Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-stream CNN with Learnable Architecture for Multi-source Remote Sensing Data

In this paper, we propose an efficient and generalizable framework based on deep convolutional neural network (CNN) for multi-source remote sensing data joint classification. While recent methods are mostly based on multi-stream architectures, we use group convolution to construct equivalent network architectures efficiently within a single-stream network. Based on a recent technique called dynamic grouping convolution, We further propose a network module named SepDGConv, to make group convolution hyperparameters, and thus the overall network architecture, learnable during network training. In the experiments, the proposed method is applied to ResNet and UNet, and the adjusted networks are verified on three very diverse benchmark data sets (i.e., Houston2018 data, Berlin data, and MUUFL data). Experimental results demonstrate the effectiveness of the proposed single-stream CNNs, and in particular SepG-ResNet18 improves the state-of-the-art classification overall accuracy (OA) on HS-SAR Berlin data set from 62.23% to 68.21%. In the experiments we have two interesting findings. First, using DGConv generally reduces test OA variance. Second, multi-stream is harmful to model performance if imposed to the first few layers, but becomes beneficial if applied to deeper layers. Altogether, the findings imply that multi-stream architecture, instead of being a strictly necessary component in deep learning models for multi-source remote sensing data, essentially plays the role of model regularizer. Our code is publicly available at https://github.com/yyyyangyi/CNNs-for-Multi-Source-Remote-Sensing-Data-Fusion. We hope our work can inspire novel research in the future.
Malware MultiVerse: From Automatic Logic Bomb Identification to Automatic Patching and Tracing

Malware and other suspicious software often hide behaviors and components behind logic bombs and context-sensitive execution paths. Uncovering these is essential to react against modern threats, but current solutions are not ready to detect these paths in a completely automated manner. To bridge this gap, we propose the Malware Multiverse (MalVerse), a solution able to inspect multiple execution paths via symbolic execution aiming to discover function inputs and returns that trigger malicious behaviors. MalVerse automatically patches the context-sensitive functions with the identified symbolic values to allow the software execution in a traditional sandbox. We implemented MalVerse on top of angr and evaluated it with a set of Linux and Windows evasive samples. We found that MalVerse was able to generate automatic patches for the most common evasion techniques (e.g., ptrace checks).
Potential Socioeconomic Effects of the COVID-19 Pandemic on Neural Development, Mental Health, and K-12 Educational Achievement

The Novel Coronavirus (COVID-19) pandemic can affect more than a child’s biological health. Lack of in-person schooling and increased stress can affect neurodevelopment, mental health, and later life outcomes, especially for students who are from low socioeconomic status (SES) households. Insights from neuroscience on child development reveal potential neural mechanisms and educational outcomes likely disrupted by the pandemic—and how this will disproportionally affect low-SES children. Three policies can combat these educational and emotional effects: increased access to online resources, investments in social-emotional health, and increased access to summer/out-of-school learning. Integrating the traditionally separate fields of neuroscience and educational research will be critical for developing and assessing the most impactful policies to improve the well-being and educational achievement of our most disadvantaged children.
A [in]Segurança dos Sistemas Governamentais Brasileiros: Um Estudo de Caso em Sistemas Web e Redes Abertas

Whereas the world relies on computer systems for providing public services, there is a lack of academic work that systematically assess the security of government systems. To partially fill this gap, we conducted a security evaluation of publicly available systems from public institutions. We revisited OWASP top-10 and identified multiple vulnerabilities in deployed services by scanning public government networks. Overall, the unprotected services found have inadequate security level, which must be properly discussed and addressed.
Effects of Avatar Appearance and Locomotion on Co-Presence in Virtual Reality Collaborations

In this paper we share the results of a user study designed to investigate the impact of locomotion techniques and avatar appearances in multi-user virtual reality (VR) settings. In our study we compared continuous joystick-based and non-continuous teleportation-based locomotion with regards to the observer’s perceived co-presence, cybersickness, fairness and enjoyment in a competitive game environment. Likewise, we compared the effects of humanoid and abstract avatar appearances. The results revealed significant effects of the locomotion type on perceived co-presence and subjective fairness, such that continuous locomotion yielded significantly higher perceived co-presence and fairness. However, there were no significant differences between the avatar appearances except for a weak positive effect of realistic avatars on mutual awareness. These findings suggest that a greater emphasis should be put on the visual representation of teleportation-based navigation in multi-user VR, for instance, by animating positional transitions. They further suggest that a distinction for the effect of avatar appearance has to be made based on the context of the multi-user VR application. While previous work showed a positive effect of realistic avatars on co-presence during cooperative tasks, we found no such effect in a competitive setting.
The Roles and Challenges of Computing Terminology in Non-Computing Disciplines

Aspects of computing are used in all professions. Regardless of academic discipline, graduates require ever-improving computing competencies – applied in their own disciplinary contexts – to become proficient experts in their chosen careers, as well as informed members of society. However, teaching and learning contextually relevant computing competencies is challenging. One such challenge is the effective use of computing terminology. Terminology is fundamental to most human activities and is critical in making or breaking the interfaces where disciplines meet. Notably, we found very little work on the use of computing terminology in non-computing contexts and none utilising interviews from educators in such areas. In this qualitative research, we interviewed 20 non-computing university-level lecturers from a very broad set of disciplines in Ireland. Interviews focused on the role of computing terminology in the teaching and learning of contextually relevant computing concepts in their own disciplines, including barriers presented and methods to overcome them. This revealed a landscape as diverse as the disciplines themselves including a range of uses, issues, and approaches related to computing terminology. Although each story is unique, they all fit into a coherent spectrum, indicating that educators in one discipline would likely benefit from knowing how those in other disciplines have dealt with similar issues. They also reveal useful insights on contextual computing for computing educators. We also made several unexpected findings that are important considerations for how computing is taught and learned in other disciplines.
An in silico kinetic model of 8-oxo-7,8-dihydro-2-deoxyguanosine and 8-oxo-7,8-dihydroguanosine metabolism from intracellular formation to urinary excretion

Abstract Oxidatively generated DNA damage is of paramount importance in a wide range of physiological and pathophysiological processes. Urinary 8-oxo-7,8-dihydro-2-deoxyguanosine (8-oxodG) is often used as an outcome marker in studies on the role of oxidatively generated DNA damage, but its exact relation to intracellular damage levels and variations in DNA repair have been unclear. Using a new approach of quantitative kinetic modeling inspired by pharmacokinetics, we find evidence that in steady state – i.e. when systemic consequences of given change in damage or cellular removal rates have stabilized - the urinary excretion of 8-oxodG is closely correlated to rates of damage and intracellular 8-oxodG levels, but independent of the rate of cellular removal. Steady state was calculated to occur within approximately 12 h. A similar pattern was observed in a model of the corresponding RNA marker 8-oxo-7,8-dihydroguanosine (8-oxoGuo), but with steady-state occurring slower (up to 5 d). These data have significant implications for the planning of studies and interpretation of data involving urinary 8-oxodG/8-oxoGuo excretion as outcome. Highlights The kinetics of 8-oxodG/8-oxoGuo formation, removal and excretion were simulated in silico. The model was based on existing data on 8-oxodG/8-oxoGuo levels and removal/excretion rates. Intracellular 8-oxodG/8-oxoGuo was closely correlated with urinary excretion in steady state. Changes in removal rates did not influence urinary excretion of 8-oxodG/8-oxoGuo.
An Essential Role of the N-Terminal Region of ACSL1 in Linking Free Fatty Acids to Mitochondrial β-Oxidation in C2C12 Myotubes

Free fatty acids are converted to acyl-CoA by long-chain acyl-CoA synthetases (ACSLs) before entering into metabolic pathways for lipid biosynthesis or degradation. ACSL family members have highly conserved amino acid sequences except for their N-terminal regions. Several reports have shown that ACSL1, among the ACSLs, is located in mitochondria and mainly leads fatty acids to the β-oxidation pathway in various cell types. In this study, we investigated how ACSL1 was localized in mitochondria and whether ACSL1 overexpression affected fatty acid oxidation (FAO) rates in C2C12 myotubes. We generated an ACSL1 mutant in which the N-terminal 100 amino acids were deleted and compared its localization and function with those of the ACSL1 wild type. We found that ACSL1 adjoined the outer membrane of mitochondria through interaction of its N-terminal region with carnitine palmitoyltransferase-1b (CPT1b) in C2C12 myotubes. In addition, overexpressed ACSL1, but not the ACSL1 mutant, increased FAO, and ameliorated palmitate-induced insulin resistance in C2C12 myotubes. These results suggested that targeting of ACSL1 to mitochondria is essential in increasing FAO in myotubes, which can reduce insulin resistance in obesity and related metabolic disorders.
Recommendations at Videoland

With the largest commercial TV channels in the Netherlands, RTL plays an important role in society. We reach over 85% of Dutch people on a weekly basis, spending on average around 45 minutes a day with us and our content. We feel a part of all of the Netherlands. We actively pick up the challenge to show a diverse and representative view in the media. Videoland is our subscription-based video-on-demand (VOD) platform, with over a million subscribers and thousands of local and international titles. Our daily challenges on Videoland revolve around supporting our users in discovering new content, within a mix of editorial and personalized content. In this talk, we detail the challenges and practical solutions in building the recommender systems for Videoland. In part 1, we discuss the tweaks and algorithms we deployed in this commercial settings. We show their impact using offline evaluations and online experiments with our users. In part 2, we discuss what we learned in developing novel algorithms based on sequences of user behavior.
The Interplay between Personal Relationships & Shoulder Surfing Mitigation

Shoulder surfing refers to observing someone’s device screen without their consent. Conspicuously switching off the screen upon noticing a friend observing private messages may create an embarrassing situation. Initial evidence indicates that users adopt strategies to mitigate shoulder surfing based on their relationship to the observer. However, the social implications of such mitigation strategies remain largely unexplored. We present findings from an interview study with 12 participants to address this. We analyze experiences with shoulder surfers of different relationships to the user and collect feedback on eleven state-of-the-arts strategies for mitigating shoulder surfing. We show that the user-observer relationship impacts the choice of mitigation methods and that users often do not want observers to know they were caught. Based on our results, we conclude with implications for designing socially acceptable privacy protection mechanisms on mobile devices.
Assessing Large-Scale Power Relations among Locations from Mobility Data

The pervasiveness of smartphones has shaped our lives, social norms, and the structure that dictates human behavior. They now directly influence how individuals demand resources or interact with network services. From this scenario, identifying key locations in cities is fundamental for the investigation of human mobility and also for the understanding of social problems. In this context, we propose the first graph-based methodology in the literature to quantify the power of Point-of-Interests (POIs) over its vicinity by means of user mobility trajectories. Different from literature, we consider the flow of people in our analysis, instead of the number of neighbor POIs or their structural locations in the city. Thus, we modeled POI’s visits using the multiflow graph model where each POI is a node and the transitions of users among POIs are a weighted direct edge. Using this multiflow graph model, we compute the attract, support, and independence powers. The attract power and support power measure how many visits a POI gathers from and disseminate over its neighborhood, respectively. Moreover, the independence power captures the capacity of a POI to receive visitors independently from other POIs. We tested our methodology on well-known university campus mobility datasets and validated on Location-Based Social Networks (LBSNs) datasets from various cities around the world. Our findings show that in university campus: (i) buildings have low support power and attract power; (ii) people tend to move over a few buildings and spend most of their time in the same building; and (iii) there is a slight dependence among buildings, even those with high independence power receive user visits from other buildings on campus. Globally, we reveal that (i) our metrics capture places that impact the number of visits in their neighborhood; (ii) cities in the same continent have similar independence patterns; and (iii) places with a high number of visitation and city central areas are the regions with the highest degree of independence.
The Influence of Spatial Representation on Remote Peer Consultation: A Study on Mixed Reality Remote Support for Choosing Furniture

This paper presents a study in which we investigated the use of different devices and mechanisms to implement Mixed Reality settings for peer recommendations on furniture. In particular, we developed a mechanism to scan rooms for new furniture to be placed in, make the resulting 3D environment available to users and display virtual furniture in it. In an exploratory study, we asked 21 participants to use this environment to support an imaginary friend in their buying decision on a couch for their living room. This was done in five conditions, including a static photo of the living room, 3D representations on mobile phones and PC screens as well as mobile (cardboard-based) VR and a dedicated VR HMD. In the study, we wanted to explore how display size, 3D experience and the fidelity of HMDs affect the quality of support for recommendations by the remote peers. Our results show that the screen-based and VR-based 3D mechanisms outperform a simple photo and that VR HMDs created highest perceptions of spatial presence and were preferred by users. Surprisingly, users perceived all mechanisms showing the 3D environment to create similar qualities of support for their recommendations. We analyze these findings and derive design recommendations from them.
FINN.no Slates Dataset: A new Sequential Dataset Logging Interactions, all Viewed Items and Click Responses/No-Click for Recommender Systems Research

We present a novel recommender systems dataset that records the sequential interactions between users and an online marketplace. The users are sequentially presented with both recommendations and search results in the form of ranked lists of items, called slates, from the marketplace. The dataset includes the presented slates at each round, whether the user clicked on any of these items and which item the user clicked on. Although the usage of exposure data in recommender systems is growing, to our knowledge there is no open large-scale recommender systems dataset that includes the slates of items presented to the users at each interaction. As a result, most articles on recommender systems do not utilize this exposure information. Instead, the proposed models only depend on the user's click responses, and assume that the user is exposed to all the items in the item universe at each step, often called uniform candidate sampling. This is an incomplete assumption, as it takes into account items the user might not have been exposed to. This way items might be incorrectly considered as not of interest to the user. Taking into account the actually shown slates allows the models to use a more natural likelihood, based on the click probability given the exposure set of items, as is prevalent in the bandit and reinforcement learning literature. \cite{Eide2021DynamicSampling} shows that likelihoods based on uniform candidate sampling (and similar assumptions) are implicitly assuming that the platform only shows the most relevant items to the user. This causes the recommender system to implicitly reinforce feedback loops and to be biased towards previously exposed items to the user.
Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?

Offline evaluation is a popular approach to determine the best algorithm in terms of the chosen quality metric. However, if the chosen metric calculates something unexpected, this miscommunication can lead to poor decisions and wrong conclusions. In this paper, we thoroughly investigate quality metrics used for recommender systems evaluation. We look at the practical aspect of implementations found in modern RecSys libraries and at the theoretical aspect of definitions in academic papers. We find that Precision is the only metric universally understood among papers and libraries, while other metrics may have different interpretations. Metrics implemented in different libraries sometimes have the same name but measure different things, which leads to different results given the same input. When defining metrics in an academic paper, authors sometimes omit explicit formulations or give references that do not contain explanations either. In 47% of cases, we cannot easily know how the metric is defined because the definition is not clear or absent. These findings highlight yet another difficulty in recommender system evaluation and call for a more detailed description of evaluation protocols.
Strong experimental support for the hologenome hypothesis revealed from Drosophila melanogaster selection lines

Recently it has been proposed, that the holobiont, i.e., the host and its associated microbiome, constitute a distinct biological entity, on which selection operates. This is a fascinating idea that so far has limited empirical justification. Here Drosophila melanogaster lines from a large-scale artificial selection experiment, where we selected for stress resistance traits and for longevity, were used to test the hologenome hypothesis. We raised flies from all selection regimes, including a regime where flies were kept at benign standard laboratory condition (control regime) throughout the duration of the experiment, under common garden conditions and sequenced the microbiome of the flies. We found abundant differences in microbial communities between control and selection regimes, but not between replicate lines within the regimes, and microbial diversity was higher in selected relative to control lines. Several major core Drosophila bacterial species were differentially abundant in the different selection regimes despite flies being exposed to similar nutritional and general environmental conditions. Our results support the idea that the host and microbiome genomes have evolved in concert and provide experimental support for the hologenome theory of evolution.
Hydrodeoxygenation of Lignin-Derived Phenols: From Fundamental Studies towards Industrial Applications

Hydrodeoxygenation (HDO) of bio-oils, lignin and their model compounds is summarized in this review. The main emphasis is put on elucidating the reaction network, catalyst stability and time-on-stream behavior, in order to better understand the prerequisite for industrial utilization of biomass in HDO to produce fuels and chemicals. The results have shown that more oxygenated feedstock, selection of temperature and pressure as well as presence of certain catalyst poisons or co-feed have a prominent role in the HDO of real biomass. Theoretical considerations, such as density function theory (DFT) calculations, were also considered, giving scientific background for the further development of HDO of real biomass.
Soliciting User Preferences in Conversational Recommender Systems via Usage-related Questions

A key distinguishing feature of conversational recommender systems over traditional recommender systems is their ability to elicit user preferences using natural language. Currently, the predominant approach to preference elicitation is to ask questions directly about items or item attributes. These strategies do not perform well in cases where the user does not have sufficient knowledge of the target domain to answer such questions. Conversely, in a shopping setting, talking about the planned use of items does not present any difficulties, even for those that are new to a domain. In this paper, we propose a novel approach to preference elicitation by asking implicit questions based on item usage. Our approach consists of two main steps. First, we identify the sentences from a large review corpus that contain information about item usage. Then, we generate implicit preference elicitation questions from those sentences using a neural text-to-text model. The main contributions of this work also include a multi-stage data annotation protocol using crowdsourcing for collecting high-quality labeled training data for the neural model. We show that out approach is effective in selecting review sentences and transforming them to elicitation questions, even with limited training data.