Miracle-dz/UniFAR-FTUs huggingface dataset:
MPTCP Meets FEC: Supporting Latency-Sensitive Applications Over Heterogeneous Networks

Over the past years, TCP has gone through numerous updates to provide performance enhancement under diverse network conditions. However, with respect to losses, little can be achieved with legacy TCP detection and recovery mechanisms. Both fast retransmission and retransmission timeout take at least one extra round trip time to perform, and this might significantly impact the performance of latency-sensitive applications, especially in lossy or high delay networks. While forward error correction (FEC) is not a new initiative in this direction, the majority of the approaches consider FEC inside the application. In this paper, we design and implement a framework, where FEC is integrated within TCP. Our main goal with this design choice is to enable latency sensitive applications over TCP in high delay and lossy networks, but remaining application agnostic. We further incorporate this design into multipath TCP (MPTCP), where we focus particularly on heterogeneous settings, considering the fact that TCP recovery mechanisms further escalate head-of-line blocking in multipath. We evaluate the performance of the proposed framework and show that such a framework can bring significant benefits compared with legacy TCP and MPTCP for latency-sensitive real application traffic, such as video streaming and web services.
Towards Designing Multi-Agent Coverage Systems Capable of Anticipation and Tight Coordination with Detailed Environmental and Perception Models

This paper introduces a new approach for modelling coverage problems, capable of taking into consideration 1) evolving points of interest, 2) environmental dynamics, including the influence of agent actions, and 3) detailed perception models. Such coverage problems requires tight coordination between agents while anticipating the consequences of their actions for maximizing the covered areas over time while avoiding adverse situations (e.g. collisions).
A Probabilistic Privacy Preserving Strategy for Word-of-Mouth Social Networks

An online social network (OSN) is a platform that makes people communicate with friends, share messages, accelerate business, and enhance teamwork. In the OSN, privacy issues are increasingly concerned, especially in private message leaks in word-of-mouth. A user’s privacy may be leaked out by acquaintances without user’s consent. In this paper, an integrated system is designed to prevent this illegal privacy leak. In particular, we only use the method of space vector model to determine whether the user’s private message is really leaked. Canary traps techniques are used to detect leakers. Then, we define a trust degree mechanism to evaluate trustworthiness of a communicator dynamically. Finally, we set up a new message publishing system to determine who can obtain the message of publisher. Secrecy performance analysis is provided to verify the effectiveness of the proposed message publishing system. Accordingly, a user in social networks can check whether other users are trustworthy before sending their private messages.
Testing Sparsity over Known and Unknown Bases

Sparsity is a basic property of real vectors that is exploited in a wide variety of applications. In this work, we describe property testing algorithms for sparsity that observe a low-dimensional projection of the input. We consider two settings. In the first setting, for a given design matrix A in R^{d x m}, we test whether an input vector y in R^d equals Ax for some k-sparse unit vector x. Our algorithm projects the input onto O(k \eps^{-2} log m) dimensions, accepts if the property holds, rejects if ||y - Ax|| > \eps for any O(k/\eps^2)-sparse vector x, and runs in time polynomial in m. Our algorithm is based on the approximate Caratheodory's theorem. Previously known algorithms that solve the problem for arbitrary A with qualitatively similar guarantees run in exponential time. In the second setting, the design matrix A is unknown. Given input vectors y_1, y_2,. ,y_p in R^d whose concatenation as columns forms Y in R^{d x p} , the goal is to decide whether Y=AX for matrices A in R^{d x m} and X in R^{m x p} such that each column of X is k-sparse, or whether Y is "far" from having such a decomposition. We give such a testing algorithm which projects the input vectors to O(log p/\eps^2) dimensions and assumes that the unknown A satisfies k-restricted isometry. Our analysis gives a new robust characterization of gaussian width in terms of sparsity.
Ontology-Grounded Topic Modeling for Climate Science Research

In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize and exploit the research is invaluable. Topic modeling is an effective technique for summarizing a collection of documents to find the main themes among them and to classify other documents that have a similar mixture of co-occurring words. We show how grounding a topic model with an ontology, extracted from a glossary of important domain phrases, improves the topics generated and makes them easier to understand. We apply and evaluate this method to the climate science domain. The result improves the topics generated and supports faster research understanding, discovery of social networks among researchers, and automatic ontology generation.
Development and Validation of New Reverberation Chamber for Wireless Devices

This paper proposes a reverberation chamber structure consisting of new reflectors and mode stirrers for electromagnetic compatibility and wireless terminal measurements. The key design considerations for them are determined through a reasonable approach to analyze the eigenmode for a reverberation chamber and the standard deviation of its working volume based on 3D simulation. The final designs are expected to improve the standard deviation performance of the initial structure of the reverberation chamber and provide a better mode stirring environment. The results measured in the fabricated chamber demonstrate that these predictions are clearly realized. The results satisfy the main requirements of this paper, which are defined in consideration of the specifications of commercial reverberation chamber products. Therefore, the reverberation chamber of this paper is expected to be useful for performance measurement and evaluation of commercial wireless terminals. To verify this logical approach to obtain a good design and its results, the results measured in the actual fabricated reverberation chamber are described along with analytical and computational results.
Position Paper: Using Early Goal Test in A

This paper focuses on the stopping condition of A*. Traditionally, A* is described such that the goal test is done oncea node is chosen for expansion (A*-LATE). An alternativeway is to perform the goal test when a node is generated (A*-EARLY). In this position paper we compare the two approaches from pedagogical and practical aspects and advocatefor teaching and using A*-EARLY.
State Space Gaussian Processes with Non-Gaussian Likelihood

We provide a comprehensive overview and tooling for GP modeling with non-Gaussian likelihoods using state space methods. The state space formulation allows for solving one-dimensional GP models in $\mathcal{O}(n)$ time and memory complexity. While existing literature has focused on the connection between GP regression and state space methods, the computational primitives allowing for inference using general likelihoods in combination with the Laplace approximation (LA), variational Bayes (VB), and assumed density filtering (ADF, a.k.a. single-sweep expectation propagation, EP) schemes has been largely overlooked. We present means of combining the efficient $\mathcal{O}(n)$ state space methodology with existing inference methods. We extend existing methods, and provide unifying code implementing all approaches.
Two-Levels Verification for Secure Data Aggregation in Resource-Constrained Environments

Many resource-constrained applications make use of data aggregation in order to prolong the network lifetime. However, the resource-constrained devices are usually deployed in unattended environments in which providing security is of paramount importance. This leads to various attacks that can occur during data aggregation process. Internal attacks such as selective forwarding represent the most dangerous ones since they cannot be detected by existing cryptography- based protocols proposed to secure data aggregation. In this work, we propose a two-levels verification, in which data is verified using cryptography and intrusion detection techniques. Indeed, a lightweight homomorphic encryption is combined with a game-theory based technique to efficiently secure data aggregation. Our analysis and results show the applicability of the system for aggregation-based resource-constrained applications, especially those considering sensitive information (e.g. health monitoring, military) where the time-efficient detection is crucial.
Handover Optimization via Asynchronous Multi-User Deep Reinforcement Learning

In this paper, an asynchronous multi-user deep reinforcement learning scheme is developed to control the handover (HO) processes across multiple user equipments (UEs), in the goal of lowering the HO rate while ensuring certain system throughput. In this scheme, we use a deep neural network (DNN) as an HO controller learned by each UE via reinforcement learning in a collaborative fashion. Moreover, we use supervised learning in initializing the DNN controller before the execution of reinforcement learning to exploit what we already know with traditional HO schemes and to mitigate the negative effects of random exploration at the initial stage. Furthermore, we show that the adopted global-parameter-based framework enables us to train faster with more UEs, which could nicely address the scalability issue to support large systems. Finally, simulation results demonstrate that the proposed framework can achieve better performance than the state-of-art on-line schemes, in terms of HO rates.
MoLVERIC cards feasibility study: a technique for inspection of moLIC diagrams

During Human Computer Interaction design, the MoLIC (Modeling Language for the Conversation Interaction) interaction diagrams can be used as a basis for building other artifacts, such as mockups. However, if the MoLIC diagrams have consistency or completeness defects, such defects can be propagated to the artifacts that are built based on these MoLIC diagrams. For this reason, we should carry out an inspection in MoLIC diagrams, to prevent the propagation of defects. This paper presents a feasibility study conducted with the MoLVERIC Cards technique, a technique that helps the inspection of MoLIC diagrams. The results show the participants' perception regarding MoLVERIC Cards technique and discuss opportunities for improvement.
CTLinQ: Content-Centric Link Scheduling in Cache-Enabled Device-to-Device Wireless Networks

In this paper, we consider cache-enabled device-to- device (D2D) wireless networks and propose a content-centric link scheduling method, called CTLinQ, to maximize the number of overall D2D links that can be simultaneously activated. Then, interference among different D2D links can be reduced and the quality-of-service (QoS) of each activated link can be guaranteed. Simulation results show that the proposed method outperforms the existing ones in terms of power consumption, the number of activated links, and overall system throughput in particular in high signal-to- interference-plus-noise ratio (SINR) region.
Review on Big Data Fusion Methods of Quality Inspection for Consumer Goods

Quality big data comes from a wide range, the problem is how to eliminate the structure barriers between various types of data from different sources, to achieve the effective integration for isolated or fragmented data, and then to mine the information, knowledge and wisdom according to the actual needs. It is urgent for the quality inspection departments to solve the problem of making a decision the first time and take preventive measures. This paper introduced the research status of named entity recognition, solid unified detection, data conflict resolution, data fusion method from the characteristics of quality inspection data. The existence problems are analyzed and the research direction is looking forward of the research on data fusion in this paper.
An implementation of the Social Distances Model using multi-GPU systems

We propose a new approach for using GPUs in large scale simulations of pedestrian evacuation. The Social Distances Model is designed for efficient modeling of pedestrian dynamics. This cellular automata based model, when implemented on the most modern GPUs, can simulate up to 106–108 entities. However, a valuable simulation of pedestrian evacuation must include various factors that govern pedestrian movement, for example, information provided by event organizers and navigation or allocation of other pedestrians. The most common method for introducing such information into simulations is the application of different floor fields. The floor fields provide “local knowledge” that affects pedestrians by modifying the transition functions of an applied cellular automaton. The main disadvantage of this method is its time consuming updating process. We propose a GPU based calculation of static and dynamic floor fields, whereby simulations that use several different floor fields can be efficiently calculated. A single GPU is able to cope with the Social Distance Model calculations, while other GPUs update dynamic floor fields constantly or when required. We also present the classic approach to performing cellular automata based simulations on systems with multiple processing units. The lattice is simply partitioned between the available GPUs. We compare these two approaches in terms of performance and functionality.
Prevalence, Genetic Heterogeneity, and Antibiotic Resistance Profile of Listeria spp. and Listeria monocytogenes at Farm Level: A Highlight of ERIC- and BOX-PCR to Reveal Genetic Diversity

This study aimed to identify Listeria spp. and L. monocytogenes, characterize the isolates, and determine the antibiotic resistance profiles of the isolates Listeria spp. and L. monocytogenes in fresh produce, fertilizer, and environmental samples from vegetable farms (organic and conventional farms). A total of 386 samples (vegetables, soil, water, and fertilizer with manure) were examined. The identification of bacterial isolates was performed using PCR and characterized using ERIC-PCR and BOX-PCR. The discriminating power of the typing method was analyzed using Simpson's Index of Diversity. Thirty-four (n=34) Listeria isolates were subjected to antimicrobial susceptibility test using the disc-diffusion technique. The PCR analysis revealed that Listeria spp. were present in 7.51% (29/386) of all the samples (vegetable, soil, fertilizer, and water). None of the samples examined were positive for the presence of L. monocytogenes. Percentages of 100% (15/15) and 73.30% (11/15) of the Listeria spp. isolated from vegetables, fertilizer, and soil from organic farm B had indistinguishable DNA fingerprints by using ERIC-PCR and BOX-PCR, respectively. Listeria spp. isolated from 86 samples of vegetable, fertilizer, and environment of organic farm A and conventional farm C had distinct DNA fingerprints. Simpson's Index of Diversity, D, of ERIC-PCR and BOX-PCR is 0.604 and 0.888, respectively. Antibiotic susceptibility test revealed that most of the Listeria spp. in this study were found to be resistant to ampicillin, rifampin, penicillin G, tetracycline, clindamycin, cephalothin, and ceftriaxone. The isolates had MAR index ranging between 0.31 and 0.85. In conclusion, hygienic measures at farm level are crucial to the reduction of Listeria transmission along the food chain.
A Non-Stationary Online Learning Approach to Mobility Management

Efficient mobility management is an important problem in modern wireless networks with heterogeneous cell sizes and increased nodes densities. We show that optimization- based mobility protocols cannot achieve long-term optimal performance, particularly in a time-varying environment for ultra-dense networks. To address the complex system dynamics, especially the possible change of statistics due to user movement and environment changes, we propose piece-wise stationary online-learning algorithms to track the activities of small base stations and solve frequent handover (FHO) problems. The BASD/BASSW algorithms are proved to achieve sublinear regret performance in finite time horizon and a linear, non-trivial rigorous bound for infinite time horizon. We study the robustness of the BASD/BASSW algorithms under missing feedback. Simulations show that proposed algorithms can outperform 3GPP protocols with the best threshold, and tend to be more robust than 3GPP to various dynamics which are common in practical ultra- dense wireless networks.
Trade-offs in the design of multimodal interaction for older adults

ABSTRACT This paper presents key aspects that designers and Human–Computer Interaction practitioners might encounter when designing multimodal interaction for older adults, focusing on the trade-offs that might occur as part of the design process. The paper gathers literature on multimodal interaction and assistive technology, and describes a set of design challenges specific for older users. Building on these main design challenges, four trade-offs in the design of multimodal technology for this target group are presented and discussed. To highlight the relevance of the trade-offs in the design process of multimodal technology for older adults, two of the four reported trade-offs are illustrated with two user studies that investigate mid-air and speech-based interaction with a tablet device. The first study explores the design trade-offs related to redundant multimodal commands in older, middle-aged and younger adults, whereas the second one investigates the design choices related to the definition of a set of mid-air one-hand gestures and voice input commands for older adults. Further reflections highlight the design trade-offs that such considerations bring in the process, providing an overview of the design choices involved and of their potential consequences.
Enhance RSS-Based Indoor Localization Accuracy by Leveraging Environmental Physical Features

Indoor localization technologies based on Radio Signal Strength (RSS) attract many researchers’ attentions, since RSS can be easily obtained by wireless devices without additional hardware. However, such technologies are apt to be affected by indoor environments and multipath phenomenon. Thus, the accuracy is very difficult to improve. In this paper, we put forward a method, which is able to leverage various other resources in localization. Besides the traditional RSS information, the environmental physical features, e.g., the light, temperature, and humidity information, are all utilized for localization. After building a comprehensive fingerprint map for the above information, we propose an algorithm to localize the target based on Naïve Bayesian. Experimental results show that the successful positioning accuracy can dramatically outperform traditional pure RSS-based indoor localization method by about 39%. Our method has the potential to improve all the radio frequency (RF) based localization approaches.
Segmentação de pessoas em imagens estáticas baseada em esqueleto

This dissertation, entitled “The formation of the self in Mead and Habermas: challenges and implications for education”, is the result of a doctorate research study on education and aims to address the issue of self-formation, as a process of subjectivity and stabilization of a personal identity, within the context of the post-metaphysical thinking, based on George Herbert Mead’s symbolic interactionism and on Jürgen Habermas’s theory of communicative action. This research also discusses the role of the school in the formation process of subjects, particularly with respect to the challenges and implications which result from both theoretical positions. It is a theoretical study, in the area of philosophy of education, which includes literature review, a presentation of the main concepts involved in this issue, and a hermeneutic endeavor to locate, understand and argue logically based on issues and concepts presented. Throughout the discussion it is possible to notice that the individuation process occurs through socialization. The mechanism of communication is the structuring principle of the mind, of the self, of the identity of the self and of the human society. Personal identity is structured by the internalization of collective norms and conventions, the progressive development of the internal structures of the self, the continuous processes of individual social learning, processes of decentralization and acquisition of rationality within communicative and symbolic contexts. The stabilization of a post conventional identity, the last level of the ideal development of personality, can be understood considering an increasingly universal action and the structuring of a meaning and a biography in one’s own existence. Such kind of identity offers the possibility of a self-understanding, which allows the subject to act with autonomy and responsibility in relation to themselves, to society and to culture. The school as an institution, through systematic and intentional teaching and learning processes, can effectively contribute to the formation of the self and the building of a personal identity in higher levels, thus qualifying the subjects to appropriate and reconstruct the inherited cultural knowledge, to establish judgments concerning world issues, to coordinate their actions together with other members of the social group and to participate in the interaction and socialization processes of the individuals. This contribution will be of a higher quality as the pedagogical actions, taken within the school environment, are outlined based on assumptions
Distortion-aware CNNs for Spherical Images

Convolutional neural networks are widely used in computer vision applications. Although they have achieved great success, these networks can not be applied to 360 spherical images directly due to varying distortion effect. In this paper, we present distortion-aware convolutional network for spherical images. For each pixel, our network samples a non-regular grid based on its distortion level, and convolves the sampled grid using square kernels shared by all pixels. The network successively approximates large image patches from different tangent planes of viewing sphere with small local sampling grids, thus improves the computational efficiency. Our method also deals with the boundary problem, which is an inherent issue for spherical images. To evaluate our method, we apply our network in spherical image classification problems based on transformed MNIST and CIFAR-10 datasets. Compared with the baseline method, our method can get much better performance. We also analyze the variants of our network.
Transfer Learning for Traffic Speed Prediction: A Preliminary Study

Trafﬁc speed prediction can beneﬁt a wide range of IoT applications in intelligent transportation and smart city. Recent supervised machine learning approaches heavily leverage vast amount of historical time-series data. Consequently, they degrade dramatically in the areas where collecting a large trafﬁc data is not quite feasible. With the aim of predicting the traf-ﬁc speed of such urban areas, we propose a transfer learning framework that exploits historical data of some other data-abundant areas by utilizing various spatio-temporal semantic features. Experimental results show that classic regression models and our proposed kernel regression model can achieve competitive performance comparing to baseline methods that heavily rely on the historical data of target areas.