Miracle-dz/UniFAR-FTUs huggingface dataset:
Sonification for Conveying Data and Emotion

In the present study a sonification of running data was evaluated. The aim of the sonification was to both convey information about the data and convey a specific emotion. The sonification was evaluated in three parts, firstly as an auditory graph, secondly together with additional text information, and thirdly together with an animated visualization, with a total of 150 responses. The results suggest that the sonification could convey an emotion similar to that intended, but at the cost of less good representation of the data. The addition of visual information supported understanding of the sonification, and the auditory representation of data. The results thus suggest that it is possible to design sonification that is perceived as both interesting and fun, and convey an emotional impression, but that there may be a trade off between musical experience and clarity in sonification.
What’s the problem? interrogating actors to identify the root cause of concurrency bugs

Programs written using Communicating Event-Loops (CEL) concurrency model do not suffer from low-level data races by design but are not exempt from other concurrency bugs, such as behavioral deadlocks and message order violations. When programmers need to find the root cause of a bug, they typically ask questions about the application's behavior. However, current debugging tools are mostly operational, offering features at the source code level like breakpoints and watchpoints. Consequently, understanding the program behavior when debugging can take a lot of time for developers since questions on behaviors need to be mapped into operations in the debugger. Inspired by interrogative debugging, this paper proposes an interactive debugging approach for actor-based programs that enable developers to reason about the program output by selecting questions from a set of predefined questions about the code and the program's execution. We present the design of the questions and answers, and we describe a prototype implementation in Apgar, an online debugger for actor-based programs written in SOMns. We define questions based on key concepts of the actor model: actors, turns, messages, and promises. The debugger then computes the answers by analyzing a recorded trace of events about the program execution.
From Open Science to Open Source (and beyond): A Historical Perspective on Open Practices without and with IT

Openness as organizational philosophy and theoretical concept has continuously gained importance over the past decades. While the adoption of open practices such as open-source development or crowdsourcing is primarily academically observed in the 20th and 21st century, organizational practices adopting or facilitating openness have already been applied before there was an understanding what openness actually depicts. For centuries, public and private stakeholders utilized a broad variety of open practices such as open science, industrial exhibitions, solution sourcing or industrial democracy in order to achieve certain anticipated effects – fully in the absence of IT. Due to the missing historical understanding, this paper provides a first holistic historical perspective on the emergence of open practices, considering the context of the political, technological and societal developments. Utilizing a structured literature review, the paper puts a special focus on the historical narrative and the connection between openness without and with IT. The paper concludes that open practices are not a recent phenomenon, but were already applied successfully by affected stakeholders in previous centuries, whereas applied open practices partly build upon each other and show resembling patterns. Historically, two central shifts are identified: (1) a shift from government-driven towards organization- and community-driven open practices, and (2) a shift from mainly transparency-oriented open practices towards a stronger utilization of inclusion.
The role of fun in learning

Fun is often considered as an essential element of learning, especially among researchers in gamification and child-computer interaction. Accordingly, enjoyment of a (non-formal) learning activity or game is frequently an important design and evaluation criteria. However, while it is often declared that learning games and playful learning activities are fun for children, empirical evidence is hard to come by as measuring fun is not straightforward. Hence, to start my PhD research, I have defined fun and designed and validated a measurement tool (FunQ) for the quantitative assessment of fun. Thereafter, by using FunQ I have investigated the effect of fun on learning. My research results indicate that fun has a positive and significant effect on learning and it also has a significant indirect positive effect on learning across influencing students' attitude about the topic. In my next study, I am planning to do an intervention to see how teachers can support the experienced fun while learning, and by this, enhance students’ learning experience and their level of learning. My aim is to turn research into practice by providing teachers with guidelines for a more fun and more effective learning.
Generative type-aware mutation for testing SMT solvers

We propose Generative Type-Aware Mutation, an effective approach for testing SMT solvers. The key idea is to realize generation through the mutation of expressions rooted with parametric operators from the SMT-LIB specification. Generative Type-Aware Mutation is a hybrid of mutation-based and grammar-based fuzzing and features an infinite mutation space—overcoming a major limitation of OpFuzz, the state-of-the-art fuzzer for SMT solvers. We have realized Generative Type-Aware Mutation in a practical SMT solver bug hunting tool, TypeFuzz. During our testing period with TypeFuzz, we reported over 237 bugs in the state-of-the-art SMT solvers Z3 and CVC4. Among these, 189 bugs were confirmed and 176 bugs were fixed. Most notably, we found 18 soundness bugs in CVC4’s default mode alone. Several of them were two years latent (7/18). CVC4 has been proved to be a very stable SMT solver and has resisted several fuzzing campaigns.
User Preferences for Calming Affective Haptic Stimuli in Social Settings

This paper presents a survey informing a user-first approach to designing calming affective haptic stimuli by eliciting user preferences in different social scenarios. Prior affective haptics research presented users with stimuli and recorded emotional responses. By contrast this work focuses on the sensations users wish to experience and how these can be simulated using haptics. The survey (n=81) investigated which users preferences in four social situations to reduce social anxiety. Using thematic analysis of responses we created a coding scheme of stimuli derived from real-world experiences to emulate with affective haptics. By cross-referencing these categories with affective haptics research, we provide recommendations to designers about which calming stimuli users wish to experience socially and how they can be implemented.
Identifying Optimization Potential in the Field of Learning and Game Design by Using the Example of a Pre-Alpha Prototype of “Arctic Economy”

In recent years, and especially in times of distance learning, Serious Games have become a viable alternative to conventional teaching methods. The positive effect of Serious Games on learning motivation and learning success has already been confirmed in many studies. At the same time, educational institutions and companies are becoming more open to the use of digital Serious Games. However, the development of these games is a great challenge, because aspects of Game Design as well as aspects of Learning Design have to be included in the development. Therefore, it is important to identify deficits or optimization potentials regarding both aspects in the early stages of development already. This study uses an example to show possibilities for identifying optimization needs in early development stages and proposes improvement measures in the field of Game and Learning Design using a prototype as an example. The prototype was tested by five lecturers of the same subject domain and assessed in terms of content and didactic suitability for teaching. In addition, an AttrakDiff evaluation was performed. The results are indicative of early stakeholder involvement contributing to an effective identification process of optimization needs even in early development stages of Serious Games.
SimTyper: sound type inference for Ruby using type equality prediction

Many researchers have explored type inference for dynamic languages. However, traditional type inference computes most general types which, for complex type systems—which are often needed to type dynamic languages—can be verbose, complex, and difficult to understand. In this paper, we introduce SimTyper, a Ruby type inference system that aims to infer usable types—specifically, nominal and generic types—that match the types programmers write. SimTyper builds on InferDL, a recent Ruby type inference system that soundly combines standard type inference with heuristics. The key novelty of SimTyper is type equality prediction, a new, machine learning-based technique that predicts when method arguments or returns are likely to have the same type. SimTyper finds pairs of positions that are predicted to have the same type yet one has a verbose, overly general solution and the other has a usable solution. It then guesses the two types are equal, keeping the guess if it is consistent with the rest of the program, and discarding it if not. In this way, types inferred by SimTyper are guaranteed to be sound. To perform type equality prediction, we introduce the deep similarity (DeepSim) neural network. DeepSim is a novel machine learning classifier that follows the Siamese network architecture and uses CodeBERT, a pre-trained model, to embed source tokens into vectors that capture tokens and their contexts. DeepSim is trained on 100,000 pairs labeled with type similarity information extracted from 371 Ruby programs with manually documented, but not checked, types. We evaluated SimTyper on eight Ruby programs and found that, compared to standard type inference, SimTyper finds 69% more types that match programmer-written type information. Moreover, DeepSim can predict rare types that appear neither in the Ruby standard library nor in the training data. Our results show that type equality prediction can help type inference systems effectively produce more usable types.
Tokenfication - The potential of non-fungible tokens (NFT) for game development

In recent years, there has been an increase in the interest in non-fungible tokens (NFT) to purchase digital art and has shaped a new form of art collecting. The art community has a long history of challenges and opportunities with the commercialization of art. More recently, there has also been an interest in the purchase of game art as investments or collecting. With the advent of blockchain currencies, NFTs have introduced a way for investors, collectors, and game developers to purchase or sell game art. However, the potential of NFT for the games and game development industry is manifold. In this paper, the authors will reflect on the potential of NTF’s for game developers and content creators.
How makers responded to the PPE shortage during the COVID-19 pandemic: an analysis focused on the Hauts-de-France region

The COVID-19 pandemic led to the confinement of populations in France on the one hand and to shortages of equipment on the other hand (in particular Personal Protective Equipment). The makers therefore mobilized worldwide to produce this medical equipment. In the Hauts-de-France region, a group of makers organized to produce face shields for hospitals, public health and social care institutions and also for retailers. Our analysis of the collaborative messaging room used to coordinate the production of face shields was completed by the interview of active makers. It was based on an original tool-based integrated and hybrid (quantitative/qualitative) methodology. That work enabled us to update the profile of the participants, the intensity of their contribution, the nature of the innovation implemented, the coordination mechanisms, the associated difficulties and the role of technologies in the makers' response.
Analyze the impact of the epidemic on New York taxis by machine learning algorithms and recommendations for optimal prediction algorithms

With the growth of the economy and population, the transportation infrastructure of cities faces various challenges such as lacks of parking spots and traffic jams. One of the alternatives is to take public transport combined with proper utilization of demand-responsive transport such as Taxi or Uber. Therefore, it is valuable to improve the efficiency of these responsive transport. This research evaluates the predicting performance of different prediction models such as GBDT, XGBoost, and Random forest on taxi trip duration. At the same time, new elements were added to the modified data to contribute a higher accuracy rate including the snow status and precipitation data. Moreover, exploratory data analysis and data mining was conducted taking concerns of the Covid-19 effect of the year 2020.
What’s This? A Voice and Touch Multimodal Approach for Ambiguity Resolution in Voice Assistants

Human speech often contains ambiguity stemming from the use of demonstrative pronouns (DPs), such as “this” and “these. ” While we can typically decipher which objects of interest DPs are referring to based on context, modern day voice assistants (VAs – such as Google Assistant and Siri) are yet unable to process queries containing such ambiguity. For instance, to humans, a question such as “how much is this? ” can be clarified through visual reference (e.g., a buyer gestures to the seller the object they would like to purchase). To bridge this gap between human and machine cognition, we built and examined a touch + voice multimodal VA prototype that enables users to select key spatial information to embed as context and query the VA. The prototype converts results of mobile, real-time object recognition and optical character recognition models into augmented reality buttons that represent features. Users can interact with and modify the selected features through a word grid. We conducted a study to investigate: 1) how touch performs as an additional modality to resolve ambiguity in queries, 2) how users use DPs when interacting with VAs, and 3) how users perceive a VA that can understand DPs. From this procedure we found that as the query becomes more complex, users prefer the multimodal VA over the standard VA without experiencing elevated cognitive load. Additionally, even though it took some time getting used to, many participants eventually became comfortable with using DPs to interact with the multimodal VA and appreciated the improved human-likeness of human-VA conversations.
A Multimodal Dataset and Evaluation for Feature Estimators of Temporal Phases of Anxiety

Vicious cycles of anxiety responses underlie the onset of increasingly prevalent and highly impairing anxiety disorders and also contribute to their maintenance. Our goal is to evaluate whether different anxiety responses are evident in temporal patterns of physiological and behavioral features. Consequently, we established a rich multimodal-multisensor dataset of cardiac, electrodermal, movement, posture, and speech measures from 95 young adults during two anxiety experiments that induce social anxiety and bug-phobic anxiety. A subset of this dataset is publicly available at “Anxiety Phases Dataset” Figshare repository. We adopted a generalized mixed model approach and found that 10 out of 14 feature trajectories modeled for high- and low-anxiety groups differ significantly at 0.001 level in magnitude, creating at least two temporal phases in both groups. Further differences in magnitude, duration and the number of phases were observed for responses of confrontation, safety behaviors, escape, and avoidance in the high-anxiety group. Our findings contribute to the long-term aim of designing multimodal systems that have great potential to reduce the impacts of anxiety disorders and improve therapy.
Dyninka: a FaaS framework for distributed dataflow applications

The Internet of Things (IoT) requires applications to deal with a large amount of data - streamed, processed and stored from small devices to analytical systems. Cloud computing offers a hardware solution to this issue, providing on-demand resources to process IoT data. The newer programming paradigms simplify the use of those cloud resources. The Function-as-a-Service (FaaS) and the Serverless paradigm transform the conception of microservices applications to the definition and the composition of several callable functions. Although defined as distributed architectures - mostly publicly available solutions rely on either a gateway or an internal messaging middleware. These architectures create a single point of failure in exchange for more straightforward service to service communication. In this article, we present Dyninka, a framework to rapidly prototype FaaS-based distributed dataflow applications. Its programming model gathers the definition and the composition of services within a single file using the multitier programming paradigm and compiles them into a multitude of services deployable on cloud computing infrastructure. Dyninka is built without a gateway or a messaging platform, and services communicate directly with each other or with the cloud abstracted infrastructure. As a result, we reduce the network and the computation overheads introduced by commercial FaaS frameworks such as OpenFaaS. We validate Dyninka on a Fog computing scenario with limited resources and several load profiles. For all scenarios, Dyninka shows better stability, throughput and a reduced overhead compared to OpenFaaS.
A Systematic Cross-Corpus Analysis of Human Reactions to Robot Conversational Failures

In this paper, we analyze multimodal behavioral responses to robot failures across different tasks. Two multimodal datasets are examined in which humans interact with guided-task robots in task-oriented dialogues. In both datasets, the robots simulated failures of conversational breakdown and miscommunication typically observed in human-robot interactions. We closely examine human reactions to these failures looking at facial and acoustic features. Our analyses identify the significant behavioral features for automatic detection of such failures in interaction. We also examine human responses to different types of robot failures and if failures occurred early or late in the interaction cause variation in the responses. Our findings indicate that several nonverbal behaviors are consistently present in responses to robots’ failures, e.g., gaze and speech prosody, whereas, linguistic features appear to be task-dependent. We discuss how these findings may generalize to other tasks, and how autonomous robots may identify opportunities to detect and recover from failures in interactions with humans.
Lightweight on-stack replacement in languages with unstructured loops

On-stack replacement (OSR) is a popular technique used by just in time (JIT) compilers. A JIT can use OSR to transfer from interpreted to compiled code in the middle of execution, immediately reaping the performance benefits of compilation. This technique typically relies on loop counters, so it cannot be easily applied to languages with unstructured control flow. It is possible to reconstruct the high-level loop structures of an unstructured language using a control flow analysis, but such an analysis can be complicated, expensive, and language-specific. In this paper, we present a more lightweight strategy for OSR in unstructured languages which relies only on detecting backward jumps. We design a simple, language-agnostic API around this strategy for language interpreters. We then discuss our implementation of the API in the Truffle framework, and the design choices we made to make it efficient and correct. In our evaluation, we integrate the API with Truffle's LLVM bitcode interpreter, and find the technique is effective at improving start-up performance without harming warmed-up performance.
Direct Gaze Triggers Higher Frequency of Gaze Change: An Automatic Analysis of Dyads in Unstructured Conversation

Nonverbal cues have multiple roles in social encounters, with gaze behaviour facilitating interactions and conversational flow. In this work, we explore the conversation dynamics in dyadic settings in a free-flow discussion. Using automatic analysis (rather than manual labelling), we investigate how the gaze behaviour of one person is related to how much the other person changes their gaze (frequency in gaze change) and what their gaze target is (direct or avert gaze). Our results show that when one person is looked at they change their gaze direction with a higher frequency compared to when they are not looked at. They also tend to maintain a direct gaze to the other person when they are not looked at.
Contract-based return-value commutativity: safely exploiting contract-based commutativity for faster serializable transactions

A key challenge of designing distributed software systems is maintaining data consistency. We can define data consistency and data isolation guarantees --e.g. serializability-- in terms of schedules of atomic reads and writes, but this excludes schedules that would be semantically consistent. Others use manually provided information on "non-conflicting operations" to define guarantees that work for more applications allowing more parallel schedules. To be safe, an engineer might avoid marking operations as non-conflicting, with detrimental effects to efficiency. To be fast, they might mark more non-conflicting operations than is strictly safe. Our goal is to help engineers by automatically deriving commutative operations (using their respective contracts) such that more parallel schedules with global consistency are possible. We define a new general consistency and isolation guarantee named "Return-Value Serializability" to check consistency claims automatically, and we present distributed event processing algorithms that make use of the same "Contract-based Commutativity" information. We validated both the definitions and the algorithms using model-checking with TLA+. Previous work provided evidence that local coordination avoidance such as applied here has a significant positive effect on the performance of distributed transaction systems. Client-centric return-value commutativity promises to hit a sweet spot in design trade-offs for business applications, such as payment systems, that must scale-out while their operations are not embarrassingly parallel and consistency guarantees are of the highest priority. It can also provide design feedback, indicating that some operations will simply not scale together even before a line of code has been written.