Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized Remedial Recommendations for SQL Programming Practice System

Personalized recommendation of learning content is one of the most frequently cited benefits of personalized online learning. It is expected that with personalized content recommendation students will be able to build their own unique and optimal learning paths and to achieve course goals in the most optimal way. However, in many practical cases students search for learning content not to expand their knowledge, but to address problems encountered in the learning process, such as failures to solve a problem. In these cases, students could be better assisted by remedial recommendations focused on content that could help in resolving current problems. This paper presents a transparent and explainable interface for remedial recommendations in an online programming practice system. The interface was implemented to support SQL programming practice and evaluated in the context of a large database course. The paper summarizes the insights obtained from the study and discusses future work on remedial recommendations.
What Can I Say?: Effects of Discoverability in VUIs on Task Performance and User Experience

Discoverability, the ability for users to find and execute features through a user interface, is a recurrent problem with Voice User Interface (VUI) design that makes it difficult for users to understand what commands are supported by a newly encountered system. We studied the effects of two different discoverability strategies proposed in literature, one which provides informational prompts automatically and one which provides help only when the user requests it by asking 'What Can I Say? '. Our study adopted a Wizard of Oz approach that allowed users to order food delivery by voice. Through statistical analysis, we confirmed the beneficial nature of both strategies, with significantly better task performance and higher usability scores in comparison to a baseline. This suggests designers should consider the use of a discoverability strategy in the design of VUIs. While no significant differences were found between the strategies, a majority of the participants highlighted their preference for the 'What Can I Say? ' strategy if they were to use the VUI more frequently. Finally, we reflect on the implications for the design of VUIs, highlighting the need to distinguish between initial use and longer-term use in the selection of a strategy.
User Experience of Alexa when controlling music: comparison of face and construct validity of four questionnaires

We evaluate the user experience (UX) of Amazon's Alexa when users play and control music. For measuring UX we use established UX metrics (SASSI, SUISQ-R, SUS, AttrakDiff). We investigated face validity by asking users to rate how well they think a questionnaire measures what it is supposed to measure and we assessed construct validity by correlating UX scores of questionnaires with each other. We find a mismatch between face and construct validity of the evaluated questionnaires. Specifically, users feel that SASSI represents their experience better than other questionnaires, however this is not supported by correlations between questionnaires, which suggest that all investigated questionnaires measure UX to a similar extent. Importantly, the fact that face validity and construct validity diverge is not surprising as this has been observed before. Our work adds to existing literature by providing face and construct validity scores of UX questionnaires for interactions with the common speech assistant Alexa.
Towards a Knowledge-aware Food Recommender System Exploiting Holistic User Models

Food recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits. In this short paper, we present a recommendation strategy based on knowledge about food and users' health-related characteristics to generate personalized recipes suggestions. By focusing on personal factors as a user's BMI and dietary constraints, we exploited a holistic user model to re-rank a basic recommendation list of 4,671 recipes, and investigated in a web-based experiment (N=200) to what extent it generated satisfactory food recommendations. We found that some of the information encoded in a users' holistic user profiles affected their preferences, thus providing us with interesting findings to continue this line of research.
EmpathicSDS: Investigating Lexical and Acoustic Mimicry to Improve Perceived Empathy in Speech Dialogue Systems

In human-to-human conversations, showing empathy and thus understanding for the situation of the opposite party is crucial for a natural conversation. Thereby, emotional mimicry, i.e. imitating expressions of the person whom we are interacting with, is one of the basic mechanisms contributing to empathy. State-of-the-art speech dialogue systems still lack the ability of showing empathy, which limits naturalness. Thus, we present EmpathicSDS, a prototype to investigate the potential of lexical and acoustic mimicry for improving empathy in conversational interfaces. Our prototype comprises three different modes: 1. ) neutral, where the system's response to a user query is static, 2. ) lexical mimicry, where the wording of the user is reappraised by the system, and 3. ) lexical and acoustic mimicry, which applies both lexical mimicry and a matching of the system's voice emotion to the user's emotional state. We conducted a user study with 33 participants to evaluate the effect of the mimicry approaches on user perception and to explore the role of user emotions. Our results show that lexical mimicry significantly improves perceived empathy and personalization without affecting efficiency. Acoustic mimicry can further improve naturalness in the condition of positive emotion while impairing efficiency in the negative condition.
4D BIM Workspace Conflict Detection for Occupational Management A Case Study for Basement Construction Using Bottom Up Method

Construction activities need adequate workspaces to be executed safely and minimize workspace conflicts leading to safety hazards. This study aimed to build a process of applying Building Information Modeling (BIM) to manage construction workspace, thereby supporting safety management. The first step of the process was to build a 4D BIM model including schedule and workspace of the construction activities. Then, this 4D BIM model was used to detect workspace conflicts among activities, from which the safety engineers can provide appropriate solutions timely. This process helps the safety engineers make a safety plan visually and update safety information on the construction site promptly.
Towards Generating Personalized Country Recommendation

The rise in international migration over the past decades has given more audience to this crucial issue of human life. According to reports by United Nations, more than 243 million people live in a country that is not their place of birth. People decide to immigrate, based on a range of reasons, and choose the country of destination with the hope to begin a new life. However, such a risky decision may not necessarily lead to an improvement of life and in many cases could result in complete dissatisfaction of the emigrating person, and in the extreme cases, cause human catastrophe. Recommender Systems (RSs) are tools that could mitigate this problem by supporting the people in their decision making process. RSs can interact with the people who are willing to immigrate and acquire certain information about their preferences on potential destinations. Accordingly, RSs can build predictive models based on the acquired data and offer suggestions on where could be a better match for the specific preferences and constraints of people. This work is an attempt to build a RS that can be used in order to receive personalized recommendation of countries. The system is capable of eliciting preferences of users in the form of ratings, learning from the preferences, and intelligently generating a personalized ranking list of countries for every target user. We have conducted a user study in order to evaluate the quality of the recommendation, measured in terms of accuracy, diversity, novelty, satisfaction, and capability to understand the particular preferences of different users. The results were promising and indicated the potentials of the generating personalized recommendations in this less-explored domain.
Tangible Coding for kids with AI inside

In this paper we present the tangible coding activity we proposed as an interactive laboratory at the Festival della Scienza in Genova, Italy, in 2018 and 2019. Our goal was to disseminate basic principles of coding in a fun and accessible way, reaching young children. In the activity, each participant was given a small set of 3D shapes -- the language -- and very simple rules on how to use them to build a tangible sentence as an ordered sequence. We designed an Artificial Intelligence module that given an image of the shapes sequence, is able to identify and recognize the shapes, associating them with a label, and finally produce a fantasy sentence or a small story. Overall, more than 1000 participants attended the laboratory, confirming the potential of the activity and highlighting many possible future improvements.
Detection and Phylogenetic Affiliation of Wolbachia Endosymbiont from Drosophila melanogaster (India)

ABSTRACT: The cellular endosymbiont Wolbachia causes reproductive alterations in arthropod and nematode species. The presence of Wolbachia in natural populations of arthropods is made possible by their ability to manipulate the physiology of their host. This study evaluated the presence of Wolbachia in laboratory populations of Drosophila melanogaster (India). Using PCR based technology, WSP was amplified from D. melanogaster which yielded a 635bp gene. Here we report a different strain of Wolbachia which is placed in phylogenetic position along with the WMel Popcorn strain and Amergiers strain of India with Wolbachia A super group. Thus by comparing our results, the probable phenotype of the new strain of Wolbachia is identified. The information will be valuable in evolutionary studies of the endosymbiont Wolbachia and strain classification. Abbreviations: WSP: Wolbachia Surface Protein PCR: Polymerase Chain Reaction MEGA: Molecular Evolutionary Genetics Analysis
Carba-LNA modified siRNAs targeting HIV-1 TAR region downregulate HIV-1 replication successfully with enhanced potency

The conformationally-locked carbocyclic nucleosides carbaLNA (“jcLNA”) (Gagnon et al., Biochemistry, 2010, 49, 10166; Srivastava et al., J. Am. Chem. Soc., 2007, 129, 8362; Xu et al., J. Org. Chem., 2009, 74, 6534; Zhou and Chattopadhyaya, J. Org. Chem., 2010, 75, 2341; Zhou et al., J. Org. Chem., 2009, 74, 118) are chemically engineered by fusing a carbocyclic ring at the C2′ to C4′ chiral centres in a stereospecific manner at the α-face of the pentose-sugar of the native nucleosides. The benefit of the chemically-modified oligonucleotides with the jcLNA scaffold has been shown to be their uniquely enhanced nuclease resistance in the blood serum as well as their improved RNase H recruitment capability to cleave the target RNA in the hybrid antisense-RNA duplex when used as an antisense agent, compared to those of locked nucleic acid (LNA) modified counterparts. Herein we report the relative inhibition efficiency of HIV-1 by jcLNA modified siRNAs targeting TAR region compared to those of the LNA counterparts, in that the former were found to exhibit improved silencing efficiency and displayed enhanced stability in human serum with negligible cytotoxicity compared to those of the latter. A single jcLNA substitution as the 3′-overhang of the guide strand displayed near native-like IC50 value (of 4.01 ± 0.87 nM compared to the nearly two-fold higher IC50 value of 7.15 ± 1.57 nM for LNA modified counterparts, and of the native siRNA of 1.84 ± 0.16 nM) and significantly higher t1/2 value for the stability in serum (11.9 h for jcLNA, 6.8 h for LNA and 3.0 h for native), thereby showing that the efficiency of jcLNA-modified-siRNAs is supported by stability without compromising the native-like efficiency and target RNA recognition and subsequent down-regulation. Amongst all the modified siRNAs so far used to target HIV-1 TAR region, the best IC50 value was obtained for the doubly-modified siRNA in which jcLNA substitution was introduced both at position 1 and 20 of the antisense strand (T1 + T20, i.e. jcLNA11 which showed IC50 value of 0.54 ± 0.14 nM). The IC50 of this doubly-modified siRNA was more than three-fold lower than that of the native and two-fold lower than that of LNA modified counterpart, i.e. LNA12: IC50: 1.13 ± 0.27 nM. Hence the strategy to chemically modify the native siRNAs by substitution with the jcLNA can be considered as a significant development, leading to both enhanced siRNA efficiency and serum stability over that of the native.
A Method for Discovering Local Travel Information from Travel-blogs

Information mining for travel has become a popular research domain in the development of tourism industry nowadays. Currently, studies focusing on the extraction of available travel information from actual tourists' evaluations and reviews on social media sites have been conducted under numerous occasions. In this study, we have proposed another effective yet simple method of discovering local travel-information from traveler's blogs by locating region-sensitive information for the travelers. In this process, regionally restricted words are first extracted based on their frequency of appearances inside the blog. Then, a region-restrictedness score of each blog is calculated through the analysis of the previously extracted regionally restricted phrases. From there, we begin analyzing the content of each blog and classify them into pre-defined categories, using LDA model and word embedding-representations. Through this process, we are able to generate blog recommendations based on our method and see some successful examples as a proof for the effectiveness of our approach.
Cross-Game Modeling of Player's Behaviour in Free-To-Play Games

Player modelling is an important task for almost any game creator, which helps in understanding the player-base. One of the major issues is an early leave of players which makes modelling them challenging. In our research, we focus on the cold-start problem by utilizing information about a player from multiple games or other players in a given game. Although multiple studies focus on cross-game modelling, they still often require manual mapping of features or don't consider a player's behaviour specific to the given game. Our proposed method is based on transfer learning and unsupervised translation. In addition, we propose a combination of group-based and individual player models.
Investigating Multi-Modal Measures for Cognitive Load Detection in E-Learning

In this paper, we analyze a wide range of physiological, behavioral, performance, and subjective measures to estimate cognitive load (CL) during e-learning. To the best of our knowledge, the analyzed sensor measures comprise the most diverse set of features from a variety of modalities that have to date been investigated in the e-learning domain. Our focus lies on predicting the subjectively reported CL and difficulty as well as intrinsic content difficulty based on the explored features. A study with 21 participants, who learned through videos and quizzes in a Moodle environment, shows that classifying intrinsic content difficulty works better for quizzes than for videos, where participants actively solve problems instead of passively consuming videos. Regression analysis for predicting the subjectively reported level of CL and difficulty also works with very low error within content topics. Among the explored feature modalities, eye-based features yield the best results, followed by heart-based and then skin-based measures. Furthermore, combining multiple modalities results in better performance compared to using a single modality. The presented results can guide researchers and developers of cognition-aware e-learning environments by suggesting modalities and features that work particularly well for estimating difficulty and CL.
ARCHITECTURE OF 53 RAIN FOREST TREE SPECIES DIFFERING IN ADULT STATURE AND SHADE TOLERANCE

Tree architecture determines a tree's light capture, stability, and efficiency of crown growth. The hypothesis that light demand and adult stature of tree species within a community, independently of each other, determine species' architectural traits was tested by comparing 53 Liberian rain forest tree species. We evaluated whether species differed in their tree height, crown depth, and crown diameter, when compared at a standardized size of 15-cm diameter at breast height, and how their architecture changed early during ontogeny. Tree height was positively correlated with adult stature and light demand. By producing a relatively slender stem, large-stature species are able to rapidly reach their reproductive size, at a low cost for construction and support. Light-demanding species need a slender stem to be able to attain or maintain a position in the canopy. Both crown depth and crown diameter were negatively correlated with adult stature, but not with light demand. This is in contrast with the hypothesis that shade-tolerant species should have a shallow crown to reduce self-shading in a light-limited environment. Investing energy in height growth rather than lateral crown growth allows a rapid vertical stem extension, but crown diameter has to be sufficiently small to reduce the risk of mechanical failure. All architectural patterns were maintained during ontogeny. The key factors driving interspecific differences in tree architecture are the costs of height extension and mechanical stability. In general, light demand and adult stature represent independent axes of architectural differentiation, af- fecting tree architecture in different ways.
Exploring Personalized University Ranking and Recommendation

Finding the right university to study is still a challenge for many people due to the large number of universities worldwide. Although there exist a number of global university rankings, they provide non# personalized rankings as one-size-fits-all solution. This becomes an issue since different people may have different preferences and considerations in mind, when choosing the university to study. This paper addresses this problem and presents a Recommender System to generate a personalized ranking list based on users particular preferences. The system is capable of eliciting users preferences, provided as ratings for universities, building predictive models on the preference data, and generating a personalized university ranking list that is tailored to the particular preferences and needs of the users. We performed two sets of experiments. First, we conducted an offline experiment using a dataset of user preferences, collected by the early version of our system. This allowed us to cross-validate and compare different recommender algorithms and choose the most accurate recommender algorithm that can better suit the particular problem at hand. We integrated the chosen algorithm in the final implementation of our system. As the follow-up, we performed a user study in order to analyze whether or not the final version of our system is usable from the perception of users. The results showed that the system has scored well above the benchmark and users assessed it as "good" in term of usability.
Program-aware fuzzing for MQTT applications

Over the last few years, MQTT applications have been widely exposed to vulnerabilities because of their weak protocol implementations. For our preliminary research, we conducted background studies to: (1) determine the main cause of vulnerabilities in MQTT applications; and (2) analyze existing MQTT-based testing frameworks. Our preliminary results confirm that MQTT is most susceptible to malformed packets, and its existing testing frameworks are based on blackbox fuzzing, meaning vulnerabilities are difficult and time-consuming to find. Thus, the aim of my research is to study and develop effective fuzzing strategies for the MQTT protocol, thereby contributing to the development of more robust MQTT applications in IoT and Smart Cities.
Crowdsourced requirements generation for automatic testing via knowledge graph

Crowdsourced testing provides an effective way to deal with the problem of Android system fragmentation, as well as the application scenario diversity faced by Android testing. The generation of test requirements is a significant part of crowdsourced testing. However, manually generating crowdsourced testing requirements is tedious, which requires the issuers to have the domain knowledge of the Android application under test. To solve these problems, we have developed a tool named KARA, short for Knowledge Graph Aided Crowdsourced Requirements Generation for Android Testing. KARA first analyzes the result of automatic testing on the Android application, through which the operation sequences can be obtained. Then, the knowledge graph of the target application is constructed in a manner of pay-as-you-go. Finally, KARA utilizes knowledge graph and the automatic testing result to generate crowdsourced testing requirements with domain knowledge. Experiments prove that the test requirements generated by KARA are well understandable, and KARA can improve the quality of crowdsourced testing. The demo video can be found at https://youtu.be/kE-dOiekWWM.
Daisy: A Friendly Conversational Agent for Older Adults

We present Daisy, a voice-controlled conversational agent embodied as a household potted flower. Daisy has been designed for the needs of older adults, providing companionship in times of social isolation by engaging with them in casual conversation, suggesting relevant activities to keep them connected with their community and having them care for it. Daisy was created using user centered design methods. This paper describes Daisy, our experiences designing conversational agents for older adults and the considerations that are crucial when designing such agents.
Effects of Proactive Dialogue Strategies on Human-Computer Trust

Intelligent computer systems aim at providing user-assistance for challenging tasks, like decision-making, planning, or learning. For offering optimal assistance, it is essential for such systems to decide when to be reactive or proactive and how active system behaviour should be designed. Especially, as this decision may greatly influence the user's trust in the system. Therefore, we conducted a mixed-factorial study which examines how different levels of proactivity (none, notification, suggestion, and intervention) as well as timing strategies (fixed-timing and insecurity-based) are trusted by subjects while performing a planning task. The results showed, that proactive system behaviour is perceived trustworthy in insecure situations independent of the timing. However, proactive dialogue showed strong effects on cognition-based trust (system's perceived competence and reliability) depending on task difficulty. Furthermore, fully autonomous system behaviour fails to establish an adequate human-computer trust relationship, in contrast to conservative strategies.