Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration

To achieve scenario intelligence, humans must transfer knowledge to robots by developing goal-oriented algorithms, which are sometimes insensitive to dynamically changing environments. While deep reinforcement learning achieves significant success recently, the long and difficult training process limits its application. In this paper, we propose a hybrid structure named Option-Interruption in which human knowledge is embedded into a hierarchical reinforcement learning framework. Our architecture has two key components: options, represented by existing human-designed methods, can significantly speed up the training process and interruption mechanism, based on learnable termination functions, enables our system to timely terminate the current option according to the external environment. To implement this architecture, we derive a set of update rules based on policy gradient methods and present a complete training process. In the experiment part, our method is evaluated in two simulated tasks, Four-room navigation and exploration task, which shows the efficiency and flexibility of our framework.
Exploiting Justifications for Lazy Grounding of Answer Set Programs

Answer set programming (ASP) is an established knowledge representation formalism. Lazy grounding avoids the so-called grounding bottleneck of ASP by interleaving grounding and solving; this technique was recently extended to work with conflict-driven clause learning. Unfortunately, it often happens that such a lazy grounding ASP system, at the fixpoint of the evaluation, arrives at an assignment that contains literals that are true but unjustified. The system then is unable to determine the actual causes of the situation and falls back to chronological backtracking, potentially wasting an exponential amount of time. In this paper, we show how top-down query mechanisms can be used to analyze the situation, learn a new clause or nogood, and backjump further in the search tree. Contributions include a rephrasing of lazy grounding in terms of justifications and algorithms to construct relevant justifications without grounding. Initial experiments indicate that the newly developed techniques indeed allow for an exponential speed-up.
Modular Sensor Fusion for Semantic Segmentation

Sensor fusion is a fundamental process in robotic systems as it extends the perceptual range and increases robustness in real-world operations. Current multi-sensor deep learning based semantic segmentation approaches do not provide robustness to under-performing classes in one modality, or require a specific architecture with access to the full aligned multi-sensor training data. In this work, we analyze statistical fusion approaches for semantic segmentation that overcome these drawbacks while keeping a competitive performance. The studied approaches are modular by construction, allowing to have different training sets per modality and only a much smaller subset is needed to calibrate the statistical models. We evaluate a range of statistical fusion approaches and report their performance against state-of-the-art baselines on both realworld and simulated data. In our experiments, the approach improves performance in IoU over the best single modality segmentation results by up to 5%. We make all implementations and configurations publicly available.
A Cyber Insurance Approach to Manage Physical Layer Secrecy for Massive MIMO Cellular Networks

Due to the fading and broadcast nature of wireless medium, it is challenging to provide full wireless coverage and secure the transmitted signals from unintended users in cellular networks. As a result, cyber risks, such as service outage and secrecy outage, would inevitably occur and cause loss/damage to the users. To transfer the cyber risks and mitigate the impact of loss, cyber insurance appears to be a promising solution for the economics of wireless services. In this paper, we introduce a cyber insurance framework for wireless users to relieve loss from the cyber risks. In this framework, each user pays a premium to an insurer. If the user experiences an outage, he/she will claim the loss, and the insurer will pay the corresponding %claim or indemnity to the user. Under the network model of a large-scale massive multiple-input multiple- output (MIMO) cellular networks and cyber insurance, we first characterize the user performance in terms of both service outage probability and secrecy outage probability using stochastic geometry analysis. Based on these performance results, we quantify the ruin probability of the cyber insurer, which indicates the chance that the insurer does not have enough capital reserve to afford the claims from the outage users. Through numerical evaluation, we show that the ruin probability of the insurer can be efficiently reduced by equipping a larger number of antennas at base stations or increasing network frequency reuse.
A Secure Fair Exchange for SMS-Based Mobile Payment Protocols Based on Symmetric Encryption Algorithms with Formal Verification

Information security and fair exchange are essential to creating trust among all the parties participating in any sale transaction. However, implementing them in any mobile commerce is challenging due to the limitation of resources on mobile devices. Numerous m-commerce protocols that have been proposed so far still lack those two important aspects. In this paper, we propose mobile payment (m-payment) protocols, a crucial part of m-commerce, that incorporate both information security and fair exchange while retaining their own lightweight property. To allow convenience of use, the proposed protocols can be implemented on the existing Short Message Service (SMS) infrastructure. Our approach is based on the secure session key generation technique to enhance information security under lightweight conditions and involves a trusted third party to guarantee fair exchange without information disclosure. We have formally proven that our protocols are more effective and efficient than others in terms of fairness, security, and lightweight properties. In addition, the soundness and completeness of the protocols have been analyzed and proven using BAN logic and an automated security protocol proof tool named Scyther.
Adaptive Neural Fuzzy Inference System for Accurate Localization of Wireless Sensor Network in Outdoor and Indoor Cycling Applications

When localizing wireless sensor networks, estimating the distances of sensor nodes according to the known locations of the anchor nodes remains a challenge. As nodes may transfer from one place to another, a localization technique that can measure or determine the location of a mobile node is necessary. In this paper, the distance between a bicycle when moves on the cycling track and a coordinator node (i.e., coach), which positioned on the middle of the cycling field was estimated for the indoor and outdoor velodromes. The distance was determined based on two methods. First, the raw estimate is done by using the log-normal shadowing model (LNSM) and later, the intelligence technique, based on adaptive neural fuzzy inference system (ANFIS) is applied to improve the distance estimation accuracy, especially in an indoor environment, which the signal is severely dominated by the effect of wireless multipath impairments. The received signal strength indicator from anchor nodes based on ZigBee wireless protocol are employed as inputs to the ANFIS and LNSM. In addition, the parameters of the propagation channel, such as standard deviation and path loss exponent were measured. The results shown that the distance estimation accuracy was improved by 84% and 99% for indoor and outdoor velodromes, respectively, after applying the ANFIS optimization, relative to the rough estimate by the LNSM method. Moreover, the proposed ANFIS technique outperforms the previous studies in terms of errors of estimated distance with minimal mean absolute error of 0.023 m (outdoor velodrome) and 0.283 m (indoor velodrome).
Loss Decomposition for Fast Learning in Large Output Spaces

For problems with large output spaces, evaluation of the loss function and its gradient are expensive, typically taking linear time in the size of the output space. Recently, methods have been developed to speed up learning via efficient data structures for Nearest-Neighbor Search (NNS) or Maximum Inner-Product Search (MIPS). However, the performance of such data structures typically degrades in high dimensions. In this work, we propose a novel technique to reduce the intractable high dimensional search problem to several much more tractable lower dimensional ones via dual decomposition of the loss function. At the same time, we demonstrate guaranteed convergence to the original loss via a greedy message passing procedure. In our experiments on multiclass and multilabel classification with hundreds of thousands of classes, as well as training skip-gram word embeddings with a vocabulary size of half a million, our technique consistently improves the accuracy of search-based gradient approximation methods and outperforms sampling-based gradient approximation methods by a large margin.
STMC: Semantic Tag Medical Concept Using Word2Vec Representation

In this paper we propose a recognition system of medical concepts from free text clinical reports. Our approach tries to recognize also concepts which are named with local terminology, with medical writing scripts, short words, abbreviations and even spelling mistakes. We consider a clinical terminology ontology (Snomed-CT), as a dictionary of concepts. In a first step we obtain an embedding model using word2vec methodology from a big corpus database of clinical reports. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space, and so the geometrical similarity can be considered a measure of semantic relation. We have considered 615513 emergency clinical reports from the Hospital "Rafael Mendez" in Lorca, Murcia. In these reports there are a lot of local language of the emergency domain, medical writing scripts, short words, abbreviations and even spelling mistakes. With the model obtained we represent the words and sentences as vectors, and by applying cosine similarity we identify which concepts of the ontology are named in the text. Finally, we represent the clinical reports (EHR) like a bag of concepts, and use this representation to search similar documents. The paper illustrates 1) how we build the word2vec model from the free text clinical reports, 2) How we extend the embedding from words to sentences, and 3) how we use the cosine similarity to identify concepts. The experimentation, and expert human validation, shows that: a) the concepts named in the text with the ontology terminology are well recognized, and b) others concepts that are not named with the ontology terminology are also recognized, obtaining a high precision and recall measures.
Cloud-Based Charging Management of Electric Vehicles in a Network of Charging Stations

A large scale of electric vehicles (EVs) and the operation of smart grid requires the support of a reliable and robust communication infrastructure. Cloud computing has gained popularity in smart grid for reducing computational and communication complexity. Based on cloud computing services, this paper considers the issues of high charging demand in fast charging stations (FCSs) during peak hours and communication among a large-scale of EVs, a network of FCSs, and system operator (SO). More specifically, we propose a novel cloud-based hierarchical charging management model of EVs, whereby two levels of cloud computing infrastructures are considered to meet different latency requirements of customers in highway exits and parking lots. Considering the quality of service (QoS) metrics (average waiting time in the queue, and blocking probability), the model is composed of: server planning in the cloud, capacity planning in FCSs, and profit maximization. Meanwhile, a price incentive mechanism is applied to shift the heavy load from peak hours to off-peak hours. Numerical results demonstrate the effectiveness of the proposed method, which can guarantee QoS and system profit, thereby more customers can satisfy their charging demand.
The Improvement of Memory Deficits after Whole-Body Cryotherapy - The First Report.

BACKGROUND Mild Cognitive Impairments (MCI) and dementia are still incurable. The Whole-Body Cryotherapy (WBC) - short, cyclic exposure to extremely low temperatures - has proven anti-inflammatory and anti-oxidative effects. It can also induce hormonal, lipid and neural changes. OBJECTIVE To evaluate the WBC effect on cognitive functioning and blood parameters of MCI patients. MATERIALS AND METHODS Participants with MCI undertook 10 WBC sessions. Cognitive functions and depressive symptoms were assessed before the first session, after the last session and 2 weeks later. Whole blood samples were collected. RESULTS The cognitive functioning improved after 10 WBC sessions (p<0.05), especially memory processes. WBC caused a significant (p<0.05) increase of NO plasma level, BDNF concentration (p<0.05) and reduction of IL-6 (p<0.05). The moderate relationship between NO and cognitive functioning was noticed after WBC. CONCLUSION The preliminary results of the first study evaluating WBC on memory deficits suggest that WBC may be useful as a supportive therapy of MCI.
Energy Efficiency Maximization of Dynamic CoMP-JT Algorithm in Dense Small Cell Networks

We firstly formulate the energy efficiency (EE) maximization problem of joint user association and power allocation considering minimum data rate requirement of small cell users (SUEs) and maximum transmit power constraint of small cell base stations (SBSs), which is NP-hard. Then, we propose a dynamic coordinated multipoint joint transmission (CoMP-JT) algorithm to improve EE. In the first phase, SUEs are associated with the SBSs close to them to reduce the loss of power by the proposed user association algorithm, where the associated SBSs of each small cell user (SUE) form a dynamic CoMP-JT set. In the second phase, through the methods of fractional programming and successive convex approximation, we transform the EE maximization subproblem of power allocation for SBSs into a convex problem that can be solved by proposed power allocation optimization algorithm. Moreover, we show that the proposed solution has a much lower computational complexity than that of the optimal solution obtained by exhaustive search. Simulation results demonstrate that the proposed solution has a better performance.
Network Association for Cognitive Communication and Radar Co-Systems: A POMDP Formulation

In order to beneficially exploit wireless spectral resources, spectrum sharing between communication systems and radar systems has become a popular research topic. However, traditional network association strategies may not result in an efficient co-system. We circumvent this problem by formulating a partially observable Markov decision process (POMDP) aided network association scheme. For maximizing the network throughput, whilst minimizing the interference imposed on the radar user, communication users are capable of adaptively selecting underlay or overlay access mode. Moreover, a near-optimal reinforcement learning algorithm is proposed considering both the computational complexity and feasibility. Finally, simulations are conducted in order to evaluate the effectiveness of our proposed POMDP based network association scheme.
Combining Quality Estimation and Automatic Post-editing to Enhance Machine Translation output

We investigate different strategies for combining quality estimation (QE) and automatic post-editing (APE) to improve the output of machine translation (MT) systems. The joint contribution of the two technologies is analyzed in different settings, in which QE serves as either: i) an activator of APE corrections, or ii) a guidance to APE corrections, or iii) a selector of the ﬁnal output to be returned to the user. In the ﬁrst case (QE as activator), sentence-level predictions on the raw MT output quality are used to trigger its automatic correction when the estimated (TER) scores are below a certain threshold. In the second case (QE as guidance), word-level binary quality predictions (“good”/“bad”) are used to inform APE about problematic words in the MT output that should be corrected. In the last case (QE as selector), both sentence-and word-level quality predictions are used to identify the most accurate translation between the original MT output and its post-edited version. For the sake of comparison, the underlying APE technologies explored in our evaluation are both phrase-based and neural. Experiments are carried out on the English-German data used for the QE/APE shared tasks organized within the First Conference on Machine Translation (WMT 2016). Our evaluation shows positive but mixed results, with higher performance observed when word-level QE is used as a selector for neural APE applied to the output of a phrase-based MT system. Overall, our ﬁndings motivate further investigation on QE technologies. By reducing the gap between the performance of current solutions and “oracle” results, QE could signiﬁcantly add to competitive
Parallel Bayesian Network Structure Learning

Recent advances in Bayesian Network (BN) structure learning have focused on local-to-global learning, where the graph structure is learned via one local subgraph at a time. As a natural progression, we investigate parallel learning of BN structures via multiple learning agents simultaneously, where each agent learns one local subgraph at a time. We find that parallel learning can reduce the number of subgraphs requiring structure learning by storing previously queried results and communicating (even partial) results among agents. More specifically, by using novel rules on query subset and superset inference, many subgraph structures can be inferred without learning. We provide a sound and complete parallel structure learning (PSL) algorithm, and demonstrate its improved efficiency over state-of-the-art single-thread learning algorithms.
Asymptotic Approximation of Optimal Portfolio for Small Time Horizons

We consider the problem of portfolio optimization in a simple incomplete market and under a general utility function. By working with the associated Hamilton-Jacobi-Bellman partial differential equation (HJB PDE), we obtain a closed-form formula for a trading strategy which approximates the optimal trading strategy when the time horizon is small. This strategy is generated by a first order approximation to the value function. The approximate value function is obtained by constructing classical sub- and super-solutions to the HJB PDE using a formal expansion in powers of horizon time. Martingale inequalities are used to sandwich the true value function between the constructed sub- and super-solutions. A rigorous proof of the accuracy of the approximation formulas is given. We end with a heuristic scheme for extending our small-time approximating formulas to approximating formulas in a finite time horizon.
Warming Up the Language Engines: Short-Term Second Language Use Increases Subsequent Fluency

Subjective experience suggests that using one’s second language (L2) becomes easier after an initial period of adjustment. We report the results of an experiment suggesting that even a brief period of L2 use can facilitate subsequent retrieval. Native Hebrew speakers completed letter and category verbal fluency tasks in English. In a subsequent experimental session, participants performed a short comprehension or production task, either in Hebrew (L1) or in English (L2), and then completed an additional round of the English fluency tasks. English use resulted in a reliable improvement in letter fluency, associated with executive functioning. However, there was no reliable improvement in category fluency, associated with lexical knowledge. No reliable improvement in the fluency tasks was observed following Hebrew use. Results suggest that even a brief period of L2 use can facilitate retrieval, by increasing the relative activation of L2. Furthermore, improvement did not result from priming specific lexical items, suggesting increased relative activation affects the L2 lexicon as a whole.
SeMIBIoT: Secure Multi-Protocol Integration Bridge for the IoT

The Internet of Things (IoT) is gradually becoming a reality, supported by an assortment of heterogeneous devices, varying from resource- starved wireless sensors to embedded devices and resource-rich backend systems, which are supplemented by a range of networking technologies and protocols. Nevertheless, this diverse ecosystem of platforms and protocols, along with the inherent limitations in processing power, energy, memory and communications bandwidth for some of the involved devices, render secure and interoperable interactions a primary concern, and an important obstacle to the introduction of novel applications, and the adoption of IoT in general. Motivated by the above, this work presents SeMIBIoT, a Secure Multi-protocol Integration Bridge for the IoT. Acting as a gateway, SeMIBIoT is able to provide hop-by-hop or end-to-end secure communications between an array of heterogeneous nodes and standardized IoT protocols, guaranteeing seamless interactions and thus alleviating security and interoperability concerns. Using a realistic testbed featuring a number of diverse platforms, the bridge is evaluated in a variety of scenarios, validating the feasibility of the proposed approach.
ArchAIDE - Archaeological Automatic Interpretation and Documentation of cEramics

The goals of H2020 project "ArchAIDE: are to support the classification and interpretation work of archaeologists with innovative computer-based tools, able to provide the user with features for the semi-automatic description and matching of potsherds over the huge existing ceramic catalogues. Pottery classification is of fundamental importance for the comprehension and dating of the archaeological contexts, and for understanding production, trade flows and social interactions, but it requires complex skills and it is a very time consuming activity, both for researchers and professionals. The aim of ArchAIDE is to support the work of archaeologists, in order to meet real user needs and generate economic benefits, reducing time and costs. This would create societal benefits from cultural heritage, improving access, re-use and exploitation of the digital cultural heritage in a sustainable way. These objectives will be achieved through the development of: • an as-automatic-as-possible procedure to transform the paper catalogues in a digital description, to be used as a data pool for search and retrieval process; • a tool (mainly designed for mobile devices) that will support archaeologists in recognizing and classifying potsherds during excavation and post-excavation analysis, through an easy-to-use interface and efficient algorithms for characterisation, search and retrieval of the visual/geometrical correspondences; • an automatic procedure to derive a complete potsherds identity card by transforming the data collected into a formatted electronic document, printable or visual; • a web-based real-time data visualisation to improve access to archaeological heritage and generate new understanding; • an open archive to allow the archival and re-use of archaeological data, transforming them into common heritage and permitting economic sustainability. Those tools will be tested and assessed on real-cases scenarios, paving the way to future exploitation.