Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining shape and color. A bottom-up approach to evaluate object similarities

The objective of the present work is to develop a bottom-up approach to estimate the similarity between two unknown objects. Given a set of digital images, we want to identify the main objects and to determine whether they are similar or not. In the last decades many object recognition and classification strategies, driven by higher-level activities, have been successfully developed. The peculiarity of this work, instead, is the attempt to work without any training phase nor a priori knowledge about the objects or their context. Indeed, if we suppose to be in an unstructured and completely unknown environment, usually we have to deal with novel objects never seen before; under these hypothesis, it would be very useful to define some kind of similarity among the instances under analysis (even if we do not know which category they belong to). To obtain this result, we start observing that human beings use a lot of information and analyze very different aspects to achieve object recognition: shape, position, color and so on. Hence we try to reproduce part of this process, combining different methodologies (each working on a specific characteristic) to obtain a more meaningful idea of similarity. Mainly inspired by the human conception of representation, we identify two main characteristics and we called them the implicit and explicit models. The term "explicit" is used to account for the main traits of what, in the human representation, connotes a principal source of information regarding a category, a sort of a visual synecdoche (corresponding to the shape); the term "implicit", on the other hand, accounts for the object rendered by shadows and lights, colors and volumetric impression, a sort of a visual metonymy (corresponding to the chromatic characteristics). During the work, we had to face several problems and we tried to define specific solutions. In particular, our contributions are about: • defining a bottom-up approach for image segmentation (which does not rely on any a priori knowledge); • combining different features to evaluate objects similarity (particularly focus-
LIGHTWEIGHT SECURE SCHEME FOR IOT-CLOUD CONVERGENCE BASED ON ELLIPTIC CURVE

The internet of things appears as a solution to connect people around the world. Its utility lies in the ability to connect objects and exchange information anywhere and everywhere. Many objects and services in different fields will be created, such as smart homes, e health, transport and logistics. The evolution of IoT, increases the number of connected object that generates a huge number of data. However, with the low capacity of storage and processing of these objects, there is a requirement to connect these objects to a large pool of resource like Cloud computing. The convergence between IoT and Cloud, will bring many services that will be of great benefit to humanity. However, this convergence will not see the day unless the communication between devices and the Cloud is secure. Most of the secure scheme proposed, that we will quote in the following sections, either have a weakness on their scheme, or are based on Hypertext Transfer Protocol (HTTP) which consumes bandwidth and which will exhaust the resources of the devices. Publish / Subscribe is a messaging pattern where publishers publishes messages to subscribers. The use of protocols based on pub/sub like Message Queuing Telemetry Transport (MQTT) is very essential when response time, lower battery, bandwidth and throughput usage are on the first place for future solutions. In this paper, a secure Elliptic Curve Cryptography (ECC) protocol using Publish / Subscribe lightweight protocol has been proposed for creating a secure tunnel between IoT devices and Cloud Computing, and that can allow a very fast communication also it's a light protocol that will not exhaust the resources of the IoT object. In fact, we use the AVISPA tool for a formal verification of our proposed protocol.
DT-SFC-6G: Digital Twins Assisted Service Function Chains in Softwarized 6G Networks for Emerging V2 X

Vehicle-to-X (V2X) with specific user-defined performance metrics emerges as one vital application scenario in sixth generation (6G) networks. According to released reports and whitepapers, 6G networks are designed to have softwarization attributes. Through softwarization, tailored V2X services, constituted by softwarized (resource and function) blocks, can be deployed on top of underlying network appliances. Chaining these softwarized blocks in predefined order is abbreviated as service function chain (SFC). However, existing SFC studies are conducted on the assumption that no performance descending of network appliance exists. Due to inherent shortcomings of softwarization, the softwarized 6G network appliances will bring about performance descending, compared with dedicated hardware. Emerging digital twin (DT) technology paves one way for studying SFC placement and scheduling without worrying about performance descending. By creating a digital replica of softwarized 6G networks, tailored SFCs can be implemented efficiently. This article investigates the SFC placement and scheduling in DT-em-powered softwarized 6G networks for emerging V2X. First, a novel business model and a problem model are presented. Next, one framework design, abbreviated as DT-SFC-6G, is proposed, including all technical details. The DT-SFC-6G design guarantees to provide efficient placement and scheduling solution per SFC request and feedback SFC solution and digital replica's state to softwarized 6G networks in time. Furthermore, experimental work of DT-SFC-6G is conducted. A variety of SFC algorithms, inserted in the DT-SFC-6G design, are evaluated in order to highlight the feasibility and merits of DT-SFC-6G. Finally, three most promising directions of SFC in DT-empowered softwarized 6G networks are selected for discussions.
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning

In offline reinforcement learning (RL), an RL agent learns to solve a task using only a fixed dataset of previously collected data. While offline RL has been successful in learning real-world robot control policies, it typically requires large amounts of expert-quality data to learn effective policies that generalize to out-of-distribution states. Unfortunately, such data is often difficult and expensive to acquire in real-world tasks. Several recent works have leveraged data augmentation (DA) to inexpensively generate additional data, but most DA works apply augmentations in a random fashion and ultimately produce highly suboptimal augmented experience. In this work, we propose Guided Data Augmentation (GuDA), a human-guided DA framework that generates expert-quality augmented data. The key insight behind GuDA is that while it may be difficult to demonstrate the sequence of actions required to produce expert data, a user can often easily characterize when an augmented trajectory segment represents progress toward task completion. Thus, a user can restrict the space of possible augmentations to automatically reject suboptimal augmented data. To extract a policy from GuDA, we use off-the-shelf offline reinforcement learning and behavior cloning algorithms. We evaluate GuDA on a physical robot soccer task as well as simulated D4RL navigation tasks, a simulated autonomous driving task, and a simulated soccer task. Empirically, GuDA enables learning given a small initial dataset of potentially suboptimal experience and outperforms a random DA strategy as well as a model-based DA strategy.
ASPECTS OF THE DEPOSITIONAL HISTORY OF THE LEVANT BASIN, OFFSHORE CYPRUS AND LEBANON

The giant gas discoveries of Tamar, Leviathan and most recently Aphrodite, found within the Oligo-Miocene strata of the Levant Basin, have proven the significant hydrocarbon potential of the Eastern Mediterranean. Nevertheless, the region can still be considered underexplored and it is likely that deeper plays will be tested in the very near future. The geological development of the Levant Basin is complex, and the nature and origin of the basin fill are currently poorly understood. The well data for the Levant Basin is sparse and the Lebanese offshore area remains undrilled. However, valuable information can be obtained from the seismic data acquired across the basin. A dense 2D grid and multiple overlapping new 3D surveys, which provide a regionally consistent 3D seismic coverage, have been acquired. This data provides clearer imaging of the present structural pattern, within the regional context, and allows investigation of the depositional history of the northern Levant Basin. Several key horizons have been interpreted; providing a stratigraphic framework ranging from Upper Jurassic to present. Several canyons and conduits leading into the northern Levant Basin have been identified on the seismic data and amplitude extraction surfaces over specific Miocene intervals illustrate the distributary pattern of these sediment conduits. These conduits include channelized systems that may have delivered clastic material; from the east and northeast, probably coming off the Levant Margin, and from the south probably from the Nile Delta. The development of these channels coincides with a change in the depositional geometry of the basin that may be attributed to the onset of the final uplift of the Palmyrides.
TITAN: A Distributed Large-Scale Trapped-Ion NISQ Computer

Trapped-Ion (TI) technology offers potential breakthroughs for Noisy Intermediate Scale Quantum (NISQ) computing. TI qubits offer extended coherence times and high gate fidelity, making them appealing for large-scale NISQ computers. Constructing such computers demands a distributed architecture connecting Quantum Charge Coupled Devices (QCCDs) via quantum matter-links and photonic switches. However, current distributed TI NISQ computers face hardware and system challenges. Entangling qubits across a photonic switch introduces significant latency, while existing compilers generate suboptimal mappings due to their unawareness of the interconnection topology. In this paper, we introduce TITAN, a large-scale distributed TI NISQ computer, which employs an innovative photonic interconnection design to reduce entanglement latency and an advanced partitioning and mapping algorithm to optimize matter-link communications. Our evaluations show that TITAN greatly enhances quantum application performance by 56.6% and fidelity by 19.7% compared to existing systems.
A Survey On MAC Protocols for Wireless Adhoc Networks with Beamforming Antennas

The beamforming antenna technology is a promising solution to many challenges facing wireless ad hoc networks. Beamforming antennas have the ability to increase the spatial reuse, improve the transmission reliability, extend the transmission range and/or save the power consumption. If they are effectively used, they can significantly improve the network capacity, lifetime, connectivity and security. However, traditional Medium Access Control (MAC) protocols fail to exploit the potential benefits due to the unique characteristics of wireless ad hoc networks with beamforming antennas. To that end, numerous MAC protocols have been designed over the years to harness the offered potential. In this paper, we survey the literature on MAC protocols proposed for wireless ad hoc networks with beamforming antennas during the last decade. We discuss the main beamforming-related challenges facing the medium access control in ad hoc networks. We present taxonomy of the MAC protocols proposed in the literature based on their mode of operation and the mechanisms used to address the challenges. In addition, we provide a qualitative comparison of the protocols highlighting their features, benefits and requirements. Finally, we provide directions for possible future work.
Nonexistence of Voting Rules That Are Usually Hard to Manipulate

Aggregating the preferences of self-interested agents is a key problem for multiagent systems, and one general method for doing so is to vote over the alternatives (candidates). Unfortunately, the Gibbard-Satterthwaite theorem shows that when there are three or more candidates, all reasonable voting rules are manipulable (in the sense that there exist situations in which a voter would benefit from reporting its preferences insincerely). To circumvent this impossibility result, recent research has investigated whether it is possible to make finding a beneficial manipulation computationally hard. This approach has had some limited success, exhibiting rules under which the problem of finding a beneficial manipulation is NP-hard, #P-hard, or even PSPACE-hard. Thus, under these rules, it is unlikely that a computationally efficient algorithm can be constructed that always finds a beneficial manipulation (when it exists). However, this still does not preclude the existence of an efficient algorithm that often finds a successful manipulation (when it exists). There have been attempts to design a rule under which finding a beneficial manipulation is usually hard, but they have failed. To explain this failure, in this paper, we show that it is in fact impossible to design such a rule, if the rule is also required to satisfy another property: a large fraction of the manipulable instances are both weakly monotone, and allow the manipulators to make either of exactly two candidates win. We argue why one should expect voting rules to have this property, and show experimentally that common voting rules clearly satisfy it. We also discuss approaches for potentially circumventing this impossibility result.
Robust and Sparse Estimation of the Inverse Covariance Matrix Using Rank Correlation Measures

Spearman's rank correlation is a robust alternative for the standard correlation coefficient. By using ranks instead of the actual values of the observations, the impact of outliers remains limited. In this paper, we study an estimator based on this rank correlation measure for estimating covariance matrices and their inverses. The resulting estimator is robust and consistent at the normal distribution. By applying the graphical lasso, the inverse covariance matrix estimator is positive definite if more variables than observations are available in the data set. Moreover, it will contain many zeros, and is therefore said to be sparse. Instead of Spearman's rank correlation, one can use the Quadrant correlation or Gaussian rank scores. A simulation study compares the different estimators. This type of estimator is particularly useful for estimating (inverse) covariance matrices in high dimensions, when the data may contain several outliers in many cells of the data matrix. More traditional robust estimators are not well defined or computable in this setting. An important feature of the proposed estimators is their simplicity and easiness to compute using existing software.
Making Choices—Improving Behavior—Engaging in Learning

Do you have students who display inappropriate types of behavior, or who seem depressed, or who lack friends? Perhaps these are students with emotional and behavioral disorders (EBD; see box), and maybe the students present challenges that you find difficult to deal with and that interfere with their educational progress. This article highlights one strategy, providing opportunities to make choices, that is effective in increasing appropriate behaviors for students with EBD (Munk & Repp, 1994), most notably when used by classroom teachers during ongoing classroom routines (Jolivette, Wehby, Canale, & Massey, 2001b; see also the box on page 28, “What Does the Literature Say? ” for a link between the research literature on choice and related behavioral characteristics of students with EBD.) We present a hypothetical case example to show how a teacher might incorporate choice-making opportunities for a student with EBD who is failing math in school. In the course of this article, we suggest practical strategies that both special and general education teachers may use in their academic curricula to encourage students to make appropriate choices. These opportunities may have benefits that long outlast the particular task or situation, to enhance relationships within the classroom, engage students in learning, and promote a positive classroom environment.
Exhaled nitric oxide in children with allergic rhinitis: A potential biomarker of asthma development

Pearce N, Foliaki S, Wong G. Birthweight and the risk of atopic diseases: the ISAAC Phase III study. Pediatr Allergy Immunol 2014: 25: 264–70. 5. Briana D, Malamitsi-Puchner A. Small for gestacional age birth weight: impact on lung structure and function. Paediatr Respir Rev 2013: 4: 256–62. 6. Gluckman P, Hanson M, Cooper C, Thornburg K. Effect of in utero and earlylife conditions on adult health and disease. N Engl J Med 2008: 359: 61–73. 7. Duijts L. Fetal and infant origins of asthma. Eur J Epidemiol 2012: 27: 5–14. 8. Murphy V, Namazy J, Powell H, et al. A meta-analysis of adverse perinatal outcomes in women with asthma. BJOG 2011: 118: 1314–23. 9. Wandalsen G, Chong-Neto H, Souza F, Sol e D, Bacharier L. Early weight gain and the development of asthma and atopy in children. Curr Opin Allergy Clin Immunol 2014: 14: 126–30.
Evaluation of an automated microplate technique in the Galileo system for ABO and Rh(D) blood grouping.

BACKGROUND A number of automated devices for pretransfusion testing have recently become available. This study evaluated the Immucor Galileo System, a fully automated device based on the microplate hemagglutination technique for ABO/Rh (D) determinations. METHODS Routine ABO/Rh typing tests were performed on 13,045 samples using the Immucor automated instruments. Manual tube method was used to resolve ABO forward and reverse grouping discrepancies. D-negative test results were investigated and confirmed manually by the indirect antiglobulin test (IAT). RESULTS The system rejected 70 tests for sample inadequacy. 87 samples were read as "No-type-determined" due to forward and reverse grouping discrepancies. 25 tests gave these results because of sample hemolysis. After further tests, we found 34 tests were caused by weakened RBC antibodies, 5 tests were attributable to weak A and/or B antigens, 4 tests were due to mixed-field reactions, and 8 tests had high titer cold agglutinin with blood qualifications which react only at temperatures below 34 degrees C. In the remaining 11 cases, irregular RBC antibodies were identified in 9 samples (seven anti-M and two anti-P) and two subgroups were identified in 2 samples (one A1 and one A2) by a reference laboratory. As for D typing, 2 weak D+ samples missed by automated systems gave negative results, but weak-positive reactions were observed in the IAT. CONCLUSIONS The Immucor Galileo System is reliable and suited for ABO and D blood groups, some reasons may cause a discrepancy in ABO/D typing using a fully automated system. It is suggested that standardization of sample collection may improve the performance of the fully automated system.
Estimating causal effects.

Although one goal of aetiologic epidemiology is to estimate ‘the true effect’ of an exposure on disease occurrence, epidemiologists usually do not precisely specify what ‘true effect’ they want to estimate. We describe how the counterfactual theory of causation, originally developed in philosophy and statistics, can be adapted to epidemiological studies to provide precise answers to the questions ‘What is a cause? ’, ‘How should we measure effects? ’ and ‘What effect measure should epidemiologists estimate in aetiologic studies? ’ We also show that the theory of counterfactuals (1) provides a general framework for designing and analysing aetiologic studies; (2) shows that we must always depend on a substitution step when estimating effects, and therefore the validity of our estimate will always depend on the validity of the substitution; (3) leads to precise definitions of effect measure, confounding, confounder, and effect-measure modification; and (4) shows why effect measures should be expected to vary across populations whenever the distribution of causal factors varies across the populations.
Starch digestion capacity of poultry 1

Starch is quantitatively the most important nutrient in poultry diets and will to a large extent be present as intact starch granules due to very limited extent of gelatinization during pelleting. Although native starch is difficult to digest due to a semi-crystalline structure, even fast-growing broiler chickens appears to be able to digest this starch more or less completely during passage through the jejunum. However, reduced starch digestibility has been observed, particularly in pelleted diets containing large quantities of wheat. Although properties of the starch granule such as size and components on the granule surface may affect digestibility, the entrapment of starch granules in cell walls and a protein matrix may be even more important factors impeding starch digestion. In that case, this and the fact that amylase secretion is normally very high in poultry may explain the lack of convincing effects of exogenous α-amylase added to the diet. However, few well-designed experiments assessing mechanisms of starch digestion and the effect of α-amylase supplementation have been carried out, and thus more research is needed in this important area.
Improving HybrID: How to best combine indirect and direct encoding in evolutionary algorithms

Many challenging engineering problems are regular, meaning solutions to one part of a problem can be reused to solve other parts. Evolutionary algorithms with indirect encoding perform better on regular problems because they reuse genomic information to create regular phenotypes. However, on problems that are mostly regular, but contain some irregularities, which describes most real-world problems, indirect encodings struggle to handle the irregularities, hurting performance. Direct encodings are better at producing irregular phenotypes, but cannot exploit regularity. An algorithm called HybrID combines the best of both: it first evolves with indirect encoding to exploit problem regularity, then switches to direct encoding to handle problem irregularity. While HybrID has been shown to outperform both indirect and direct encoding, its initial implementation required the manual specification of when to switch from indirect to direct encoding. In this paper, we test two new methods to improve HybrID by eliminating the need to manually specify this parameter. Auto-Switch-HybrID automatically switches from indirect to direct encoding when fitness stagnates. Offset-HybrID simultaneously evolves an indirect encoding with directly encoded offsets, eliminating the need to switch. We compare the original HybrID to these alternatives on three different problems with adjustable regularity. The results show that both Auto-Switch-HybrID and Offset-HybrID outperform the original HybrID on different types of problems, and thus offer more tools for researchers to solve challenging problems. The Offset-HybrID algorithm is particularly interesting because it suggests a path forward for automatically and simultaneously combining the best traits of indirect and direct encoding.