Miracle-dz/UniFAR-FTUs huggingface dataset:
A two-staged capstone project to foster university-business dialogue

Capstone projects improve the endowments of knowledge, ability and skills earned by higher-education students, and help intensify the university-business dialogue. While not solely suited for BSc curricula, capstones raise the employability of BSc graduates, whose market value is intrinsically inferior to their MSc correspondents, yet highly attractive thanks to shorter turn-around time. Designing a BSc curriculum in Computer Science after those premises requires balancing two forces. One is traditional content-centered teaching, which has many adepts and exerts higher pressure on the shorter duration of the study path. The other is learner-centered teaching, which promotes active teaching strategies, and makes room for capstone inserts. This paper discusses how the University of Padua went around that challenge designing its BSc curriculum in Computer Science in 2002, and critically assesses its outcome to date. 27 credit hours of the total 180 in the novel curriculum were devolved to a capstone project comprising an internship in business, whose critical review feeds the final exam. The capstone was organized as a two-staged progression: (a) the first leg being a preparatory, learning-for-work collaborative laboratory aimed at the acquisition of soft skills, and exposure to technology innovation challenges; and (b) the second leg promoting a learning-through-work individual internship. Evaluation of field data from 14 cohorts of graduates, from 07/2004 to 12/2017, shows that the BSc curriculum has met with the appreciation of students and employers, and it has served well the purpose of boosting the university-business dialogue.
Assessing Twitter Geocoding Resolution

User-defined location privacy settings on Twitter cause geolocated tweets to be placed at four different resolutions: precise, point of interest (POI), neighbourhood and city levels. The latter two levels are not described by Twitter or the API, resulting in a risk that clustered tweets are unintentionally treated as real clusters in spatial analyses. This paper outlines a framework to address these differing spatial resolutions and highlight the impact they can have on cartographic representations. As part of this framework this paper also outlines a method of discovering sources (third-party applications) that produce geolocated tweets but do not reflect genuine human activity. We found that including tweets at all spatial resolutions created an artificially inflated importance of certain locations within a city. Discovering device-level geocoded tweets was straight forward, but querying Foursquare's API was required to differentiate between neighbourhood level clusters and POIs.
A New Term Frequency Normalization Model for Probabilistic Information Retrieval

In probabilistic BM25, term frequency normalization is one of the key components. It is often controlled by parameters $k_1$ and b , which need to be optimized for each given data set. In this paper, we assume and show empirically that term frequency normalization should be specific with query length in order to optimize retrieval performance. Following this intuition, we first propose a new term frequency normalization with query length for probabilistic information retrieval, namely \textttBM25\tiny QL . Then \textttBM25\tiny QL is incorporated into the state-of-the-art models CRTER riptsize 2 and LDA-BM25, denoted as $\textttCRTER riptsize 2 ^\texttt\tiny QL $ and \textttLDA-BM25\tiny QL respectively. A series of experiments show that our proposed approaches \textttBM25\tiny QL , $\textttCRTER riptsize 2 ^\texttt\tiny QL $ and \textttLDA-BM25\tiny QL are comparable to BM25, CRTER riptsize 2 and LDA-BM25 with the optimal b setting in terms of MAP on all the data sets.
Modeling Dynamic Pairwise Attention for Crime Classification over Legal Articles

In juridical field, judges usually need to consult several relevant cases to determine the specific articles that the evidence violated, which is a task that is time consuming and needs extensive professional knowledge. In this paper, we focus on how to save the manual efforts and make the conviction process more efficient. Specifically, we treat the evidences as documents, and articles as labels, thus the conviction process can be cast as a multi-label classification problem. However, the challenge in this specific scenario lies in two aspects. One is that the number of articles that evidences violated is dynamic, which we denote as the label dynamic problem. The other is that most articles are violated by only a few of the evidences, which we denote as the label imbalance problem. Previous methods usually learn the multi-label classification model and the label thresholds independently, and may ignore the label imbalance problem. To tackle with both challenges, we propose a unified D ynamic P airwise A ttention M odel (DPAM for short) in this paper. Specifically, DPAM adopts the multi-task learning paradigm to learn the multi-label classifier and the threshold predictor jointly, and thus DPAM can improve the generalization performance by leveraging the information learned in both of the two tasks. In addition, a pairwise attention model based on article definitions is incorporated into the classification model to help alleviate the label imbalance problem. Experimental results on two real-world datasets show that our proposed approach significantly outperforms state-of-the-art multi-label classification methods.
Review Sentiment-Guided Scalable Deep Recommender System

Existing review-aware recommendation methods represent users (or items) through the concatenation of the reviews written by (or for) them, and depend entirely on convolutional neural networks (CNNs) to extract meaningful features for modeling users (or items). However, understanding reviews based only on the raw words of reviews is challenging because of the inherent ambiguity contained in them originated from the users' different tendency in writing. Moreover, it is inefficient in time and memory to model users/items by the concatenation of their associated reviews owing to considerably large inputs to CNNs. In this work, we present a scalable review-aware recommendation method, called SentiRec, that is guided to incorporate the sentiments of reviews when modeling the users and the items. SentiRec is a two-step approach composed of the first step that includes the encoding of each review into a fixed-size review vector that is trained to embody the sentiment of the review, followed by the second step that generates recommendations based on the vector-encoded reviews. Through our experiments, we show that SentiRec not only outperforms the existing review-aware methods, but also drastically reduces the training time and the memory usage. We also conduct a qualitative evaluation on the vector-encoded reviews trained by SentiRec to demonstrate that the overall sentiments are indeed encoded therein.
Investigating Web Defacement Campaigns at Large

Website defacement is the practice of altering the web pages of a website after its compromise. The altered pages, calleddeface pages, can negatively affect the reputation and business of the victim site. Previous research has focused primarily on detection, rather than exploring the defacement phenomenon in depth. While investigating several defacements, we observed that the artifacts left by the defacers allow an expert analyst to investigate the actors' modus operandi and social structure, and expand from the single deface page to a group of related defacements (i.e., acampaign ). However, manually performing such analysis on millions of incidents is tedious, and poses scalability challenges. From these observations, we propose an automated approach that efficiently builds intelligence information out of raw deface pages. Our approach streamlines the analysts job by automatically recognizing defacement campaigns, and assigning meaningful textual labels to them. Applied to a comprehensive dataset of 13 million defacement records, from Jan. 1998 to Sept. 2016, our approach allowed us to conduct the first large-scale measurement on web defacement campaigns. In addition, our approach is meant to be adopted operationally by analysts to identify live campaigns on the field. We go beyond confirming anecdotal evidence. We analyze the social structure of modern defacers, which includes lone individuals as well as actors that cooperate with each others, or with teams, which evolve over time and dominate the scene. We conclude by drawing a parallel between the time line of World-shaping events and defacement campaigns, representing the evolution of the interests and orientation of modern defacers.
A new solution for the Energy Packet-based Dispatching using power/signal dual modulation

The current process of transformation affecting the energy system is calling for completely new approaches. This paper investigates the possibility of introducing energy packets as a fundamentally new abstraction concept that is following the transformation of communication networks in the 1970s from synchronous circuit switching to asynchronous store-and-forward routing. Focus is placed on the exploitation of power electronic devices in creating information embedded into the power flow. Considerations on some criticalities and future developments that are needed to further proceed on the development of this new vision are discussed. A simple test in Matlab/SIMULINK is provided in support for the packet dispatching described in the paper.
Going beyond performance scores: understanding cognitive-affective states in kindergarteners

Cognitive-affective states during learning or interactions with technologies is dependent on the mental effort of the learner and / or the cognitive load imposed by the system. Despite the growing research on the importance of understanding cognitive-affective state and their relationship to learning, measurement of such states during the learning process in Kindergartners is still unclear. While most assessments of learning and usability evaluations with Kindergartners focus on performance, self-reports and inferring from observable behaviours, they provide limited insights into the cognitive load and emotional state during the learning or interaction that are essential for a holistic picture of learning. Through a study with 18 kindergartners, we explore the feasibility of understanding cognitive-affective states associated with mental effort by triangulating the data obtained from observations, physiological markers, self-reports and performance as they performed tasks of varying mental effort. We present findings on the reliable markers within these sources across tasks. Results reveal that such a triangulation offers deeper insights into the cognitive-affective state of the learner. We believe this work would be a step towards better understanding of the learning process thereby facilitating instruction that is more aligned with the learner's cognitive-affective architecture as well as establishing guidelines for comprehensive usability / evaluation processes based on well-defined associations between child behaviour and child action.
Numerical Weather Prediction Data Free Solar Power Forecasting with Neural Networks

The worldwide increase in renewable energy penetration levels has made accuracy, availability, and affordability of wind and solar energy forecasting systems an integral part of the modern power grids. The present paper describes an approach to forecasting one-day-ahead photovoltaic (PV) power generation without the use of numerical weather prediction (NWP) data. The presented approach uses a closed loop non-linear autoregressive artificial neural network (CL-NAR-ANN) model with only the historical generated PV power data as input. In case of emergency, if the communication channel with the weather provider fails, the whole forecasting system runs a risk of failing. Also, purchasing NWP data might be too expensive for smaller utilities. In such situations, NWP data free models can provide cost-effective and reasonably accurate PV power forecasts, which can act as a good backup solution. Furthermore, the model is evaluated using a dataset from the Global Energy Forecasting Competition of 2014 (GEFCom14) and its results are compared to other data-driven models such as polynomial and artificial neural network (ANN) models with and without NWP data as input. The results suggest that the CL-NAR-ANN model delivers acceptable forecasts and outperforms other NWP free models by a margin of 8% in terms of root mean square error, hence supporting the possibility of obtaining acceptable forecasts using the CL-NAR-ANN.
TABOR: A Graphical Model-based Approach for Anomaly Detection in Industrial Control Systems

Industrial Control Systems (ICS) such as water and power are critical to any society. Process anomaly detection mechanisms have been proposed to protect such systems to minimize the risk of damage or loss of resources. In this paper, a graphical model-based approach is proposed for profiling normal operational behavior of an operational ICS referred to as SWaT (Secure Water Treatment). Timed automata are learned as a model of regular behaviors shown in sensors signal like fluctuations of water level in tanks. Bayesian networks are learned to discover dependencies between sensors and actuators. The models are used as a one-class classifier for process anomaly detection, recognizing irregular behavioral patterns and dependencies. The detection results can be interpreted and the abnormal sensors or actuators localized due to the interpretability of the graphical models. This approach is applied to a dataset collected from SWaT. Experimental results demonstrate the model's superior performance on both precision and run-time over methods including support vector machine and deep neural networks. The underlying idea is generic and applicable to other industrial control systems such as power and transportation.
FBSleuth: Fake Base Station Forensics via Radio Frequency Fingerprinting

Fake base station (FBS) crime is a type of wireless communication crime that has appeared recently. The key to enforcing the laws on regulating FBS based crime is not only to arrest but also to convict criminals effectively. Much work on FBS discovering, localization, and tracking can assist the arresting, but the problem of collecting evidence accurately to support a proper conviction has not been addressed yet. To fill in the gap of enforcing the laws on FBS crimes, we design FBSleuth, an FBS crime forensics framework utilizing "radio frequency (RF) fingerprints'', e.g., the unique characteristics of the FBS transmitters embedded in the electromagnetic signals. Essentially, such fingerprints stem from the imperfections in hardware manufacturing and thus represent a consistent bond between an individual FBS device and its committed crime. We model the RF fingerprint from the subtle variance of the modulation errors, instantaneous frequency, and phases of the RF signals. Our validation of FBSleuth on six FBSes from four cities over more than 5 months shows that FBSleuth can achieve over 99% precision, 96.4% recall, and 97.94% F1 score in a dynamic wild environment.
Effects of playful audio augmentation on teenagers' motivations in cooperative physical play

Physical Education (PE) is beneficial for students' mental and physical health, however, teenagers are increasingly becoming less motivated to actively participate in the current form of PE. Motivational studies show that collective physical activity augmented by playful interactions could encourage more participation and social engagement in physical activities (PA). In this paper, we present a within-subject field study with 20 teenagers formed into five groups sized equally. The study is conducted using Shuttlezap, a prototype that provides real-time playful audio augmentation to the process of the activity. Results show that the teenagers enjoyed playful audio augmentation and were socially engaged with an enhancement of their perception of competence. By further comparing the conditions between with and without the audio augmentation, we found that the playful audio augmentation positively contributed to the playfulness in terms of perceived relaxation and expression. We conclude with design implications for social play in the context of PE for teenagers.
Cultures of participation in the digital age: design trade-offs for an inclusive society

This new edition of CoPDA workshop, the 5th since 2013, is dedicated to the discussion of design trade-offs that have to be addressed for embracing diversity and implementing an inclusive society. With this workshop, we invite researchers and practitioners to discuss and exchange experiences able to inform design processes in a Cultures of Participation perspective, focusing on theoretical frameworks, practical experiences, case studies, and research projects, both in Academy and Industry.
Gamification is working, but which one exactly?: Results from an experiment with four game design elements

Gamification aims to increase motivation in non-game contexts by using game design elements. Usually gamification research examines the impact of multiple game design elements, making it almost impossible to find out how and to what extent these elements individually contribute to user motivation. To narrow this research gap the present study uses an experimental online setting with 505 subjects to gain further insight about the individually applied elements badge, feedback, progress bar and storytelling and additionally in multiple combinations. Also, this study systematically extends a predecessor study that has researched only badges and storytelling. The aim of the studies is to observe whether the number of answered questions in a quiz can be increased by using, ceteris paribus, different game elements. Basically, the task was to answer a maximum number of 190 questions about continents, countries and space. 14 different badges could be achieved, with different completion criteria for all of them. The element feedback was designed as "correct-false"-feedback. As another element a round progress bar implemented as a slowly filling globe was added. Finally, storytelling was realized as a tale about an alien which gives us a chance to save earth from destruction because a bypass of an intergalactic highway is necessary. Participating subjects were told that they can continue as long as they want and stop at any time answering questions by clicking a designated button. The results provide support for significant motivation increases, but they vary strongly for different gamification elements in terms of their motivational potential.
Visual complexity of graphical user interfaces

Graphical User Interfaces (GUIs) of low visual complexity tend to have higher aesthetics, usability and accessibility, and result in higher user satisfaction. Despite a few authors recently used or studied visual complexity, the concept of visual complexity still needs to be better defined for the use in HCI research and GUI design, with its underlying aspects systematized and operationalized, and different measures validated. This paper reviews the aspects of GUI visual complexity and operationalizes four aspects with nine computation-based measures in total. Two user studies validated the measures on two types of stimuli - webpages (study 1, n = 55) and book pages (study 2, n = 150) - with two user groups, dyslexics (people with reading difficulties) and typical readers. The same complexity aspects could be expected to determine complexity perception for both GUI types, whereas different complexity aspects could be expected to determine complexity perception for dyslexics, relative to typical readers. However, the studies showed little to no difference between dyslexics and average readers, whereas web pages did differ from book pages in what aspects made them seem complex. It was not the intergroup differences, but the stimulus type that defined criteria to judge visual complexity. Future research and visual design could rely on the visual complexity aspects outlined in this paper.
Patterns, dynamics and consequences of microplastic ingestion by the temperate coral, Astrangia poculata

Microplastics (less than 5 mm) are a recognized threat to aquatic food webs because they are ingested at multiple trophic levels and may bioaccumulate. In urban coastal environments, high densities of microplastics may disrupt nutritional intake. However, behavioural dynamics and consequences of microparticle ingestion are still poorly understood. As filter or suspension feeders, benthic marine invertebrates are vulnerable to microplastic ingestion. We explored microplastic ingestion by the temperate coral Astrangia poculata. We detected an average of over 100 microplastic particles per polyp in wild-captured colonies from Rhode Island. In the laboratory, corals were fed microbeads to characterize ingestion preference and retention of microplastics and consequences on feeding behaviour. Corals were fed biofilmed microplastics to test whether plastics serve as vectors for microbes. Ingested microplastics were apparent within the mesenterial tissues of the gastrovascular cavity. Corals preferred microplastic beads and declined subsequent offerings of brine shrimp eggs of the same diameter, suggesting that microplastic ingestion can inhibit food intake. The corals co-ingested Escherichia coli cells with microbeads. These findings detail specific mechanisms by which microplastics threaten corals, but also hint that the coral A. poculata, which has a large coastal range, may serve as a useful bioindicator and monitoring tool for microplastic pollution.
Multidrug-resistant Shigella infection in pediatric patients with diarrhea from central Iran

Background: Shigella spp. are primary pathogens of diarrhea in children worldwide. Emergence of resistance to fluoroquinolones and third-generation cephalosporins is crucial in the management of pediatric shigellosis. We determined the prevalence and the antibiotic resistance patterns of Shigella species isolated from pediatric patients in central Iran. Materials and methods: Pediatric diarrhea samples (n=230) were cultured on MacConkey and XLD agar media and in GN broth. Genus-specific PCR for ipaH was also used for detection directly from fecal specimens. Antibiotic resistance and the frequency of ESBL and AmpC genes were determined. Results: Out of the 230 samples, 19 (8.2%) cases of Shigella spp. were identified using culture. Twenty-six samples were positive by PCR (11.3%), S. flexneri (4/19; 21%) and S. sonnei (15/19; 78.9%) being the most detected. The highest antibiotic resistance rates were found for cotrimoxazole (19/19; 100%), ampicillin (16/19; 84.2%), cefixime (13/19; 68.4%) and ceftriaxone (12/19; 63.1%). Ten cases showed phenotypic ESBL presence and all these strains were positive for blaTEM, blaCTX-M-1, and blaCTX-M-15. Three strains were AmpC positive, all of which harbored blaCMY-2 and two contained blaCIT. Of the 19 Shigella isolates 5 (26.3%), 2 (10.5%), and 1 (5.2%) were phenotypically resistant to nalidixic acid, ciprofloxacin, and norfloxacin, respectively. Class 1 integron was found in 18 (94.7%) isolates whereas class 2 integron was found in 19 (100%) strains. Conclusion: We found a considerable presence of Shigella species with elevated antibiotic resistance levels. In particular, the resistance to third-generation cephalosporins (ESBL) and ciprofloxacin must be taken seriously.