Miracle-dz/UniFAR-FTUs huggingface dataset:
TEMPERED STABLE DISTRIBUTIONS AND APPLICATIONS TO FINANCIAL MATHEMATICS

We investigate the class of tempered stable distributions and present applications to financial mathematics. Our analysis of tempered stable distributions includes limit distributions, parameter estimation and the study of their densities. Afterwards, we deal with stock price models driven by tempered stable processes and are concerned with the existence of appropriate martingale measures as well as option pricing.
Shared Frailty Model for Left-Truncated Multivariate Survival Data

Left-truncated multivariate survival data are studied assuming a shared frailty model. A conditional model is studied where the interpretation of regression parameters is conditional on the frailty value. The survival of individuals in a group of related individuals depends on the frailty value and individuals with left-truncated life times have survived until their entry age and will carry information about their frailty value. This needs to be considered in the analysis. We propose a simple approximation to the likelihood function and compare it with an earlier proposed approximation. The conditional model is more relevant than a marginal model, for example the copula models, if the grouping variable may be a potential confounder. A real-life example from Guinea-Bissau, West Africa is used as illustration.
Stick to the script: lightweight recording and playback of live coding

"Live coding" describes the creation of programs by the instructor during lecture, in front of a live student audience. But how can the performance be captured for later use by students? We present a simple and lightweight, yet remarkably effective system for recording and playback of live coding. It has been used since 2016 in two instances of CS1 and one other introductory programming course, and most importantly has been received well by students.
Killing Two Birds With One Stone: Concurrent Ranking of Tags and Comments of Social Images

User-generated comments and tags can reveal important visual concepts associated with an image in Flickr. However, due to the inherent noisiness of the metadata, not all user tags are necessarily descriptive of the image. Likewise, comments may contain spam or chatter that are irrelevant to the image. Hence, identifying and ranking relevant tags and comments can boost applications such as tag-based image search, tag recommendation, etc. In this paper, we present a lightweight visual signature-based model to concurrently generate ranked lists of comments and tags of a social image based on their joint relevance to the visual features, user comments, and user tags. The proposed model is based on sparse reconstruction of the visual content of an image using its tags and comments. Through empirical study on Flickr dataset, we demonstrate the effectiveness and superiority of the proposed technique against state-of-the-art tag ranking and refinement techniques.
Improving students' engagement with large-team software development projects

Computer science and technology education should provide not only a strong theoretical foundation, but also problem solving, and communication and teamwork skills to prepare the students for careers. Including projects in curricula is a norm in many disciplines. However, projects are generally individual or based on small teams (two to five members). This paper presents my approach to teaching a capstone undergraduate computer technology course at the British Columbia Institute of Technology (BCIT) in the Computer System Technology (CST) Program in which a large class of students (maximum 22), organized into small teams work together and apply Agile software development practices to design, implement, integrate and test a large project. This model provides students with unique learning opportunities and experiences, as well as improving their soft skills, engagement and motivation.
A Co-Memory Network for Multimodal Sentiment Analysis

With the rapid increase of diversity and modality of data in user-generated contents, sentiment analysis as a core area of social media analytics has gone beyond traditional text-based analysis. Multimodal sentiment analysis has become an important research topic in recent years. Most of the existing work on multimodal sentiment analysis extracts features from image and text separately, and directly combine them to train a classifier. As visual and textual information in multimodal data can mutually reinforce and complement each other in analyzing the sentiment of people, previous research all ignores this mutual influence between image and text. To fill this gap, in this paper, we consider the interrelation of visual and textual information, and propose a novel co-memory network to iteratively model the interactions between visual contents and textual words for multimodal sentiment analysis. Experimental results on two public multimodal sentiment datasets demonstrate the effectiveness of our proposed model compared to the state-of-the-art methods.
From Greedy Selection to Exploratory Decision-Making: Diverse Ranking with Policy-Value Networks

The goal of search result diversification is to select a subset of documents from the candidate set to satisfy as many different subtopics as possible. In general, it is a problem of subset selection and selecting an optimal subset of documents is NP-hard. Existing methods usually formalize the problem as ranking the documents with greedy sequential document selection. At each of the ranking position the document that can provide the largest amount of additional information is selected. It is obvious that the greedy selections inevitably produce suboptimal rankings. In this paper we propose to partially alleviate the problem with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP), referred to as M$^2$Div. In M$^2$Div, the construction of diverse ranking is formalized as an MDP process where each action corresponds to selecting a document for one ranking position. Given an MDP state which consists of the query, selected documents, and candidates, a recurrent neural network is utilized to produce the policy function for guiding the document selection and the value function for predicting the whole ranking quality. The produced raw policy and value are then strengthened with MCTS through exploring the possible rankings at the subsequent positions, achieving a better search policy for decision-making. Experimental results based on the TREC benchmarks showed that M$^2$Div can significantly outperform the state-of-the-art baselines based on greedy sequential document selection, indicating the effectiveness of the exploratory decision-making mechanism in M$^2$Div.
Effect of synaptic failure on the performance of sparsely encoded Hopfield associative memory

This paper investigates the fault-tolerance characteristic of sparsely encoded Hopfield associative memory with respect to synaptic disconnection. The effect of this fault on the network performance is evaluated in terms of capacity degradation. The optimum neuron's threshold turns out to be depend on the failure-ratio /spl beta/. Failure-ratio being an unknown, threshold is set to its optimum value corresponding to the case /spl beta/=0. It then turns out that faulty-network is unable to store and retrieve patterns for at least /spl beta//spl ges/1/2. Next, assuming that there exists an adaptive mechanism which estimates failure ratio and resets the threshold to its optimum value, it will be shown that network fault-tolerance is improved; network keeps its associative recall functionality for whole range of synaptic disconnectivity, showing only a graceful performance degradation. This degradation appears to be dependent on the sparsity of stored patterns. <<ETX>>
Predicting Contradiction Intensity: Low, Strong or Very Strong?

Reviews on web resources (e.g. courses, movies) become increasingly exploited in text analysis tasks (e.g. opinion detection, controversy detection). This paper investigates contradiction intensity in reviews exploiting different features such as variation of ratings and variation of polarities around specific entities (e.g. aspects, topics). Firstly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Secondly, the polarity of each review segment containing an aspect is estimated. Only resources containing these aspects with opposite polarities are considered. Finally, some features are evaluated, using feature selection algorithms, to determine their impact on the effectiveness of contradiction intensity detection. The selected features are used to learn some state-of-the-art learning approaches. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. Results showed that variation of ratings, variation of polarities, and reviews quantity are the best predictors of contradiction intensity. Also, J48 was the most effective learning approach for this type of classification.
Transesteri fi cation of jatropha oil with methanol over Mg e Zn mixed metal oxide catalysts

A design was developed for the transesterification reaction of non-edible Jatropha Curcas oil using a heterogeneous catalysis system to replace the use of a homogeneous catalytic reaction. Investigations were conducted on solid MgOeZnO mixed metal oxide catalyst bases with different atomic ratios of magnesium to zinc (Mg/Zn). These catalysts were characterized by BET (BrunauereEmmereTeller) surface area analysis, X-ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) with Energy Dispersive X-ray Spectroscopy (EDS), and the alkalinity of the catalysts was studied by Temperature Programmed Desorption of carbon dioxide (TPD-CO2). The physicochemical properties of the MgOeZnO binary system were superior to those of the individual bulk oxides of MgO and ZnO. In addition, the formation of a binary system between MgO and ZnO established an effective method for transesterification processes. In this study, the effects of stoichiometric composition and surface characteristics on the transesterification activity of MgOeZnO were investigated. The catalysts exhibited high catalytic activity (w80%) with reliable reusability for biodiesel production. 2012 Published by Elsevier Ltd.
mu-tree: an ordered index structure for NAND flash memory

As NAND flash memory becomes increasingly popular as data storage for embedded systems, many file systems and database management systems are being built on it. They require an efficient index structure to locate a particular item quickly from a huge amount of directory entries or database records. This paper proposes μ-Tree, a new ordered index structure tailored to the characteristics of NAND flash memory. μ-Tree is a balanced tree similar to B+-Tree. In μ-Tree, however, all the nodes along the path from the root to the leaf are put together into a single flash memory page in order to minimize the number of flash write operations when a leaf node is updated. Our experimental evaluation shows that μ-Tree outperforms B+-Tree by up to 28% for traces extracted from real workloads. With a small in-memory cache of 8 Kbytes, μ-Tree improves the overall performance by up to 90% compared to B+-Tree with the same cache size.
A safety-aware, systems-based approach to teaching software testing

Although no one doubts that ubiquitous software should be tested thoroughly, a dedicated course on software testing (ST) does not commonly feature in most CS curricula. Even when this is the case, existing courses often fail to offer a real-world testing experience, essential for a successful software engineering career. In our opinion, the main underlying reasons are twofold. First, abstracting software from its socio-technical system context, creates the false impression that ST “equals” unit testing based on functional requirements. Second, practicing on small, anonymously pre-engineered artifacts, conveys a detached and truncated view of the whole testing process. This paper describes an original approach to teach ST that addresses these two limitations by adopting a system engineering paradigm, with an additional strong emphasis on safety. Targeting CS graduates, the course consists of two modules. In the core module, students build fundamental testing knowledge in a software engineering context, and put this knowledge to work in both roles of developer and tester. In the project module, students get hands-on experience in engineering, and in particular testing, of microcontroller-based, safety-critical systems. Software risk and test experts from industry are actively involved in both modules, for advising, guest lectures and project steering. Enthusiastic students’ evaluations throughout years demonstrate that cultivating safety- and hardware-awareness, although unusual for CS programs at non-engineering universities, creates a unique testing experience, while revealing remarkable technical and psychological trade-offs. All this brings more realism, and contributes to a meaningful and efficient learning in ST classrooms.
Mutual collision avoidance during walking in real and collaborative virtual environments

In walkable multi-user HMD-based VR immersed users have to avoid colliding with each other while performing tasks or exploring the virtual environment. This paper presents an experiment in which we compare mutual collision avoidance behaviour of pairs of users walking in the matched real and virtual environments. To generalize our study, we investigate two types of multi-user VR setup: one in which both users share a common tracking space (colocated condition) and one where they move in two separate tracking spaces while sharing a common virtual space (distributed condition). Our results indicated significant differences in locomotor trajectories of participants between the real and virtual conditions. Precisely, we observed an increase in the clearance distance and the median path curvature and a decrease in the walking speed when participants performed the test in VR in the colocated condition. Moreover, we found effects of pre-conditioning between the colocated and distributed scenarios.
Adaptive remediation for novice programmers through personalized prescriptive quizzes

Learning to program is a cognitively demanding activity. Students need to combine mental models of various concepts and constructs to solve problems. Many students new to IT and CS programs have little or no prior experience with abstract reasoning and problem-solving. Instructors attempt to present the core concepts early to allow adequate time for students to complete their programming assignments. However, misconceptions of basic concepts formed in the early stages often get propagated blocking any further progress. Such students often begin to form poor opinions about their capability leading to low self-esteem and performance. This paper proposes a framework to help individual students to overcome their misconceptions through personalized prescriptive quizzes. These quizzes are generated by combining the rich meta-data captured by each quiz question with analysis of past responses to class quizzes. The personalized prescriptive quizzes generated helped to improve student engagement and performance substantially. Over 91% of the students surveyed indicated that personalized quizzes helped them to clarify their own misconceptions and made them more confident of their progress. Students using the prescriptive quizzes performed significantly better than others in subsequent class assessments and the final exam.
Domain-Independent Detection of Emergency Situations Based on Social Activity Related to Geolocations

In general, existing methods for automatically detecting emergency situations using Twitter rely on features based on domain-specific keywords found in messages. This type of keyword-based methods usually require training on domain-specific labeled data, using multiple languages, and for different types of events (e.g., earthquakes, floods, wildfires, etc.). In addition to being costly, these approaches may fail to detect previously unexpected situations, such as uncommon catastrophes or terrorist attacks. However, collective mentions of certain keywords are not the only type of self-organizing phenomena that may arise in social media when a real-world extreme situation occurs. Just as nearby physical sensors become activated when stimulated, localized citizen sensors (i.e., users) will also react in a similar manner. To leverage this information, we propose to use self-organized activity related to geolocations to identify emergency situations. We propose to detect such events by tracking the frequencies, and probability distributions of the interarrival time of the messages related to specific locations. Using an off-the-shelf classifier that is independent of domain-specific features, we study and describe emergency situations based solely on location-based features in messages. Our findings indicate that anomalies in location-related social media user activity indeed provide information for automatically detecting emergency situations independent of their domain.
Stochastic Simulation of Test Collections: Evaluation Scores

Part of Information Retrieval evaluation research is limited by the fact that we do not know the distributions of system effectiveness over the populations of topics and, by extension, their true mean scores. The workaround usually consists in resampling topics from an existing collection and approximating the statistics of interest with the observations made between random subsamples, as if one represented the population and the other a random sample. However, this methodology is clearly limited by the availability of data, the impossibility to control the properties of these data, and the fact that we do not really measure what we intend to. To overcome these limitations, we propose a method based on vine copulas for stochastic simulation of evaluation results where the true system distributions are known upfront. In the basic use case, it takes the scores from an existing collection to build a semi-parametric model representing the set of systems and the population of topics, which can then be used to make realistic simulations of the scores by the same systems but on random new topics. Our ability to simulate this kind of data not only eliminates the current limitations, but also offers new opportunities for research. As an example, we show the benefits of this approach in two sample applications replicating typical experiments found in the literature. We provide a full R package to simulate new data following the proposed method, which can also be used to fully reproduce the results in this paper.
Dynamic Permissions based Android Malware Detection using Machine Learning Techniques

Android is by far the most widely used mobile phone operating system around. However, Android based applications are highly vulnerable to various types of malware attacks attributed to their open nature and high popularity in the market. The fault lies in the underneath permission model of Android applications. These applications need a number of sensitive permissions during their installation and runtime, which enables possible security breaches by malware. The contributions of this paper are twofold: 1) We extract a set of 123 dynamic permissions from 11000 Android applications in a largest publicly available dataset till date; 2) We evaluate a number of machine learning classification techniques including Naive Bayes (NB), Decision Tree (J48), Random Forest (RF), Simple Logistic (SL), and k-star on the newly designed dataset for detecting malicious Android applications. The experimental results indicate that although the malware classification accuracy of RF, J48, and SL are comparable, SL performs marginally better than the other techniques.
Auto-completion for Question Answering Systems at Bloomberg

The Bloomberg Terminal is the leading source of information and news in the finance industry. Through hundreds of functions that provide access to a vast wealth of structured and semi-structured data, the terminal is able to satisfy a wide range of information needs. Users can find what they need by constructing queries, plotting charts, creating alerts, and so on. Until recently, most queries to the terminal were constructed through dedicated GUIs. For instance, if users wanted to screen for technology companies that met certain criteria, they would specify the criteria by filling out a form via a sequence of interactions with GUI elements such as drop-down lists, checkboxes, radio and toggle buttons, etc. To facilitate information retrieval in the terminal, we are equipping it with the ability to understand and answer queries expressed in natural language. Our QA (question answering) systems map structurally complex questions like the above to a logical meaning representation which can then be translated to an executable query language (such as SQL or SPARQL). At that point we can execute the queries against a suitable back end, obtain the results, and present them to the users. Adding a natural-language interface to a data repository introduces usability challenges of its own, chief amongst them being this: How can the user know what the system can and cannot understand and answer (without needing to undergo extensive training)? We can unpack this question into two separate parts: 1) How can we convey the full range of the system's abilities? 2) How can we convey its limitations? We use auto-complete as a tool to help meet both challenges. Specifically, the first question pertains to the general issue of discoverability: We want at least some of the suggested completions to act as vehicles for discovering data and functionality of which users may have not been previously aware. The second question pertains to expectation management. Naturally, no QA system can attain perfect performance; limiting factors include representational shortcomings and various kinds of incompleteness of the underlying data sources, as well as NLP technology limitations. We want to stop generating completions as a signal indicating that we are not able to understand and/or answer what is being typed.
Utilizing Knowledge Graphs for Text-Centric Information Retrieval

The past decade has witnessed the emergence of several publicly available and proprietary knowledge graphs (KGs). The depth and breadth of content in these KGs made them not only rich sources of structured knowledge by themselves, but also valuable resources for search systems. A surge of recent developments in entity linking and entity retrieval methods gave rise to a new line of research that aims at utilizing KGs for text-centric retrieval applications. This tutorial is the first to summarize and disseminate the progress in this emerging area to industry practitioners and researchers.
EPICURE - Aspect-based Multimodal Review Summarization

Restaurant reviews are popular and a valuable source of information. Often, large number of reviews are written for restaurants which warrants the need for automated summarization systems. In this paper we present epicure, a novel text and image summarization platform. For the summarization of opinionated content like reviews, considering different aspects have largely been ignored, and we address this by creating balanced reviews for different aspects like food and service. We argue that traditional criteria for extractive review summarization such as coverage and diversity have limited applicability. We draw on the power and usefulness of submodular functions for extractive summarization and introduce novel submodular functions such as importance, freshness, purity, trustworthiness and balanced opinion. We are also one of the first to provide an image summary for diffeerent aspects of a restaurant by mapping text to images using a multimodal neural network, for which we provide initial experiments. We show the effectiveness of our platform by evaluating it against strong baselines and also use crowdsourcing experiments for a subjective comparison of our approach with existing works.