Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Multiple Code Representations to Prioritize Static Analysis Warnings

In order to ensure the quality of software and prevent attacks from hackers on critical systems, static analysis tools are frequently utilized to detect vulnerabilities in the early development phase. However, these tools often report a large number of warnings with a high false-positive rate, which causes many difficulties for developers. In this paper, we introduce VULRG, a novel approach to address this problem. Specifically, VuLRG predicts and ranks the warnings based on their likelihoods to be true positives. To predict these likelihoods, VuLRG combines two deep learning models CNN and BiGRU to capture the context of each warning in terms of program syntax, control flow, and program dependence. Our experimental results on a real-world dataset of 6,620 warnings show that VuLRG’s Recall at Top-50% is 90%. This means that using VuLRG, 90% of the vulnerabilities can be found by examining only 50% of the warnings. Moreover, at Top-5%, VULRG can improve the state-of-the-art approach by +30% in both Precision and Recall.
Speech Enhancement with Perceptually-motivated Optimization and Dual Transformations

To address the monaural speech enhancement problem, numerous research studies have been conducted to enhance speech via operations either in time-domain on the inner-domain learned from the speech mixture or in time-frequency domain on the fixed full-band short time Fourier transform (STFT) spectrograms. Very recently, a few studies on sub-band based speech enhancement have been proposed. By enhancing speech via operations on sub-band spectrograms, those studies demonstrated competitive performances on the benchmark dataset of DNS2020. Despite attractive, this new research direction has not been fully explored and there is still room for improvement. As such, in this study, we delve into the latest research direction and propose a sub-band based speech enhancement system with perceptually-motivated optimization and dual transformations, called PT-FSE. Specially, our proposed PT-FSE model improves its backbone, a full-band and sub-band fusion model, by three efforts. First, we design a frequency transformation module that aims to strengthen the global frequency correlation. Then a temporal transformation is introduced to capture long range temporal contexts. Lastly, a novel loss, with leverage of properties of human auditory perception, is proposed to facilitate the model to focus on low frequency enhancement. To validate the effectiveness of our proposed model, extensive experiments are conducted on the DNS2020 dataset. Experimental results show that our PT-FSE system achieves substantial improvements over its backbone, but also outperforms the current state-of-the-art while being 27% smaller than the SOTA. With average NB-PESQ of 3.57 on the benchmark dataset, our system offers the best speech enhancement results reported till date.
Stochastic Gradient Descent Captures How Children Learn About Physics

As children grow older, they develop an intuitive understanding of the physical processes around them. They move along developmental trajectories, which have been mapped out extensively in previous empirical research. We investigate how children's developmental trajectories compare to the learning trajectories of artificial systems. Specifically, we examine the idea that cognitive development results from some form of stochastic optimization procedure. For this purpose, we train a modern generative neural network model using stochastic gradient descent. We then use methods from the developmental psychology literature to probe the physical understanding of this model at different degrees of optimization. We find that the model's learning trajectory captures the developmental trajectories of children, thereby providing support to the idea of development as stochastic optimization.
Multi-Agent Exploration of an Unknown Sparse Landmark Complex via Deep Reinforcement Learning

In recent years Landmark Complexes have been successfully employed for localization-free and metric-free autonomous exploration using a group of sensing-limited and communication-limited robots in a GPS-denied environment. To ensure rapid and complete exploration, existing works make assumptions on the density and distribution of landmarks in the environment. These assumptions may be overly restrictive, especially in hazardous environments where landmarks may be destroyed or completely missing. In this paper, we first propose a deep reinforcement learning framework for multi-agent cooperative exploration in environments with sparse landmarks while reducing client-server communication. By leveraging recent development on partial observability and credit assignment, our framework can train the exploration policy efficiently for multi-robot systems. The policy receives individual rewards from actions based on a proximity sensor with limited range and resolution, which is combined with group rewards to encourage collaborative exploration and construction of the Landmark Complex through observation of 0-, 1- and 2-dimensional simplices. In addition, we employ a three-stage curriculum learning strategy to mitigate the reward sparsity by gradually adding random obstacles and destroying random landmarks. Experiments in simulation demonstrate that our method outperforms the state-of-the-art landmark complex exploration method in efficiency among different environments with sparse landmarks.
Realistic Hair Synthesis with Generative Adversarial Networks

REALISTIC HAIR SYNTHESIS WITH GENERATIVE ADVERSARIAL NETWORKS PEKTAŞ, Muhammed MSc in Computer Engineering Supervisor: Prof. Dr. Aybars UĞUR January 2022, 54 pages Recent successes in generative modeling have accelerated studies on this subject and attracted the attention of researchers. One of the most important methods used to achieve this success is Generative Adversarial Networks (GANs). It has many application areas such as; virtual reality (VR), augmented reality (AR), super resolution, image enhancement. Despite the recent advances in hair synthesis and style transfer using deep learning and generative modelling, due to the complex nature of hair still contains unsolved challenges. The methods proposed in the literature to solve this problem generally focus on making high-quality hair edits on images. In this thesis, a generative adversarial network method is proposed to solve the hair synthesis problem. While developing this method, it is aimed to achieve real-time hair synthesis while achieving visual outputs that compete with the best methods in the literature. The proposed method was trained with the FFHQ dataset and then its results in hair style transfer and hair reconstruction tasks were evaluated. The results obtained in these tasks and the operating time of the method were compared with MichiGAN, one of the best methods in the literature. The comparison was made at a resolution of 128x128. As a result of the comparison, it has been shown that the proposed method achieves competitive results with MichiGAN in terms of realistic hair synthesis, and performs better in terms of operating time.
Myopia prediction for adolescents via time-aware deep learning

Background: Quantitative prediction of the adolescents’ spherical equivalent based on their variablelength historical vision records. Methods: From October 2019 to March 2022, we examined binocular uncorrected visual acuity, axial length, corneal curvature, and axial of 75,172 eyes from 37,586 adolescents aged 6-20 years in Chengdu, China. 80% samples consist of the training set and the remaining 20% form the testing set. Time-Aware Long Short-Term Memory was used to quantitatively predict the adolescents’ spherical equivalent within two and a half years. Result: The mean absolute prediction error on the testing set was 0.273 ± 0.257 for spherical equivalent, ranging from 0.189 ± 0.160 to 0.596 ± 0.473 if we consider different lengths of historical records and different prediction durations. Conclusions: Time-Aware Long Short-Term Memory was applied to captured the temporal features in irregularly sampled time series, which is more in line with the characteristics of real data and thus has higher applicability, and helps to identify the progression of myopia earlier. The overall error 0.273 is much smaller than the criterion for clinically acceptable prediction, say 0.75.
Risk-Aware Model Predictive Path Integral Control Using Conditional Value-at-Risk

In this paper, we present a novel Model Predictive Control method for autonomous robot planning and control subject to arbitrary forms of uncertainty. The proposed Risk-Aware Model Predictive Path Integral (RA-MPPI) control utilizes the Conditional Value-at-Risk (CVaR) measure to generate optimal control actions for safety-critical robotic applications. Different from most existing Stochastic MPCs and CVaR optimization methods that linearize the original dynamics and formulate control tasks as convex programs, the proposed method directly uses the original dynamics without restricting the form of the cost functions or the noise. We apply the novel RA-MPPI controller to an autonomous vehicle to perform aggressive driving maneuvers in cluttered environments. Our simulations and experiments show that the proposed RA-MPPI controller can achieve similar lap times with the baseline MPPI controller while encountering significantly fewer collisions. The proposed controller performs online computation at an update frequency of up to 80 Hz, utilizing modern Graphics Processing Units (GPUs) to multi-thread the generation of trajectories as well as the CVaR values.
The 2-24 μm source counts from the AKARI North Ecliptic Pole survey

We present herein galaxy number counts of the nine bands in the 2-24 micron range on the basis of the AKARI North Ecliptic Pole (NEP) surveys. The number counts are derived from NEP-deep and NEP-wide surveys, which cover areas of 0.5 and 5.8 deg2, respectively. To produce reliable number counts, the sources were extracted from recently updated images. Completeness and difference between observed and intrinsic magnitudes were corrected by Monte Carlo simulation. Stellar counts were subtracted by using the stellar fraction estimated from optical data. The resultant source counts are given down to the 80% completeness limit; 0.18, 0.16, 0.10, 0.05, 0.06, 0.10, 0.15, 0.16, and 0.44 mJy in the 2.4, 3.2, 4.1, 7, 9, 11, 15, 18 and 24 um bands, respectively. On the bright side of all bands, the count distribution is flat, consistent with the Euclidean Universe, while on the faint side, the counts deviate, suggesting that the galaxy population of the distant universe is evolving. These results are generally consistent with previous galaxy counts in similar wavebands. We also compare our counts with evolutionary models and find them in good agreements. By integrating the models down to the 80% completeness limits, we calculate that the AKARI NEP-survey revolves 20%-50% of the cosmic infrared background, depending on the wavebands.
ASP: An Aerospace Specification Process for Hardware Logic

This work intends to specify a process to develop aerospace devices that use VHDL (Very High-Speed Integrated Circuit (VHSIC) Hardware Description Language) to describe the digital hardware logic and assure all safety-critical features involved in the process were assessed. This work aims to a process that goes from the requirements to the logic implementation at the component described level. The research methodology contains 4 phases (Phases 1 to 4). We have already completed Phases 1 and 2. Currently, we are working on Phase 3. This work is being developed as part of a master’s degree to be concluded by the end of 2023 inside of the Postgraduate
Draft Genome Sequence of Bacillus subtilis YBS29, a Potential Fish Probiotic That Prevents Motile Aeromonas Septicemia in Labeo rohita

We report the draft genome sequence of the promising fish probiotic Bacillus subtilis YBS29. This strain exhibits in vitro antimicrobial activity against Aeromonas veronii and enhances growth and disease resistance in the Indian major carp species Labeo rohita against motile Aeromonas septicemia (MAS). Its genome contains a gene cluster encoding multiple bacteriocins and lacks genes for virulence factors. These genomic features signify potential for safe use as a probiotic in aquaculture. ABSTRACT We report the draft genome sequence of the promising fish probiotic Bacillus subtilis YBS29. This strain exhibits in vitro antimicrobial activity against Aeromonas veronii and enhances growth and disease resistance in the Indian major carp species Labeo rohita against motile Aeromonas septicemia (MAS). Its genome contains a gene cluster encoding multiple bacteriocins and lacks genes for virulence factors. These genomic features signify potential for safe use as a probiotic in aquaculture.
Genome Sequences of the Human-Skin-Originated Brevundimonas albigilva TT17 and the Soil-Originated B. albigilva KEME 9005-016T 

Brevundimonas albigilva TT17 was isolated from human skin. This strain can grow on Triton X-100. Here, we present the whole-genome sequences of this strain and B. albigilva KEME 9005-016T. This result demonstrates the first information on the genome sequences of the species of B. albigilva. ABSTRACT Brevundimonas albigilva TT17 was isolated from human skin. This strain can grow on Triton X-100. Here, we present the whole-genome sequences of this strain and B. albigilva KEME 9005-016T. This result demonstrates the first information on the genome sequences of the species of B. albigilva.
Medication time? A User Experience Evaluation of Mobile Applications targeting People with Diabetes

Diabetes is a chronic disease resulting from a lack of insulin, which permanently alters blood sugar levels. Research by the International Diabetes Federation (IDF) in the year 2021 shows that about 10% of adults aged between 20 and 79 years of the world population live with diabetes. In this context, the use of mobile apps has assisted in monitoring these patients. In this research, the experience of using mobile applications for healthcare patients with diabetes in their daily activities was investigated. For the evaluation, 5 Android mobile applications were selected for this purpose and 10 participants were invited who were people diagnosed with diabetes. To characterize the target audience, a questionnaire was applied to define the profile of the study participants. Subsequently, each participant chose two apps and followed the script for using the chosen apps. To evaluate the user experience (UX) used the User Experience Questionnaire (UEQ), which assesses aspects of usability (efficiency, perspicuity, and dependability) and user experience (novelty and stimulation). Among the 5 apps evaluated, one of the apps presented problems regarding the stimulus and novelty aspects. The aspect with the lowest Likert scale value among all the apps was a novelty. Thus, the results indicated that most apps present a creative design and an attractive interface according to the users.
Designing a Mobile App with Patients with Discordant Chronic Comorbidities (DCCs): a Usability Study

Patients with complex conditions and treatment plans often find it challenging to prioritize various management tasks. The challenge is even greater for patients with discordant chronic comorbidities (DCCs). DCCs occur when a patient has conditions that have unrelated and/or conflicting treatment plans. In our prior work, we explored patients’ perspectives and providers’ perspectives on DCCs care and proposed a conceptual care model. The model suggested tools to support patients with DCCs in every stage of management. We used these findings to guide a design of a mobile application (App). We then conducted a usability study with 12 DCCs patients to understand how they might use this App to navigate through their complex needs. The participants provided insightful comments regarding: i) visual aesthetics user interfaces, ii) prioritization and goal setting, iii) and user experience. We incorporated these suggestions when re-designing our App and suggestion future design directions.
Supporting Awareness of Visual Impairments and Accessibility Reflections through Video Demos and Design Cards

Disabled people’s experiences and knowledge are oftentimes not central in design processes. Further, the burden of outreach and sensitising others to these experiences and knowledge is frequently not recognised. This paper offers a workshop approach for including disabled people in the early stages of design and supporting accessibility awareness among non-disabled design practitioners. Our approach and associated tools—designed to help support this deeper participatory work—bring together users, researchers and design specialists with different visual abilities (blind, partially sighted and sighted). We describe how these groups were engaged with video demos and reflective design cards for prompting conversations about technology, accessibility, and visual impairments (VI). Eight online workshops were conducted with 17 participants (2-3 participants per session) and found varied types of interactions between them. Overall, the approach and tools enabled participants to learn about, share, and reflect on how technologies are used by visually impaired people (VIP).
Effects of Humanlikeness and Conversational Breakdown on Trust in Chatbots for Customer Service

Trust in chatbots can be shaped by various factors such as humanlikeness in terms of visual appearance and conversational content, and conversational performance in terms of the chatbot’s ability to avoid conversational breakdown. The literature is inconclusive concerning the effect of humanlikeness and conversational performance on trust, especially their interaction effect. To examine the relations among these variables, we conducted a 2x3 (humanlikeness x conversational performance) factorial experiment with 251 participants, who were asked to perform three tasks with a chatbot for an online bank under one of the six conditions. Participants completed a questionnaire measuring trust and commented on trust factors. Results of between-group analysis showed that for the task with seeded breakdowns there were significant differences in trust across the six groups with the lowest ratings for the two groups experiencing breakdowns without repairs and that humanlikeness did not impact the extent to which the trust level changed. Results of within-group analysis showed significant differences in trust across the tasks but non-significant inter-task correlations on trust for the two groups. These observations challenge the effect of humanlikeness on trust while supporting the notion of trust resilience as the participants did not spill the impaired trust over the subsequent task. Thematic analysis showed that inter-group contrasts could be found for the theme ‘underlying functionality’ and ‘affective responses. ’ Implications for research, practice and future work were drawn.
Human-ChatBot Interaction: measuring the psychophysiological reactions of chatbot users

Affective computing and its applications have gained popularity in recent years. However, recognizing emotions is a challenge. In Computing, the artifacts used to recognize emotions are instruments of self-report or data collection through biological sensors. Interaction through chatbots, in turn, has grown and brings with it challenges related to how effective the system is and how satisfied the user is when using it. We carried out an experiment with the aim of learning more about the characteristics of human-non-human interaction. As a result, the expressive responses of users and the chatbots with whom they interacted are the focus of this human-chatbot interaction paper. We gathered face registration, body movements, self-report, and peripheral signals such as the electrocardiogram (ECG), valence, and arousal estimations of the various emotional changes during the engagement period with the Bob chatbot, a conversational recommendation system, that operates through the WhatsApp app, and recommends restaurants to the user based on information such as location. The results were encouraging; users felt comfortable and were receptive to a new conversational tool and the ECG sensors attached to the thorax, which motivates us to make improvements for future tests.
How Politicians Communicate in Social Media: A Cross-Platform Study

With the advent of social media, politicians have access to a new way of communicating with their constituents. It made it possible for politicians without television time, considered essential for a campaign, to expose their ideas and reach a large part of the electorate. As it is a relatively recent phenomenon, researchers from different areas of knowledge have found fertile ground to carry out their research. However, most studies focus on analyzing politicians’ communication on only one social media. In this work, we performed a cross-platform analysis of the communication of Brazilian politicians on Facebook, Instagram, and Twitter. We quantified the posts of these politicians, how much they replicate content and the level of engagement on each of the three social networks. In addition, we grouped and characterized the profiles and communication strategies used by these politicians. As a result, we observed that the majority post on the three social networks, there is diversity in the strategic choice of the main media and there are many politicians who only replicate content on the three networks. However, some post a lot of messages and adapt the content for each network, which suggests management by communication professionals on social media. We also verified that there are several communication profiles, from comical to controversial, with the eclectic profile prevailing, which diversifies the style of the posts.
An Approach for Sensory Effects Dispersion Simulation with Computational Fluid Dynamics

The inclusion of sensory effects in multimedia applications has the potential to increase the Quality of Experience (QoE) and improve users immersion. However, authoring such applications presents challenges arising from the need to control the rendering of sensory effects in the physical environment along with the presentation of their counterpart in the multimedia application and the constantly changing sensory effects state according to user interaction. Computational Fluid Dynamics (CFD) techniques can be used to simulate the sensory effects in a virtual environment and use the generated data to automatically control actuators. In this work, we propose an architecture to simulate wind sensory effects in an interactive real-time application and validate it using a CFD method. Data from simulation is then used to infer propagation delay and the wind temperature at the user position.
Feeling Air: Exploring Aesthetic and Material Qualities of Architectural Inflatables

This work explores aesthetic, material, and experiential qualities of inflatable architecture. We created large-scale inflatable structures—from several stories high, to 15m long, to filling a plaza with an inflatables assemblage—in public space. Working from a first-person approach, we offer somaesthetic, material, and practical reflections and design considerations for architectural inflatables. Our findings detail how such large scale inflatables can alter sensory perception in compelling ways. Our discussion suggests future directions for sensory engagements with architectural inflatables.