Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Simple Two-Robot Precise Self-Localization Method

This paper presents a novel two-robot collaboration method for precise 2D self-localization using relatively simple sensors. The main advantage of this method lies in its ability to precisely measure the orientations of the robots, therefore reducing cumulative errors. Each robot is fitted with a rotating turret carrying a camera to track the moving robot and calculate the relative distance and position, and an encoder to provide the orientation of the turret. At each step, a single robot advances while the other remains stationary and measures the position of the moving robot (continuously or at the end of the step), using the angular orientation of the turret and the distance measured using the camera. The orientation of the moving robot is obtained by turning its own turret towards the static robot and measuring its turret orientation. By fusing the data from the two robots, the precise location and orientation of the moving robot are obtained. We also present an analytical model of the position of the robots as a function of the sensor data and then proceed to present a statistical estimate using Monte Carlo simulations of the location of the robots while assuming that the sensor data includes random errors. Additionally, lab experiments are presented and compared to simulation results.
Implicit User Trust Modeling Based on User Attributes and Behavior in Online Social Networks

In this paper, we present a new user trustworthiness estimation model for social networks (SN), whereas most of existing researches have been focused on the user-user/item relationship trustworthiness estimation. Users share information of their interest on various social media without their trustworthiness verification. Therefore, SN are susceptible to malicious users for misinformation spreading. In SN, the original information source is generally unknown and the user who is sharing the contents is the only known information about the source. Therefore, the user’s trustworthiness is an effective criterion for SN content’s trustworthiness estimation. However, the users are unable to identify trustworthy/untrustworthy users, and the existing user-user/item relationship models do not provide user trustworthiness information. Our proposed model provides a systematic way to assess the user trustworthiness based on user attributes and interaction behavior. The proposed model is helpful to avoid the trust sparsity (implicit trust model), trust subjectivity (user’s objective/collective trustworthiness estimation model) and cold-start user’s trustworthiness (user’s attributes-based trust modeling) problems. We employ friends-recommendation (FR) as an exemplary application to evaluate the performance of our proposed model in trust-aware recommendations. Simulation results illustrate that our trust-aware FR model outperformed the existing trust and FR models.
GENERAL PRACTITIONERS AND PSYCHOSOCIAL PROBLEMS AN EVALUATION USING PSEUDOPATIENTS

A study is presented using the method of pseudopatient observations in the context of general practice. Persons playing the role of a patient suffering from a depression of psychosocial origin presented themselves to a random sample of 25 general practitioners such that each of the general practitioners saw one male and one female pseudopatient for an initial visit. The most common form of treatment was by prescription (78% of all consultations), of which 90% contained at least one psychotropic agent. Other treatments were reassurance and discussion (42%), counselling (28%), psychiatric referrals (12%) and social agency referrals (4%). No mention of possible side effects was given in 57% of the occasions when a psychotropic drug was given. The method used is discussed and recommendations are made for its further use on a more regular basis as a monitoring device for the quality of primary care.
CWI and TU Delft Notebook TREC 2013: Contextual Suggestion, Federated Web Search, KBA, and Web Tracks

This paper provides an overview of the work done at the Centrum Wiskunde & Informatica (CWI) and Delft University of Technology (TU Delft) for different tracks of TREC 2013. We participated in the Contextual Suggestion Track, the Federated Web Search Track, the Knowledge Base Acceleration (KBA) Track, and the Web Ad-hoc Track. In the Contextual Suggestion track, we focused on filtering the entire ClueWeb12 collection to generate recommendations according to the provided user profiles and contexts. For the Federated Web Search track, we exploited both categories from ODP and document relevance to merge result lists. In the KBA track, we focused on the Cumulative Citation Recommendation task where we exploited different features to two classification algorithms. For the Web track, we extended an ad-hoc baseline with a proximity model that promotes documents in which the query terms are positioned closer together.
Fast Analytic Electromigration Analysis for General Multisegment Interconnect Wires

Electromigration (EM) is considered to be one of the most important reliability issues for current and future ICs in 10-nm technology and below. In this article, we propose a fast analytic solution to compute the stress evolution in the confined multisegment interconnect wires. The new method, called the accelerated separation of variables (ASOV) method, aims to find the analytic solutions of the partial differential equations of stress in confined interconnect metals based on the SOV method. It offers several improvements over the existing plain SOV-based method. First, we show that the accuracy of the solution depends on the structure of the interconnects. As a result, the number of required eigenvalues is structure and problem dependent, instead of fixed numbers used by the existing SOV method. Second, for the straight line multisegment and star-structured multiterminal interconnects, analytical expressions are formulated to calculate the eigenvalues directly instead of using numerical methods as in the existing SOV method. Third, we propose a linear Gaussian elimination (GE) algorithm by exploiting the banded structure with the serrated-edge form of the transcendental matrix, which can significantly speed up GE process, and is the key computing step in the SOV-based solution framework. Fourth, instead of using the simple bisection search, we propose to use an enhanced determinant-based secant iterative method to find the eigenvalues of the transcendental matrix. Numerical results show that a good agreement is achieved between analytical and numerical results on two special cases, and the resulting algorithm can lead to 3–5X speedup over the existing plain SOV-based solution on a number of multisegment interconnects benchmarks.
A Remark on Hierarchical Threshold Secret Sharing

The main results of this paper are theorems which provide a solution to the open problem posed by Tassa [1]. He considers a specific family Γν of hierarchical threshold access structures and shows that two extreme members Γ and Γv of Γν are realized by secret sharing schemes which are ideal and perfect. The question posed by Tassa is whether the other members of Γν can be realized by ideal and perfect schemes as well. We show that the answer in general is negative. A precise definition of secret sharing scheme introduced by Brickell and Davenport in [2] combined with a connection between schemes and matroids are crucial tools used in this paper. Brickell and Davenport describe secret sharing scheme as a matrix M with n+1 columns, where n denotes the number of participants, and define ideality and perfectness as properties of the matrix M. The auxiliary theorems presented in this paper are interesting not only because of providing the solution of the problem. For example, they provide an upper bound on the number of rows of M if the scheme is perfect and ideal.
Towards a Theoretical Understanding of the Power of Restart in SAT solvers

Restarts are a widely used class of techniques integral to the efficiency of Conflict-Driven Clause Learning (CDCL) SAT solvers. While the utility of such policies has been wellestablished empirically, to-date we still lack a deep theoretical understanding of why restart policies are crucial to the power of CDCL SAT solvers. This thesis first presents studies on three classes of formulas which were conjectured to be able to exponentially separate a solver configuration S with restarts and S with no restarts. However we were able to prove that these candidates classes of formulas are not even sufficient to super-polynomially separate the two solver configurations. We then provide a series of results that theoretically provide evidences for establishing the power of restarts for various models of Boolean SAT solvers. More precisely, we make the following contributions. First, we introduce a new class of satisfiable instances called Laddern and use it to construct another formula FT , and prove that for the drunk randomized DPLL SAT solver D (introduced by Alekhnovich and Razborov), the configuration D with restarts can solve Laddern formulas in sub-exponential time in size of Laddern, while D without restarts requires exponential time in the size of Laddern, with high probability. Two crucial insights enabled us to prove this separation result for restarts: first, we changed the focus from unsatisfiable instances to satisfiable ones; second, we observed that, at least for the models we considered, restart heuristics add proof-theoretic or algorithmic power by compensating for the weaknesses in some other important heuristic like value selection. Second, we introduce a key new notion used in above-mentioned proofs, called Decision Complexity dc(φ), for DPLL proofs of an unsatisfiable formula φ and show that size of DPLL proofs of φ are lower bounded by 2.
An FCA-based Boolean Matrix Factorisation for Collaborative Filtering

We propose a new approach for Collaborative Filtering which is based on Boolean Matrix Factorisation (BMF) and Formal Concept Analysis. In a series of experiments on real data (Movielens dataset) we compare the approach with the SVD- and NMF-based algorithms in terms of Mean Average Error (MAE). One of the experimental consequences is that it is enough to have a binary-scaled rating data to obtain almost the same quality in terms of MAE by BMF than for the SVD-based algorithm in case of non-scaled data.
Digital Donation Platform for Nonprofit and Charity Organizations

Social economy has assumed a role of growing importance in these last decades, being seen by several entities and political agents as one of the pillars in a new sustainable economic model. Volunteer programs are multiplying, and greater coordination between the various involved actors is essential to guarantee that society's help reaches the people who need them. In this sense, a digital donation platform based exclusively on open source technologies has been proposed and developed, which simplifies the process of donation of goods to a set of causes created by social solidarity institutions. The application permits anonymous donations and allows the monitoring of the status of each donation by the donors. Additionally, usability was considered as a key element in the design of the application
Multi-User Offloading for Edge Computing Networks: A Dependency-Aware and Latency-Optimal Approach

Driven by the tremendous application demands, the Internet of Things (IoT) systems are expected to fulfill computation-intensive and latency-sensitive sensing and computational tasks, which pose a significant challenge for the IoT devices with limited computational ability and battery capacity. To address this problem, edge computing is a promising architecture where the IoT devices can offload their tasks to the edge servers. Current works on task offloading often overlook the unique task topologies and schedules from the IoT devices, leading to degraded performance and underutilization of the edge resources. In this article, we investigate the problem of fine-grained task offloading in edge computing for low-power IoT systems. By explicitly considering: 1) the topology/schedules of the IoT tasks; 2) the heterogeneous resources on edge servers; and 3) the wireless interference in the multiaccess edge networks, we propose a lightweight yet efficient offloading scheme for multiuser edge systems, which offloads the most appropriate IoT tasks/subtasks to edge servers such that the expected execution time is minimized. To support the multiuser offloading, we also propose a distributed consensus algorithm for low-power IoT devices. We conduct extensive simulation experiments and the results show that the proposed offloading algorithms can effectively reduce the end-to-end task execution time and improve the resource utilization of the edge servers.
Cuffless Continuous Blood Pressure Estimation From Pulse Morphology of Photoplethysmograms

Cuffless blood pressure monitoring is critical for the prevention and early diagnosis of a wide variety of cardiovascular diseases. However, cuffless blood pressure estimation devices suitable for home healthcare applications still suffer from numerous technological limitations. In this paper, we propose a novel method to continuously estimate blood pressure from the pulse morphology of the photoplethysmographic signals, acquired by a commercial oximeter. We conducted cold-pressor tests for 70 healthy subjects, simultaneously acquiring the photoplethysmograms from the oximeter and the continuous blood pressure from a commercial sphygmomanometer as a reference. The shape of each pulse was extracted, normalized, and regressed with the reference blood pressure using support vector machines. The performance of the regression model for each subject was evaluated using 10-fold cross-validation. Results showed that the predicted values from the regression model were highly correlated with the reference measurements from the commercial device. The mean correlation coefficients were 0.748, 0.754, and 0.775 for systolic, diastolic, and mean blood pressures, respectively. The mean errors were 0.043 mmHg, 0.011 mmHg, and 0.008 mmHg, with standard deviations of 5.001 mmHg, 3.689 mmHg, and 4.140 mmHg for systolic, diastolic, and mean blood pressures, respectively. These results suggest high feasibility of the proposed method to be employed in wearable medical and home healthcare devices.
An RC-Network Approach for HVAC Precooling Optimization in Buildings

To lower buildings’ significant energy consumption and high impacts on environmental sustainability, recent years have witnessed rapidly growing interests in efficient HVAC precooling control and optimization. However, due to the complex analytical modeling of building thermal transfer, rigorous mathematical optimization for HVAC precooling is highly challenging. As a result, progress on HVAC precooling optimization remains limited in the literature. Our main contribution is that we overcome the aforementioned challenge and propose an accurate and tractable HVAC precooling optimization framework. The main results of this paper are three-fold: i) We develop an RC-network-based analytical model for multi-zone HVAC precooling to minimize both total energy costs and peak load demand. ii) We show that the HVAC procooling optimization problem based on the proposed RC network model admits a convex approximation, which enables an efficient optimization algorithm design. iii) Based on the convex approximation insight and by exploiting special problem structures, we develop an efficient distributed algorithm to solve the HVAC precooling optimization problem. Further, we conduct extensive simulation studies to verify the performance of our proposed mathematical model and algorithms. Our numerical results indicate that compared with the five existing HVAC control strategies, the proposed algorithm consistently outperforms existing state-of-the-art approaches.
Fostering Computational Thinking skills in the Last Years of Primary School

The introduction of Computational Thinking (CT) in Italian compulsory schools is on the way, and there is a general need for new methodologies to support teachers’ work. A one-year long learning path supporting the development of CT skills in primary school students through game making activities was defined and tested in a case study with one grade 5 class. All students in the class were actively involved regardless of their personal interest, their participation was generally high, their skills increased along the project, and the main objectives were reached. Nevertheless, results suggest that a longer time span is needed for students to master deeply the new concepts and tools, class organization proved to be crucial and this confirms the need for an adequate teachers’ training before introducing CT in classroom activities. Students demonstrated to be on the right path to approach CT through game making, but they still need to be guided in taking the player’s perspective and making their games easy to be understood and played. At the end of the case study, the learning path was revised and is now in use in a three-year long project in a primary school.
A Low-Cost Tonometer Alternative: A Comparison Between Photoplethysmogram and Finger Ballistocardiogram and Validation Against Tonometric Waveform

Hypertension is a silent killer and one-third of its sufferers are unaware of its presence. Tonometric devices, like SphygmoCor, Compilor etc., represent the gold standard in pulse wave velocity (PWV) and augmentation index (AIx) measurements which are limited by their high cost and operational accuracy. Here, we present an alternative technology that is low cost and may be suitable for the ‘wearable’ setting. We undertook the comparisons of arterial waveforms obtained by photoplethysmogram (PPG) and finger ballistocardiogram (BPP) sensors which were then validated against a SphygmoCor tonometric device. Specifically, the agreement analysis of the augmentation, stiffness, reflection, elasticity, ejection elasticity and dicrotic reflection indexes showed that arterial distension waveform sensing using BPP sensor, has precision and accuracy similar to that of a SphygmoCor tonometric device whilst outperforming the volumetric arterial flow sensing using a PPG sensor, in every index. BPP indexes showed the r2 fit of up to 0.95 and Spearman’s rank correlation up to 0.91 when validated against the SphygmoCor tonometer. The estimated individual transfer functions for the BPP sensor, with reference to SphygmoCor, have accuracies of above 85% and 98% for 2 and 4-element windkessel (WK) models, respectively. The findings reported in this work may also be useful for the development of systems that are beneficial in the early and/or routine detection of hypertension.
Analysis, design and performance evaluation of an LED driver with unity power factor and constant-current primary sensing regulation

This work focuses on an isolated offline driver to power LED lamps, realized with a high-power-factor quasi-resonant (Hi-PF QR) flyback converter with peak current mode control and employing constant-current primary-sensing regulation (CC-PSR). The converter is controlled with a recently introduced control technique that enables this kind of converter to ideally draw a sinusoidal current from the input source and, at the same time, to accurately regulate the dc output current using only quantities available on the primary side. The resulting absence of an optocoupler or other means crossing the isolation barrier to close a feedback loop not only reduces size and cost of the driver but also brings greater safety and reliability. The analysis addresses those factors inherent in the control method that affect the shape of the input current that have not been covered in the existing literature. The aim is to set up some design guidelines to minimize the Total Harmonic Distortion (THD) of the input current. The experimental work shows that using this technique enables the design of an LED driver for wide range mains (90 to 264 Vac) that achieves output current regulation better than ±2%, power factor close to unity and THD of the input current <10% over the input voltage range and over a 2:1 range of the output voltage.
Joint Optimization of Pico-Base-Station Density and Transmit Power for an Energy-Efficient Heterogeneous Cellular Network

Heterogeneous cellular networks (HCNs) have emerged as the primary solution for explosive data traffic. However, an increase in the number of base stations (BSs) inevitably leads to an increase in energy consumption. Energy efficiency (EE) has become a focal point in HCNs. In this paper, we apply tools from stochastic geometry to investigate and optimize the energy efficiency (EE) for a two-tier HCN. The average achievable transmission rate and the total power consumption of all the BSs in a two-tier HCN is derived, and then the EE is formulated. In order to maximize EE, a one-dimensional optimization algorithm is used to optimize picocell BS density and transmit power. Based on this, an alternating optimization method aimed at maximizing EE is proposed to jointly optimize transmit power and density of picocell BSs. Simulation results validate the accuracy of the theoretical analysis and demonstrate that the proposed joint optimization method can obviously improve EE.
University of Southern Denmark Designing a Flexible Grasp Tool and Associated Grasping Strategies for Handling Multiple Meat Products in an Industrial Setting

In this paper, we present a suction cup based gripper tool for grasping different cuts of meat. The tool is attached to a 6DOF robot arm in order to test different grasp strategies. The task of grasping pork bellies is difficult since the objects are highly flexible. Due to the deformations, an airtight connection can form between the meat and the object below it. During the lift, this can lead to a vacuum which sometimes causes the grasp to fail. Thus it is important that the tool and the grasp strategy is able to minimize the effects of this vacuum in order to generate a stable grasp. Furthermore, there is a high variation in the meat products and the initial placement of the products. Thus the tool has to be sufficiently flexible to handle the different cases robustly. 
Real-Time Deep-Learning-Based System for Facial Recognition

—This work presents an open code deep-learning-based system to perform facial recognition. The system is composed of ﬁve main steps: face segmentation, facial features detection, face alignment, embedding, and classiﬁcation. We use using deep learning methods for the ﬁducial points extraction and embedding. Support Vector Machine (SVM) is used for classiﬁcation task since it is fast for both training and inference. The system achieves an error rate of 0 . 12103 for facial features detection, which is pretty close to state of the art algorithms, and 0 . 05 for face recognition. Besides, it is capable to run in real-time.
Bearing Condition Monitoring based on the Indicator Generated in Time-frequency Domain

Most condition monitoring systems rely on system-driven generation of indicators or features for early fault detection. However, this strategy requires the prior knowledge on the system kinematics and/or exact structure parameters of monitored system. To address this problem, this paper presents a novel condition monitoring framework where the condition indicator is generated via data-driven method. In this framework, the time-frequency periodogram is extracted from raw vibration signal first. Then, the acquired time-frequency periodogram is mapped by pseudo Perron vector, which is learned from vibration data, to generate the condition indicator. Finally, the bearing can be monitored via analyzing this indicator using gaussian based control chart. Based on experimental results on a publicly-available database, we show the effectiveness of presented framework for early fault detectionin the continuous operation of rolling bearing, indicating its great potentials in real engineering applications.