Miracle-dz/UniFAR-FTUs huggingface dataset:
A Large-scale Investigation into Geodifferences in Mobile Apps

Recent studies on the web ecosystem have been raising alarms on the increasing geodifferences in access to Internet content and services due to Internet censorship and geoblocking. However, geodifferences in the mobile app ecosystem have received limited attention, even though apps are central to how mobile users communicate and consume Internet content. We present the ﬁrst large-scale measurement study of geodifferences in the mobile app ecosystem. We design a semi-automatic, parallel measurement testbed that we use to collect 5,684 popular apps from Google Play in 26 countries. In all, we collected 117,233 apk ﬁles and 112,607 privacy policies for those apps. Our results show high amounts of geoblocking with 3,672 apps geoblocked in at least one of our countries. While our data corroborates anecdotal evidence of takedowns due to government requests, unlike common perception, we ﬁnd that blocking by developers is signiﬁcantly higher than takedowns in all our countries, and has the most inﬂuence on geoblocking in the mobile app ecosystem. We also ﬁnd instances of developers releasing different app versions to different countries, some with weaker security settings or privacy disclosures that expose users to higher security and privacy risks. We provide recommendations for app market proprietors to address the issues discovered.
National Evaluation of Safe Start Promising Approaches Results Appendix N: Toledo, Ohio

The Toledo Safe Start Program implemented a Child-Parent Psychotherapy (CPP) program for children (ages 0–5) who had been exposed to domestic violence. A full description of the program can be found in National Evaluation of Safe Start Promising Approaches: Assessing Program Implementation (Schultz et al., 2010). Toledo attempted to conduct a randomized controlled trial with randomization occurring at the family level. However, inadequate recruitment and retention for follow-up assessments made the evaluation impossible. Toledo enrolled 30 families, retaining only 23 percent of them for the six-month assessment. Due to low enrollment and retention, the evaluation of the Toledo program was discontinued near the end of the project. At baseline, caregivers reported that children had been exposed to an average of 4.7 types of violence during their lives. Forty-seven percent of enrolled families reported baseline child posttraumatic stress disorder (PTSD) symptoms that fell in the “significant” range, and 73 percent had levels of parental stress that fell in the “clinical” range. Toledo Safe Start families in the intervention group received CPP. Overall, 40 percent of the families in the intervention group received CPP, with an average of 15.8 sessions per family. The sample size limitations mean that no conclusions can be drawn about the impact of the Toledo intervention on child- and family-level outcomes. Overall, the Toledo model requires further testing with a sufficient sample to assess the potential impacts of the intervention on outcomes for children exposed to violence.
Neural Language Models Evaluate Human Performance: The Role of Language and Prosody in Predicting Job Interview Scores

In this work we test the use of state-of-the-art 001 neural language model representations to pre-002 dict behavioral traits that cannot be easily ex-003 tracted from the textual input alone. We take 004 the task of automated job interview scoring 005 and make predictions on behavioral traits such 006 as hirability , engagement , or friendliness . We 007 find that representing text using neural mod-008 els trained only on text already leads to better 009 overall prediction results compared to a fea-010 ture engineering approach that uses a combina-011 tion of linguistic and extra-linguistic materials. 012 Moreover, we show that combining word em-013 beddings and prosodic features improves the 014 results even further, highlighting the value of 015 adding information from modalities other than 016 text when evaluating human performance. 017
Ecotourism Development Strategy of Kupang Bay Marine Nature Tourism Park

: Planning for ecotourism in the Marine Nature Tourism Park (TWAL) of Kupang Bay, needs to begin with an assessment of tourist attractions and development strategies. This research is to obtain the value of natural tourism attractiveness and the choice/alterntives? of ecotourism development strategies. The research method used is a descriptive survey, with the technique of observation and assessment of economic, social, and environmental components referring to the standard for assessing of natural tourism objects and attractions (Ministry of Forestry 2002). Further data analysis regarding the choice of development strategy is SWOT. The results showed that the TWAL of Kupang Bay is highly potential to be developed as an ecotourism area. This opportunity is very high as it is supported by the status of the area as a park for tourism, and the government's commitment to developing regional ecotourism, as one of the excellent destinations on Timor Island, East Nusa Tenggara Province. Based on the analysis of the development strategy for the tourism object, TWAL Kupang Bay is in Quadrant III, with this a highly favorable situation where the TWAL Kupang Bay has very high opportunities and few weaknesses. An alternative strategy for the TWAL of Kupang Bay that is more innovative, productive and integrated needs to be done by implementing the development strategy based on existing (internal and external) conditions. In order to improve the performance of ecotourism development in the park, several things need to be noted by the parties, namely the choice of tourism development is adaptive collaborative management, and provides opportunities for stakeholders to participate in the planning, implementation and evaluation of ecotourism development activities.
Stiﬀness Mapping of Deformable Objects Through Supervised Embedding and Gaussian Process Regression

The stiﬀness map of a deformable object stores information about that object’s surface compliance. Thus, through a stiﬀness map, we gain insight into the physical properties of that object. Depending on the object, an understanding of stiﬀness has applications ranging from localizing tumors for surgery to grasping policies in manipulation. However, generating a stiﬀness map or stiﬀness mapping is challenging as even with information about the thickness of the underlying material, the object’s physical geometry, and the composition of the material itself, it can be enormously computationally complex to model an object’s surface compliance. Therefore it is often necessary to generate the stiﬀness map of an object by direct sampling through palpation or applying a force and measuring the subsequent displacement. Then by densely palpating over every point on an object’s surface, a time-consuming process task for a very ﬁne sampling, a stiﬀness map for an entire object can be generated. Alternatively, it is also possible to only palpate a subset of points from the surface. Then by applying regression, the sampled stiﬀness data can be used to estimate a function between points on a given surface (known as inputs or predictors) and the stiﬀness data. Finally, using this function, an object’s entire stiﬀness map can be extrapolated.
Tutorials for Children by Children: Design and Evaluation of a Children’s Tutorial Authoring Tool for Digital Art

Digital art tools allow children to express their creativity and can help them develop important skills. There are numerous software tutorials available to help teach and inspire digital art enthusiasts, however, most are authored for and by adults. Given that children are increasingly contributing online digital content, in this paper, we investigate a tutorial authoring design concept where children can capture their drawings and information on their process, with the long-term objective of allowing children to share both their creativity and their workflows with other children. Through participatory design sessions, prototyping, and an evaluation, we explore children’s attitudes towards the creation of digital art tutorials, focusing on their perceived incentives to author such tutorials and how they feel about the concept of sharing their tutorials with other children. We also elicit reactions towards specific design elements. Our findings suggest important considerations for tools designed to motivate and support ch ildren’s creation of digital art tutorials.
Hydrogen for climate neutrality: conditions for deployment in France and Europe

The Institute for Sustainable Development and International Relations (IDDRI) is an independent think tank that facilitates the transition towards sustainable development. It was founded in 2001. To achieve this, IDDRI identifies the conditions and proposes the tools for integrating sustainable development into policies. It takes action at different levels, from international cooperation to that of national and sub-national governments and private companies, with each level informing the other. As a research institute and a dialogue platform, IDDRI creates the conditions for a shared analysis and expertise between stakeholders. It connects them in a transparent, collaborative manner, based on leading interdisciplinary research. IDDRI then makes its analyses and proposals available to all. Four issues are central to the institute’s activities: climate, biodiversity and ecosystems, oceans, and sustainable development governance.
UvA-DARE (Digital Academic UvA@Home Team Description

This team description paper describes the approaches that will be taken by the UvA@Home team to compete in Standard Platform League with the Softbank Robotics Pepper. The research challenges concern person recognition, natural language processing and navigation. Modules implemented so far include people detection, speech recognition and natural language processing. The remaining challenges will be solved using the previous research and achievements of the UvA teams in the RoboCup.
RIT Using Machine Learning in Disaster Tweets Classification Using Machine Learning in Disaster Tweets Classification

People share real-time updates on social media platforms (i.e. Twitter) when disaster occurs, this information is very valuable for disaster relief and response teams as it can alert them immediately in order to prioritize tasks. Text mining and Machine learning algorithm can scan the huge generated unstructured data on social media platforms such as Twitter, to spot such information through keywords and phrases that refers to disasters. One challenge that the algorithm might face is whether a tweet text is talking about a real disaster or uses those keywords as a metaphor, which can lead to huge mislabeling of tweets. Hence, this research aims on using Natural Language Processing (NLP) and classification models to distinguish between real and fake disaster tweets. The dataset was acquired from Kaggle website, and it contain tweets that are related to real disasters, and other tweets that refers to fake disasters. Furthermore, using RStudio software, exploratory data analysis (EDA), feature selections, and data cleaning were performed prior to the data modeling, two different training to testing split were tested. In addition, four classifiers were built, which are SVM, KNN, Naïve Bayes, and XGBoost. As a result, the best accuracies achieved with 80/20 ratio split, and with using the whole dataset rather than sampling, SVM and XGBoost performed well with accuracies of 80% and 78% respectively, while KNN suffered overfitting (99% accuracy) and Naïve Bayes performed poorly (65%).
Still bearing the burden How poor rural women in Bangladesh are paying most for climate risks

The study assesses the percentage of climate expenditure as a share of household income and expenditure in climate-vulnerable regions of Bangladesh, based on primary data. In particular, it investigates disaster and climate adaptation expenditure by rural households; the socioeconomic factors influencing disaster and climate adaptation expenditure; income shares of disaster and climate adaptation expenditure; and gendered differences in this expenditure. This study updates a 2019 review by IIED which used secondary data, and finds similar estimates of total rural household level expenditure.
Long-term impact of tillage on biogeochemical phosphorus cycle : analysis of the test of L'Acadie (Quebec, Canada) and modelling

The no-till (NT) is gaining great attention for soil preparation. This practice modifies number of soil properties such as the distribution of phosphorus (P) in the soil profile. This work aims to analyze the impacts on the biogeochemical P cycle after decades of NT and incorporate those effects in an operational model. We used a long-term field experiment under corn-soybean rotation established on a clay loam soil (L’Acadie, Quebec, Canada). The design was a split-plot plan with 4 blocks under moldboard plough (MP) and NT, subdivided by 3 doses of P [0 (0P), 17.5 (0.5P), 35 (1P) kg P ha-1] applied in corn phase and localized to 5-cm deep and 5-cm from the corn row.The phosphate ion concentration in MP was relatively constant (0.08 mg P L-1) in the tilled layer (0-20 cm), slightly lower in 20-30 cm (0.05 mg P L-1) and much lower below (0.01 mg P L-1). In [NT, 0.5P] and [NT, 1P] plots, Cp was higher (0.28 et 0.19 mg P L-1) in the 0-10 cm layer compared to the tilled layer in MP, but decreased sharply with depth. This vertical stratification in NT was also observed for P-Olsen, P-M3 and other nutrients as C, N, and K. After 23- and 24-year of experimentation, maize roots tended to be fewer (-14%) under NT than MP, probably because of increased weed infestation under NT. For soybean, more roots accumulated in the 0-10 cm layer under NT (44% of total length) than MP (21%) and vice versa for the 10-20 cm layer. Those differences in root distribution under NT and MP corresponded to the stratification of N, P, and K.This set of data on the distribution of roots and phosphorus was used i) to develop a 1D model describing P dynamics over several decades in MP, ii) to test a method to assess the spatial P uptake distribution according local root length density and soil P availability, and iii) to develop a spatial 2D model describing P dynamic in NT. This model simulates the soil P availability dynamic on long term according soil properties and crop root distribution within soil profile for different soil preparation regime and different P fertilization rate. Although the model overestimates the P availability near the localized P fertilizer, it is able to predict the soil P stratification in NT treatment and its consequences on crop P uptake. This new model will be a useful tool to improve P fertilization management in context of no-till practices.
SNMP JManager: An Open Source Didactic Application for Teaching and Learning SNMP v1/2c/3 with Support for IPv4 and IPv6

In this paper we present SNMP JManager, a free open source application developed under the GNU General Public License in Java. Its main goal is to be used as a didactic application in network advanced courses at Universidad Central de Venezuela to support the teaching and learning of SNMP v1/2c/3. SNMP JManager is platform independent, easy to use, and has a user-friendly interface. Additionally, SNMP JManager can be a powerful application for network administrators and allows them to collect and modify the configuration of network devices through the use of SNMP v1/2c/3, import MIBs, manage traps, etc. It was developed to support both IPv4 and IPv6, which makes it one of the strongest free open source SNMP managers.
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations

Models that generate extractive rationales (i.e., subsets of features) or natural language explanations (NLEs) for their predictions are important for explainable AI. While an extractive rationale provides a quick view of the features most responsible for a prediction, an NLE allows for a comprehensive description of the decision-making process behind a prediction. However, current models that generate the best extractive rationales or NLEs often fall behind the state-of-the-art (SOTA) in terms of task performance. In this work, we bridge this gap by introducing RExC, a self-rationalizing framework that grounds its predictions and two complementary types of explanations (NLEs and extractive rationales) in background knowledge. Our framework improves over previous methods by: (i) reaching SOTA task performance while also providing explanations, (ii) providing two types of explanations, while existing models usually provide only one type, and (iii) beating by a large margin the previous SOTA in terms of quality of both types of explanations. Furthermore, a perturbation analysis in RExC shows a high degree of association between explanations and predictions, a necessary property of faithful explanations.
Coalitional model predictive control for systems of systems

An aspect so far rarely contemplated in distributed control problems is the explicit consideration of individual (local) interests of the components of a complex system. Indeed, the focus of the majority of the literature about distributed control has been the overall system performance. While on one hand this permitted to address fundamental properties of centralized control, such as system-wide optimality and stability, one the other hand it implied assuming unrestricted cooperation across local controllers. However, when dealing with multi-agent systems with a strong heterogeneous character, cooperation between the agents cannot be taken for granted (due to, for example, logistics, market competition), and selfish interests may not be neglected. Another critical point that must be kept into consideration is the diversity characterizing systems of systems (SoS), yielding very complex interactions between the agents involved (one example of such system is the smart grid). In order to tackle such inherent aspects of SoS, the research presented in this thesis has been concerned with the development of a novel framework, the coalitional control, that extends the scope of advanced control methods (in particular MPC) by drawing concepts from cooperative game theory that are suited for the inherent heterogeneity of SoS, providing as well an economical interpretation useful to explicitly take into account local selfish interests. Thus, coalitional control aims at governing the association/dissociation dynamics of the agents controlling the system, according to the expected benefits of their possible cooperation. From a control theoretical perspective, this framework is founded on the theory of switched systems and variable structure/topology networked systems, topics that are recently experiencing a renewed interest within the community. The main concepts and challenges in coalitional control, and the links with cooperative network game theory are presented in this document, tracing a path from model partitioning to the control schemes whose principles delineate the idea of coalitional control. This thesis focuses on two basic architectures: (i) a hierarchically supervised evolution of the coalitional structure, and (ii) a protocol for autonomous negotiation between the agents, with specific mechanisms for benefit redistribution, leading to the emergence of cooperating clusters.
First Do Not Fall: Learning to Exploit a Wall With a Damaged Humanoid Robot

Humanoid robots could replace humans in hazardous situations but most of such situations are equally dangerous for them, which means that they have a high chance of being damaged and falling. We hypothesize that humanoid robots would be mostly used in buildings, which makes them likely to be close to a wall. To avoid a fall, they can therefore lean on the closest wall, as a human would do, provided that they find in a few milliseconds where to put the hand(s). This letter introduces a method, called D-Reflex, that learns a neural network that chooses this contact position given the wall orientation, the wall distance, and the posture of the robot. This contact position is then used by a whole-body controller to reach a stable posture. We show that D-Reflex allows a simulated TALOS robot (1.75 m, 100 kg, 30 degrees of freedom) to avoid more than 75% of the avoidable falls and can work on the real robot.
Comparison of ventilation performance of three different air supply devices: a measurement study

ABSTRACT People today spend a significant part of their time in an indoor environment, whether it be home, school, vehicle or workplace. This has put greater demands on indoor environment, in terms of both air quality and thermal comfort. The main objective of building ventilation is to take care of pollutants and lower their concentration, but it is also used to cool or heat indoor air. The aim of this paper was to study the behavior of three different ventilation supply devices, i.e. mixing supply device, displacement supply device and confluent jet supply device, in an office room. Different cases have been studied experimentally with different airflow rates, supply air temperature and supply devices. The results shows that mostly that we can expect, but the results show a very small difference in ventilation efficiency between the different systems and in theory there should be a larger difference.
Towards biologically plausible Dreaming and Planning

Humans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performances. Recent model-based approaches show promising results by reducing the number of necessary interactions with the environment to learn a desirable policy. However, these methods require biological implausible ingredients, such as the detailed storage of older experiences, and long periods of offline learning. The optimal way to learn and exploit word-models is still an open question. Taking inspiration from biology, we suggest that dreaming might be an efficient expedient to use an inner model. We propose a two-module (agent and model) spiking neural network in which"dreaming"(living new experiences in a model-based simulated environment) significantly boosts learning. We also explore"planning", an online alternative to dreaming, that shows comparable performances. Importantly, our model does not require the detailed storage of experiences, and learns online the world-model and the policy. Moreover, we stress that our network is composed of spiking neurons, further increasing the biological plausibility and implementability in neuromorphic hardware.
A Peek at Peak Emotion Recognition

Despite much progress in the field of facial expression recognition, little attention has been paid to the recognition of peak emotion. Aviezer et al. [1] showed that humans have trouble discerning between positive and negative peak emotions. In this work we analyze how deep learning fares on this challenge. We find that (i) despite using very small datasets, features extracted from deep learning models can achieve results significantly better than humans. (ii) We find that deep learning models, even when trained only on datasets tagged by humans, still outperform humans in this task.
Causal Discovery and Injection for Feed-Forward Neural Networks

Neural networks have proven to be effective at solving a wide range of problems but it is often unclear whether they learn any meaningful causal relationship: this poses a problem for the robustness of neural network models and their use for high-stakes decisions. We propose a novel method overcoming this issue by injecting knowledge in the form of (possibly partial) causal graphs into feed-forward neural networks, so that the learnt model is guaranteed to conform to the graph, hence adhering to expert knowledge. This knowledge may be given up-front or during the learning process, to improve the model through human-AI collaboration. We apply our method to synthetic and real (tabular) data showing that it is robust against noise and can improve causal discovery and prediction performance in low data regimes.