may be taken by a given
Winventor: A Machine-driven Approach for the Development of Winograd Schemas

: The Winograd Schema Challenge — the task of resolving pronouns in certain carefully-constructed sentences — has recently been proposed as a basis for a novel form of CAPTCHAs. Such uses of the task necessitate the availability of a large, and presumably continuously-replenished, collection of available Winograd Schemas, which goes beyond what human experts can reasonably develop by themselves. Towards tackling this issue, we introduce Winventor , the ﬁrst, to our knowledge, system that attempts to fully automate the development of Winograd Schemas, or at least to considerably help humans in this development task. Beyond describing the system, the paper presents a series of three studies that demonstrate, respectively, Winventor ’s ability to replicate existing Winograd Schemas from the literature, automatically develop reasonable Winograd Schemas from scratch, and aid humans in developing Winograd Schemas by post-processing the system’s suggestions.
On Analyzing Third-party Tracking via Machine Learning

: Nowadays, websites rely on services provided by third party sites to track users and offer personalized experiences. However, this practice threatens the privacy of individuals through the use of valuable information to create a digital personal proﬁle. The existing client-side countermeasures to protect privacy, exhibit performance issues, mainly due to the use of blacklisting mechanisms (list of resources to be ﬁltered out). In this paper, we study the use of machine learning methods to classify the thirdy-party privacy intrusive resources (trackers). To this end, we ﬁrst downloaded (browsing Alexa’s Top 10 websites for each category like sports, shopping etc.) a dataset of 1000 web resources split into functional and tracking , and then we identiﬁed suitable metrics to distinguish between the two classes. In order to evaluate the effectiveness of the proposed metrics we have compared the performances of several machine learning models based on supervised learning among the most used in literature. As a result, we obtained that the Random Forest can classify functional and tracking resources with an accuracy of 91%.
Exploring Vulnerabilities in Solidity Smart Contract

A smart contract is a decentralized program executed automatically, reliably, and transparently on a blockchain. It is now commonly used in financial-related applications, which require heavily secure operations and transactions. However, like other programs, smart contracts might contain some flaws. Thus, developers are encouraged to write secure smart contracts, and some approaches are proposed to detect vulnerabilities of smart contracts before deployment. Due to the immutability property of a blockchain, developers cannot modify the smart contract even though there is a vulnerability which may cause financial losses. In this paper, we propose the comparison of vulnerability detection tools to deployed smart contracts on the Ethereum blockchain. We also present the analysis of the state of vulnerabilities in smart contracts as well as their char-
Improving Face Recognition Methods based on POEM Features

: POEM descriptors has been successfully used for face recognition. The usual way how the descriptor is utilized consists in constructing POEM features in the rectangular non-overlapping regions covering the whole image. The features created in the regions are then concatenated into one long vector representing the face. We propose an enhancement of this method using automatic key-point identiﬁcation strategies. In our approach, the image features are created in the detected key-points. We also employ a more complex matching procedure that compares the features individually. This method is efﬁcient particularly when the number of training samples is small and therefore neural network based methods fail, because they do not have enough training data. The proposed approach is evaluated on three standard face corpora. We also study the inﬂuence of several parameters of the method on the overall performance. The obtained results show that the combination of POEM features with the automatic point identiﬁcation and a more sophisticated matching algorithm brings signiﬁcant improvement over the baseline method.
Distributed Defence of Service (DiDoS): A Network-layer Reputation-based DDoS Mitigation Architecture

The predominant strategy for DDoS mitigation involves resource enlargement so that victim services can handle larger demands, however, with growing attack strengths, this approach alone is unsustainable. This paper proposes DiDoS (Distributed Defence of Service), a collaborative DDoS defence architecture that leverages victim feedback to build network-level sender reputations that are applied to identify and thwart attack traffic - thus alleviating the need for resource enlargement. Since attack traffic is dropped at points of contention in the Internet, (rather than rote blocking at source) DiDoS reduces the impact of false positives and enables the traversal of legitimate traffic from said devices across the Internet. Through anti-spoofing protection and preferential treatment of DiDoS-compliant devices, DiDoS offers adoption incentives that help offset the Tragedy of the Commons effect of DDoS mitigation, which commonly sees non-victim intermediary entities benefit little from DDoS defence expenditure. In this paper, the tenets and fundamentals of the architecture are described, before being analysed against the presented threat model. Simulation results, demonstrating the effectiveness of the reputation convergence of the scheme, in the use-case of a local access network, are also presented and discussed.
Mitigate Catastrophic Forgetting by Varying Goals

: Catastrophic forgetting occurs because neural network learning algorithms change connections to learn a new skill which encodes previously acquired skills. Recent research suggests that encouraging modularity in neural networks may overcome catastrophic forgetting because it should reduce learning interference. However, manually constructing modular topology is hard in practice since it involves expert design and trial and error. Therefore, an automatic approach is needed. Kashtan et al. ﬁnd that evolution under an environment that changes in a modular fashion can lead to the spontaneous evolution of modular network structure. However, goals in their research are made of a different combination of subgoals, while real-world data is rarely perfectly separable. Therefore, in this paper, we explore the application of such approach to mitigate catastrophic forgetting in a slightly practical situation, that is applying it to classiﬁcation of small sized real images and applying it to the increment of goals. We ﬁnd that varying goals can improve catastrophic forgetting in a CIFAR-10 based classiﬁcation problem. We ﬁnd that when learning a large set of goals, a relatively small switching interval is required to have the advantage of mitigating catastrophic forgetting. On the other hand, when learning a small set of goals, an appropriate large switching interval is preferred since this less worsens the advantage and also can improve accuracy.
Evaluation of Reinforcement Learning Methods for a Self-learning System

: In recent years, interest in self-learning methods has increased significantly. A challenge is to learn to survive in a real or simulated world by solving tasks with as little prior knowledge about itself, the task, and the environment. In this paper, the state of the art methods of reinforcement learning, in particular, Q-learning, are analyzed regarding applicability to such a problem. The Q-learning algorithm is completed with replay memories and exploration functions. Several small improvements are proposed. The methods are then evaluated in two simulated environments: a discrete bit-flip and a continuous pendulum environment. The result is a lookup table of the best suitable algorithms for each type of problem.
A User Independent Method for Identifying Hand Gestures with sEMG

: We propose a method to determine hand gestures using sEMG (surface Electromyogram) measured from the forearm. The detection method uses the LSTM (Long Short Term Memory) model of RNN (Recurrent Neural Network). Although the conventional method requires the learning data of the user, this is a method that an unspecified number of users can use immediately by enhancing the data. We have confirmed that the accuracy does not change even if the mounting position of the sensor is shifted. We have shown the effectiveness of the data enhancement by numerical experiments.
Distance Metric Learning using Particle Swarm Optimization to Improve Static Malware Detection

: Distance metric learning is concerned with ﬁnding appropriate parameters of distance function with respect to a particular task. In this work, we present a malware detection system based on static analysis. We use k nearest neighbors (KNN) classiﬁer with weighted heterogeneous distance function that can handle nominal and numeric features extracted from portable executable ﬁle format. Our proposed approach attempts to specify the weights of the features using particle swarm optimization algorithm. The experimental results indicate that KNN with the weighted distance function improves classiﬁcation accuracy signiﬁcantly.
Mitigating Vocabulary Mismatch on Multi-domain Corpus using Word Embeddings and Thesaurus

: Query expansion is an extensively researched topic in the ﬁeld of information retrieval that helps to bridge the vocabulary mismatch problem, i.e., the way users express concepts differs from the way they appear in the corpus. In this paper, we propose a query-expansion technique for searching a corpus that contains a mix of terminology from several domains - some of which have well-curated thesauri and some of which do not. An iterative fusion technique is proposed that exploits thesauri for those domains that have them, and word embeddings for those that do not. For our experiments, we have used a corpus of Medicaid healthcare policies that contain a mix of terminology from medical and insurance domains. The Uniﬁed Medical Language System (UMLS) thesaurus was used to expand medical concepts and a word embeddings model was used to expand non-medical concepts. The technique was evaluated against elastic search using no expansion. The results show 8% improvement in recall and 12% improvement in mean average precision.
Implementation of a Memetic Algorithm to Optimize the Loading of Kilns for the Sanitary Ware Production

One of the most important aspects to be considered in the production lines of sanitaries is the optimization in the use of critical resources such as kilns (industrial furnaces) due to the complexity of their management (they are turned on twice a year) and the costs incurred. The manufacturing processes of products within these kilns require that the capacity be maximized by trying to reduce downtime. In this sense, Artificial Intelligence provides bioinspired and evolutionary optimization algorithms which can handle these complex variable scenarios, the memetic algorithms being one of the main means for task scheduling. In present investigation, and based on previous works of the authors, a memetic algorithm is presented for optimization in the loading of kilns starting from a real production line.
Model Smoothing using Virtual Adversarial Training for Speech Emotion Estimation using Spontaneity

: Speech-based emotion estimation increases accuracy through the development of deep learning. However, most emotion estimation using deep learning requires supervised learning, and it is difﬁcult to obtain large datasets used for training. In addition, if the training data environment and the actual data environment are signiﬁcantly different, the problem is that the accuracy of emotion estimation is reduced. Therefore, in this study, to solve these problems, we propose a emotion estimation model using virtual adversarial training (VAT), a semi-supervised learning method that improves the robustness of the model. Furthermore, research on the spontaneity of speech has progressed year by year, and recent studies have shown that the accuracy of emotion classiﬁcation is improved when spontaneity is taken into account. We would like to investigate the effect of the spontaneity in a cross-language situation. First, VAT hyperparameters were ﬁrst set by a preliminary experiment using a single corpus. Next, the robustness of the model generated by the evaluation experiment by the cross corpus was shown. Finally, we evaluate the accuracy of emotion estimation by considering spontaneity and showed improvement in the accuracy of the model using VAT by considering spontaneity.
Security of Digital Banking Systems in Poland: Users Study 2019

: Our aim of this study was to discover believes, behaviors, thoughts and habits of digital banking users in Poland, we wanted to understand their motivation and drivers while using electronic and mobile ﬁnancial services. Thanks to using Design Thinking research methodology we empathized users deeply and deﬁned personas - representing user groups of common features and way of thinking and acting. Our desk research and users interview resulted in the identiﬁcation of a number of aspects of e-banking, that can/should be taken into consideration by its users and, possibly, providers in order to assess the security of the service from the perspective of usability. We have interviewed 62 people in Poland (age span 16-72, different professions and familiarity level with e-banking solutions) in a form of qualitative study – in-depth survey (one hour per person) and discussed security issues with several Polish banks representatives. This paper is essence extract of full research conducted in this area. It presents and summarises the main assumptions and results.
Does Penis Size Influence Sexual Behaviour of Men who Have Sex with Men?

Abstract Penis size is a major body image concern for the majority of men in western nations, while in gay culture the penis has become a body part linked to sexual attractiveness and viability. The aim of this study was to reveal influence of the perceived penis size on sexual behaviour, condom use, sexually transmitted infections and men’s sexual positioning among men who have sex with men. In this cross-sectional study, the data were collected from consecutive men who have sex with men who attended Counselling for Sexually Transmitted Diseases at the City Institute for Skin and Venereal Diseases in Belgrade, Serbia. Out of 319 participants, 6.6% perceived their penis as "below average", 71.5% as "average", and 21.9% as "above average". In comparison with men with an average penis, men with below average penis were more frequently unsatisfied with their penis size (p < 0.05), more frequently lied to others about their penis size (p < 0.001) and took more frequently the passive sexual role (p < 0.05). Men with above average penis were more frequently satisfied with their penis size (p < 0.001), took more frequently the active sexual role (p < 0.01), they had more sexual partners (p < 0.05), more problems with a tight condom (p < 0.001) and more gonorrhoea/Chlamydia infections (p < 0.001) than men with an average penis. Perception of one’s penis size was associated with some aspects of sexual behaviour and the frequency of sexually transmitted diseases.
Classification of Visual Strategies in Physics Vector Field Problem-solving

: In this study, we taught 20 physics students two different visual strategies to graphically interpret the physical meaning of vector ﬁeld divergence. Using eye-tracking technology, we recorded students’ eye-movement behavior of both strategies when they were engaged in graphical vector ﬁeld representations. From the eye-tracking data we extracted the number of ﬁxations and saccadic direction and proposed a linear SVM model to classify strategies of problem-solving in the vector ﬁeld domain. The results show different gaze patterns for the two strategies, and the inﬂuence of vector ﬂow orientation on gaze-patterns. A high accuracy of 81 . 2% ( 0 . 11% ) has been achieved by testing the algorithm using cross-validation, i.e. that the algorithm is able to predict the strategy the student applies to judge the divergence of a vector ﬁeld. The results provide guiding tools for learning-effective instruction design and teachers gain beneﬁt from monitoring the students’ non-verbal level of performance and ﬂuency using each strategy. Apart from that, students would receive the objective feedback on their progress of learning.
From 3-valued Semantics to Supported Model Computation for Logic Programs in Vector Spaces

: We propose a linear algebraic approach to computing 2-valued and 3-valued completion semantics of ﬁnite propositional normal logic programs in vector spaces. We ﬁrst consider 3-valued completion semantics and construct the least 3-valued model of comp(DB), i.e. the iff (if-and-only-if) completion of a propositional normal logic program DB in Kleene’s 3-valued logic which has three truth values { t (true), f (false), ⊥ (undeﬁned) } . The construction is carried out in a vector space by matrix operations applied to the matricized version of a dualized logic program DB d of DB. DB d is a deﬁnite clause program compiled from DB and used to compute the success set P ∞ as true atoms and ﬁnite failure set N ∞ as false atoms that constitute the least 3-valued model I ∞ = ( P ∞ , N ∞ ) of comp(DB). We then construct a supported model of DB, i.e. a 2-valued model of comp(DB) by carefully assigning t or f to undeﬁned atoms not in P ∞ or N ∞ so that the resulting model is 2-valued and supported. The assigning process is guided by an atom dependency relation on undeﬁned atoms. We implemented our proposal by matrix operations and conducted an experiment with random normal logic programs which demonstrated the effectiveness of our linear algebraic approach to computing completion semantics.
A Weak-supervision Method for Automating Training Set Creation in Multi-domain Aspect Sentiment Classification

: Aspect Based Sentiment Analysis (ABSA) is receiving growing attention from the research community because it has applications in several real world use cases. To train deep learning models for ABSA in vertical domains may result a laborious process requiring a signiﬁcative human effort in creating proper training sets. In this work we present initial studies regarding the deﬁnition of an easy-to-use, ﬂexible, and reusable weakly-supervised method for the Aspect Sentence Classiﬁcation task of ABSA. Our method mainly consists in a process where templates of Labeling Functions automatically annotate sentences, and then the generative model of Snorkel constructs a probabilistic training set. In order to test effectiveness and applicability of our method we trained machine learning models where the loss function is informed about the probabilistic nature of the labels. In particular, we ﬁne-tuned BERT models on two famous disjoint SemEval datasets related to laptops and restaurants.
An AI using Construction Grammar: Automatic Acquisition of Knowledge about Words

: This paper deals with an AI implementation that uses knowledge in an original Construction Grammar (CG) format for deep understanding of text. CG is a means of processing knowledge pieces—aka constructions— that describe the form and meaning of text parts. Understanding consists in automatically ﬁnding in the text the knowledge that constructions contain and in creating knowledge networks that reﬂect the text information structure. Deeper understanding is achieved by propagating knowledge within the network, i.e. some constructions can share with others information about syntax, semantics, pragmatics and other text properties. A shortcoming of this information-rich method is limited coverage: only text for which a CG database is available can be understood; that database due to its complexity most often needs to be made up manually. The author attempts to increase the coverage by implementing automatic acquisition of word knowledge from sources such as an external (non-CG) knowledge base and formatting the knowledge as CG constructions. The resulting CG database has been used in evaluation experiments to understand the Winograd Schema (WS)—a test for AI. A 28% increase in accurate coverage and opportunities for further improvement are observed.
Patients to Mobilize Their Data: Secure and Flexible mHealth Delegation

: This work describes the development of a prototype of a secure and flexible delegation architecture, to be applied to an mHealth scenario where a mobile app is used for monitoring and coaching asthma patients. The motivation is the fact that mHealth apps are not security prepared and patients still have no trust in using them, on a regular basis. Nonetheless, patients can acknowledge mHealth potential and see the relevance of sharing/delegating health data to others, e.g., healthcare professionals, depending not only on the necessity and security, but also on the level of control they can have over it. This proposal empowers the patient to control, in a flexible, easy and secure way, fine-grained delegation features within a real mHealth setting.