Miracle-dz/UniFAR-FTUs huggingface dataset:
MIMO-Descriptor Systems Model Reduction

Abstract In this paper we present a new algorithm to construct different types of lower degree approximates of a high-degree linear singular system based onto singular value decomposition (SVD) approach that had the main advantage to preserve the key properties of the original system, such as stability, and give a quantization of approximation error. By the use of Schur method, model reduction is performed either on the proper and improper parts of a high-degree original system. A numerical example is provided to illustrate the behavior of approximates vis-a-vis original system.
N A ] 1 2 O ct 2 01 6 On a class of nonlinear matrix equations X ± A H f ( X ) − 1 A = Q ✩

Nonlinear matrix equations are encountered in many applications of control and engineering problems. In this work, we establish a complete study for a class of nonlinear matrix equations. With the aid of Sherman Morrison Woodbury formula, we have shown that any equation in this class has the maximal positive definite solution under a certain condition. Furthermore, A thorough study of properties about this class of matrix equations is provided. An acceleration of iterative method with R-superlinear convergence with order r > 1 is then designed to solve the maximal positive definite solution efficiently.
Design and Performance Analysis of Partially Depleted and Fully Depleted Silicon on Insulator MOSFET

In this paper, Partially Depleted Silicon on Insulator (PDSOI) MOSFET and Fully Depleted Silicon on Insulator (FDSOI) MOSFET are designed, and the impact of n-type doping concentration, work function variation, gate oxide, and silicon layer thickness on the performance of the device is studied and analyzed. The floating body and associated kink effects present in a PDSOI device are also investigated in detail. In addition to this, comparisons are made between PDSOI and FDSOI MOSFET to analyze their performance for various device parameters. The threshold voltage rises with increasing Si surface thickness and source doping, according to the data found. The drain current increases as the N-type doping concentration develops in both PD and FDSOI MOSFETs, and conduction begins after a concentration of 3x1017 cm−3 for PD and 2x1017 cm−3 for FDSOI, before which conduction is not effective. For the same n-type doping concentration and gate work function, FDSOI has a higher drain current than PDSOI. FDSOI is better than PDSOI since it eliminates PDSOI’s defects and enhances its performance. The Silvaco Atlas-2D TCAD simulator is used to simulate the device using conventional architecture and models.
B-open Defect: A Novel Defect Model in FinFET Technology

This article proposes an electrical analysis of a new defect mechanism, to be named as b-open defect, which may occur in nanometer technologies due to the use of the Self-Aligned Double Patterning (SADP) technique. In metal lines making use of the SADP technique, a single dust particle may cause the simultaneous occurrence of a bridge defect and an open defect. When the two defects impact the same gates, the electrical effects of the bridge and the open combine and exhibit a new specific electrical behavior; we call this new defect behavior a b-open. As a consequence, existing test generation methodologies may miss defect detection. The electrical behavior of the b-open defect is first analyzed graphically and then validated through extensive SPICE simulations. The test pattern conditions to detect the b-open defect are finally determined, and it is shown that the b-open defect requires specific test generation.
Player Profile as a Lens to Advocate Designing Compassionate Therapy Games for Parkinson’s disease

Therapy games aim to leverage the motivational power of video games towards increasing adherence to therapy, inducing and maintaining motivation, elevating mood, and encouraging physical and/or cognitive activity. Commercial games tend to offer an attractive gameplay, but usually do not consider the rehabilitation needs or physical and cognitive abilities of the player. In order to overcome this shortcoming many custom-built therapy games have been proposed, which focus on the rehabilitation process, but often struggle to offer a compelling enough game experience for long term engagement. In this paper we present a novel framework for helping developers to profile users of a rehabilitation game in order to support a more compassionate and effective design. The framework was derived by systematically reviewing the design and effectiveness of existing therapy games for rehabilitation of people living with Parkinson’s disease. Nonetheless, we believe the resulting framework can also be applied to therapy games for other disorders.
Do spinors give rise to a frame-dragging effect?

We investigate the effect of the intrinsic spin of a fundamental spinor field on the surrounding spacetime geometry. We show that despite the lack of a rotating stress-energy source (and despite claims to the contrary) the intrinsic spin of a spin-half fermion gives rise to a frame-dragging effect analogous to that of orbital angular momentum, even in Einstein-Hilbert gravity where torsion is constrained to be zero. This resolves a paradox regarding the counter-force needed to restore Newton's third law in the well-known spin-orbit interaction. In addition, the frame-dragging effect gives rise to a long-range gravitationally mediated spin-spin dipole interaction coupling the internal spins of two sources. We argue that despite the weakness of the interaction, the spin-spin interaction will dominate over the ordinary inverse square Newtonian interaction in any process of sufficiently high energy for quantum field theoretical effects to be non-negligible.
Unifying Recommender Systems and Conversational User Interfaces

This paper considers unifying research on conversational user interfaces and recommender systems. Studies on conversational user interfaces (CUIs) typically examine how conversations can be facilitated (i.e., optimizing the means). Recommender systems research (RecSys) aims to retrieve and present recommendations in a user’s session (i.e., optimizing the ends). Though these aims are overlapping across both areas, they can be better examined together to target the means and ends of what people can achieve with technology as conversational recommender systems (CRSs). We discuss the intersection of conversational user interfaces, recommender systems, and conversational recommender systems. We argue how conversations and recommendations can be designed holistically, in which recommendations can also be a means to foster engaging conversational interaction, while conversations as ends can better sustain curated, long-term recommendations.
Beyond Subservience: Using Joint Commitment to Enable Proactive CUIs for Mood Logging

Conversational user interfaces (CUIs) are a promising interaction modality to engage people with self-report activities that are widely used to study people’s experiences and support them with their mental health. However, this potential is limited by the prevailing CUI interaction paradigm being subservience to the user, which constrains self-reporting to being user initiated. A more effective approach would be for CUIs to proactively engage users with self-reporting, particularly at opportune moments. This paper proposes that joint action theory, specifically joint commitment, can be an effective framework to support designers in designing effective proactive CUI interactions. Using mood logging as a use case, we highlight three key areas where joint commitment can impact proactive CUI design. We also discuss wider challenges and future areas of research needed to identify the opportunities and challenges of using joint commitment within proactive CUI research and development.
Conversational Agents Replying with a Manzai-style Joke

Automated conversational agents are becoming popular in various everyday contexts. In order to fulfill a more important role in human society, people would need to feel a sense of familiarity with such agents. To achieve this, we focus on humor, which enables familiar relationships between people and agents. In this study, we propose an interaction style of a pair of conversational agents that make humorous statements in conversation with users in real time, referring to the Japanese Manzai. In this method, a pair of agents, one making the joke and the other serving as the butt of the joke, perform humor in real time. The results of the experiment using the prototype system showed that when the agents performed humorous statements during conversations with users, the humans perceived that agent as expressing a sense of humor, and their motivation to continue the conversation increased.
Lessons Learnt from Using Electrical Muscle Stimulation to Actuate Agonist and Antagonist Muscle Pairs

Electrical muscle stimulation is widely explored in human-computer interaction. While most of the studies focus on the possible applications, others are directed towards the actuated movement accuracy. Extending the latter, we tested 9 different signal designs that combine the actuation of the muscle with the actuation of its antagonist. Our results indicate that the movement is not consistent when combining two opposing actuations although single muscles can be actuated consistently. Based on our obtained results, we distill design recommendations.
Randoop-TSR: Random-based Test Generator with Test Suite Reduction

Software testing plays a very important role in the software development process. Automated test generation tools increase the effectiveness and efficiency of software testing, and alleviate the problem of low efficiency caused by writing hand-crafted test cases. However, different test case generation methods vary in the size, code coverage, and fault detection capacity of the automatically-produced test suites. Automated test case generation tool based on random testing, Randoop as a representative, randomly and incrementally generates a large number of method sequences, which gives various possible combinations of calling methods, but the size of the test suite is not proportional to test quality. Therefore, there exists a lot of redundancy in the test cases. This paper proposes Randoop-TSR, an approach for identifying and eliminating redundant test cases on the basis of Randoop to improve the process of test generation. Our approach adopts three strategies to realize the removal of redundancy, namely: (i) similarity-based input sequence selection; (ii) redundant and duplicate assert statements elimination based on test smell detection; (iii) redundant test cases elimination without breaking test requirements (i.e., code coverage and mutation score). Randoop-TSR can eliminate redundancy effectively, and greatly reduce the size of test suites and execution time. Furthermore, our approach improves the efficiency and understandability of test cases while retaining code coverage and mutation score.
Optimizing Substance Use Treatment Selection Using Reinforcement Learning

Substance use disorder (SUD) exacts a substantial economic and social cost in the United States, and it is crucial for SUD treatment providers to match patients with feasible, effective, and affordable treatment plans. The availability of large SUD patient datasets allows for machine learning techniques to predict patient-level SUD outcomes, yet there has been almost no research on whether machine learning can be used to optimize or personalize which treatment plans SUD patients receive. We use contextual bandits (a reinforcement learning technique) to optimally map patients to SUD treatment plans, based on dozens of patient-level and geographic covariates. We also use near-optimal policies to incorporate treatments’ time-intensiveness and cost into our recommendations, to aid treatment providers and policymakers in allocating treatment resources. Our personalized treatment recommendation policies are estimated to yield higher remission rates than observed in our original dataset, and they suggest clinical insights to inform future research on data-driven SUD treatment matching.
Casino Melanoma – Gamble with Your Life: An Interactive Roulette Game to Raise Awareness of Individual Skin Cancer Risk: Casino Melanom – Spiel mit Deinem Leben: Ein interaktives Roulette-Spiel zur Sensibilisierung des individuellen Hautkrebsrisikos

English – The interactive exhibition prototype Casino Melanoma playfully deals with the influence of UV radiation in connection with personal factors to draw attention to the danger of contracting skin cancer. Based on the classic game of roulette, a game is played in the categories of skin type, number of nevi (birthmarks and moles), skin cancer in personal or family history, time spent outdoors and sunburns per year. During each round, players are confronted with the increasing risk of skin cancer and the gamble of developing this disease in the coming years. A quantitative survey (n=24) and accompanying interviews were used to examine how much gambling about important health issues such as skin cancer can influence a person's future behavior. The hedonic and pragmatic quality of the prototype was evaluated positively. Its impact projection shows an enlightening and behavior-stimulating effect. In addition to raising awareness of the issue, Casino Melanoma encourages people to think about their sun exposure behavior and a possible future change in behavior. Deutsch – Der interaktive Ausstellungsprototyp Casino Melanom beschäftigt sich spielerisch mit dem Einfluss von UV-Strahlung in Verbindung mit persönlichen Faktoren, um auf die Gefahr einer Erkrankung an Hautkrebs aufmerksam zu machen. Angelehnt an das klassische Roulette-Spiel erfolgt ein Spieleinsatz in den Kategorien Hauttyp, Anzahl an Nävi (Muttermale und Leberflecke), Hautkrebs in Eigen- oder Familienanamnese, Aufenthalt im Freien und Sonnenbrände pro Jahr. Im Verlauf der einzelnen Runden werden die Spielenden sowohl mit dem steigenden Hautkrebsrisiko als auch dem Glücksspiel um eine Erkrankung in den kommenden Jahren konfrontiert. In einer quantitativen Erhebung (n=24) und begleitenden Interviews wurde überprüft, inwiefern die spielerische Vermittlung von wichtigen Gesundheitsthemen wie Hautkrebs das zukünftige Verhalten einer Person beeinflussen kann. Der hedonisch und pragmatisch positiv bewertete Prototyp und dessen Wirkungsprojektion zeigen einen aufklärenden und verhaltensanregenden Effekt. Neben der Sensibilisierung für das Thema regt Casino Melanom die Menschen zum Nachdenken über das eigene Sonnenexpositionsverhalten sowie einer möglichen Verhaltensänderung in der Zukunft an.
Adopting Just Another Digital Assistant or Establishing Social Interactions with a New Friend?: Conceptual Research Model of a Long-Term Analysis of First-Time Users’ Adoption and Social Interactions with Smart Speakers

Social interactions have been exclusive to human-human interactions, until recently. However, modern technologies with adaptive and intelligent features such as smart speakers imitate human social characteristics and appear to be allegedly social interaction partners. Research going beyond the analysis of mere usage and conceptualizing devices as interaction partners is rare, also because studying the establishment of social interactions would require analyses over time. The present paper addresses this research desideratum and introduces a concept of a longitudinal study which involves the collection of different data types from different sources (online questionnaire, providers’ log files, interview) and interdisciplinary data analytical methods (social science, data science). Its cross-sectional and longitudinal research questions ask for the adoption and the usage of the usage of smart speakers, for the establishment of social interactions with the device. In sum, this study contributes to the research area by presenting a conceptual research model, which provides a framework for the systematic long-term analysis of both the adoption of technology over time as well as the establishment of potential social interactions.
Sentiment Analysis of Participants Interactions in a Hackathon Context: The Example of a Slack Corpus

This paper presents the analysis of participants’ interactions during an online hackathon using Natural Language Processing (NLP) techniques. In particular, we explored the communication of groups facilitated by Slack focusing on the use of emojis. Our findings suggest that most used emojis are positive, while negative emojis appeared rarely. Sentiment of written messages was overall positive and could be linked to topics such as motivation or achievements. Topics about participants’ disappointment regarding their progress or the hackathon organization, technical issues and criticism were associated with negative sentiment. We envision that our work offers insights regarding online communication in group and collaborative contexts with an emphasis on group work and interest-based activities.
The Effects of Auditory Latency on Experienced First-Person Shooter Players

Latency is inherently part of every interactive system and is particularly critical in video games. Previous work shows that visual latency above 25 ms reduces game experience and player performance. However, latency does not only affect visual perception but also may influence auditory elements of video games. It is unclear if auditory latency impairs the gaming experience and player performance with the same magnitude as visual latency. Therefore, we conducted an experiment with 24 participants playing a first-person shooter game. Participants played with four levels (0 ms, 40 ms, 270 ms, and 500 ms) of controlled auditory latency to reveal effects on game experience and player performance. Our analysis shows that auditory latency in video games increases the perceived tension, decreases positive feelings towards the game, and on its highest tested level (500 ms), even causes significantly stronger associations with negative feelings towards the game. Furthermore, we found that the negative effects of auditory latency are particularly pronounced for high-skilled players. We conclude that auditory latency negatively affects video games and their players. Therefore, researchers should investigate it with the same rigor as visual latency.
Simple and Efficient Recommendation Strategy for Warm/Cold Sessions for RecSys Challenge 2022

In this paper, we describe our approach for the RecSys 2022 Challenge organized by Dressipi. The goal of the challenge is to predict which item is purchased next given sessions of users as well as metadata of items from fashion e-commerce service. One key characteristic of this problem is that most sessions only have few (lower than 3) previous views. Furthermore, a large number of sessions (about 19%) contain views and purchases of items that did not appear before. We propose the following approaches to overcome these problems. First, we introduce a simple, yet strong sequence-aware MLP that outperforms recently proposed sequential recommenders such as BERT4Rec and GRU4Rec in the given dataset. Secondly, we propose a similarity metric that captures not only item metadata but also item popularity. Lastly, we predict recommendations for different types of sessions with different serving models.
CaDeCFF: Compiler-Agnostic Deobfuscator of Control Flow Flattening

With the increasing influence of malware and various attacks, malware detection methods have been continuously proposed. However, in order to evade malware detection, code obfuscation which makes programs harder to understand, is widely used by malware writers. Control Flow Flattening (CFF) is a common control-flow obfuscation method. However, Control Flow Flattening deobfuscation tools have a low success rate for compilation-optimized binaries because the structural features on which the tools depend have changed. In order to analyze malwares efficiently, in this paper, we first systematically summarize the significant structural changes of compilation optimization and analyze the limitations of all state-of-the-art CFF deobfuscation tools. Based on the observations, we further propose and implement a compiler-agnostic deobfuscator of CFF named CaDeCFF to effectively deobfuscate compilation-optimized binaries. Specifically, CaDeCFF identifies useful blocks by data flow analysis of state variable, and recovers useful block Control Flow Graph (CFG) by selective symbolic execution. Finally, it patches binary by binary tree-based code generation. To demonstrate the effectiveness of CaDeCFF, we conduct an extensive evaluation with obfuscation benchmarks. The results demonstrate that the proposed system outperforms state-of-the-art CFF deobfuscation tools, and the success rate is increased from 39.77% to 99.62%.
Development and Validation of a German Version of the Player Experience Inventory (PXI)

The Player Experience Inventory (PXI), initially developed by Abeele et al. (2020), measures player experiences among English-speaking players. However, empirically validated translations of the PXI are sparse, limiting the use of the scale among non-English speaking players. In this paper, we address this issue by providing a translated version of the scale in German, the most widely spoken first language in the European Union. After translating the original items, we conducted a confirmatory factor analysis (N = 506) to validate the German version of the PXI. Our results confirmed a 10-factor model—which the original authors of the instrument suggested—and show that the German PXI has valid psychometric properties. While model fit, internal consistency and convergent validity were acceptable, there was room for improvement regarding discriminant validity. Based on our results, we advocate for the German PXI as a valid and reliable instrument for assessing player experiences in German-speaking samples.
Cross-Modal Repair: Gaze and Speech Interaction for List Advancement

Interacting with long lists of instructions or ingredients continues to be a challenge for conversational interaction. In this paper, we conducted a user study to experiment with the use of ‘cued-gaze’ – waiting for the user’s visual attention – to manage the delivery of instructions with a voice agent. In a Wizard-of-Oz setting, 12 participants were instructed to build a simple Lego tower by a conversational agent and were able to advance in the list using either speech interaction, or gaze interaction. The increasing use of speech agents in real-world cause users to encounter failures in interactions, so in this task the agent was designed to fail when providing the list of instruction to explore how the participants proceeded to recover from common failures. This showed that, for this use case, cross-modality repair was more effective than reformulation of speech.
Quantum Black Holes, Wall Crossing, and Mock Modular Forms

We show that the meromorphic Jacobi form that counts the quarter-BPS states in N=4 string theories can be canonically decomposed as a sum of a mock Jacobi form and an Appell-Lerch sum. The quantum degeneracies of single-centered black holes are Fourier coefficients of this mock Jacobi form, while the Appell-Lerch sum captures the degeneracies of multi-centered black holes which decay upon wall-crossing. The completion of the mock Jacobi form restores the modular symmetries expected from $AdS_3/CFT_2$ holography but has a holomorphic anomaly reflecting the non-compactness of the microscopic CFT. For every positive integral value m of the magnetic charge invariant of the black hole, our analysis leads to a special mock Jacobi form of weight two and index m, which we characterize uniquely up to a Jacobi cusp form. This family of special forms and another closely related family of weight-one forms contain almost all the known mock modular forms including the mock theta functions of Ramanujan, the generating function of Hurwitz-Kronecker class numbers, the mock modular forms appearing in the Mathieu and Umbral moonshine, as well as an infinite number of new examples.