Miracle-dz/UniFAR-FTUs huggingface dataset:
Processing ANN Traffic Predictions for RAN Energy Efficiency

The field of networking, like many others, is experiencing a peak of interest in the use of Machine Learning (ML) algorithms. In this paper, we focus on the application of ML tools to resource management in a portion of a Radio Access Network (RAN) and, in particular, to Base Station (BS) activation and deactivation, aiming at reducing energy consumption while providing enough capacity to satisfy the variable traffic demand generated by end users. In order to properly decide on BS (de)activation, traffic predictions are needed, and Artificial Neural Networks (ANN) are used for this purpose. Since critical BS (de)activation decisions are not taken in proximity of minima and maxima of the traffic patterns, high accuracy in the traffic estimation is not required at those times, but only close to the times when a decision is taken. This calls for careful processing of the ANN traffic predictions to increase the probability of correct decision. Numerical performance results in terms of energy saving and traffic lost due to incorrect BS deactivations are obtained by simulating algorithms for traffic predictions processing, using real traffic as input. Results suggest that good performance trade-offs can be achieved even in presence of non-negligible traffic prediction errors, if these forecasts are properly processed.
Artificial Neural Network Performance Evaluation for a Hybrid Power Domain Orthogonal / Non-Orthogonal Multiple Access (OMA / NOMA) System

Next-generation wireless technologies face considerable challenges in terms of providing the required latency and connectivity for new heterogeneous mobile networks. Driven by these problems, this study focuses on increasing user connectivity together with system throughput. For doing so, we propose and evaluate a hybrid machine learning-driven orthogonal/non-orthogonal multiple access (OMA/NOMA) system. In this work, we use an artificial neural network (ANN) to assign an OMA or NOMA access method to each user equipment (UE). As part of this research we also evaluate the accuracy and training time of the three most relevant learning algorithms of ANN (L-M, BFGS, and OSS). The main objective is to increase the sum-rate of the mobile network in the introduced beamforming and mmWave channel environment. Simulation results show up to a $20%$ sum-rate average performance increase of the system using the ANN management in contrast to a random non-ANN managed system. The Leveberg-Marquard (L-M) training algorithm is the best overall algorithm for this proposed application as presents the highest accuracy of around $77%$ despite 37 minutes of training and lower accuracy of $73%$ with approximately 28 seconds of training time.
Metazoan Parasite Communities of Three Endemic Cichlid Fish Species from the Upper Grijalva River, Chiapas, Mexico

Summary We recorded the metazoan parasite communities in three endemic cichlids (Chiapaheros grammodes, Vieja breidohri and V. hartwegi) collected between November 2008 and July 2009 in the upper Grijalva River Basin (GRB), Chiapas, Mexico. In total, 6,287 individual parasites belonging to 18 taxa (1 monogenean, 6 digeneans, 1 cestode, 4 nematodes, 2 acanthocephalans, 1 hirudinean, 2 copepods and 1 pentastomid) were found. Eleven metazoans were adult forms and 7 larvae; moreover, 14 were endoparasites and 4 ectoparasites. Sixteen parasite taxa represent new geographical and host records. The helminth community in the three cichlids was characterized by higher number of generalists than specialists, as well as a higher proportion of autogenics than allogenics. The metazoan parasites showed prevalence and mean abundances moderate to high. The infracommunities and component community of metazoan parasites had low diversity, richness, and number of individuals and are similar to those reported for other cichlids in Southeastern Mexico, characterized by the presence of typical parasites of cichlids, with a high number of digeneans and generalist parasites. We report the introduced Asian parasitic copepod Neoergasilus japonicus parasitizing endangered or threatened endemic cichlids in the upper GRB. This copepod have been widespread in other freshwater fish species, mainly in Asia (China, India, Japan, Russia, Taiwan), Europe (France, Hungary, Italy, Turkey), and America (Cuba, Mexico, Peru, United States).
Evaluating Person Re-identification Performance on GAN-enhanced Datasets

: Person re-identiﬁcation remains a hard task for AI systems because high intra-class variance across different cameras, angles and lighting conditions make it difﬁcult to create a reliable re-identiﬁcation system. Since only small datasets for person re-id tasks are available, in recent years Generative Adversarial Networks (GANs) have become popular to improve intra-class variance to train more robust re-identiﬁcation frameworks. In this work we evaluate an Inception-ResNet-v2 using triplet loss, introduced by (Weinberger and Saul, 2009), which works very well for face re-identiﬁcation and use it for full-body person re-identiﬁcation. The network is trained without GAN generated images to get a baseline accuracy of the network. In further experiments, the network is trained by adding constantly rising amounts of synthetic images produced by two image generators using different generating approaches.
Helminth Parasites and Diet of Leptodactylus Petersii (Steindachner, 1864) (Anura: Leptodactylidae) from Amapá State, Eastern Amazon, Brazil

Summary Leptodactylus petersii is a species of anuran found in both terrestrial and aquatic habitats and occurs from South America to southern North America and the West Indies. Studies involving the fauna of anuran parasites offer complementary information related to ecology. Thus, since there are few studies on the natural history of this species, this research aims to analyze the diet and the presence of endoparasitic helminths of Leptodactylus petersii from the state of Amapá, Brazil. We found 10 different taxonomic categories of prey in stomach contents, with the categories Hymenoptera (Formicidae) with 32.26 % (n = 12) being the most representative. Among the 12 individuals of L. petersii that were analyzed for helminth parasites, 83.3 % were infected with at least one species of helminths allocated to Phylum Nematoda. Our results report a new occurrence site for Rhabdias breviensis, originally described for Leptodactylus petersii in the state of Pará, as well as the second report of Ortleppascaris sp. in Brazil.
Political Will, Political Skill, Network Resources and Personal Reputation: A Serial Two-Mediator Model

Abstract Purpose: Although social capital and positive reputation in organizations are essential for career success, there is a limited perspective in the literature on the political characteristics (motivation and ability) that enable one to acquire and develop the two elements. This study seeks to investigate the sequential role of political skill and network resources as serial mediators of the political will–personal reputation relationship. Methodology: In total, what provided data for the study were 457 sales executives from 13 different sectors in the cities of İstanbul, Kocaeli, and Bursa, which is an economically leading region of western Turkey. Executives rated their political will, political skill network resources, and personal reputation. Based on the complementary theories of political influence, social network, and signaling, we analyzed the relationships between constructs with structural equation modeling. Findings: Political skill mediated the relationship between political will and network resources, network resources mediated the relationship between political skill and personal reputation, while political skill and network resources sequentially mediated the relationship between political will and personal reputation. Implications: The data were collected from a single source. Practical Implications: Political will, political skill training, and social networks may help individuals manage their personal reputation at work, thus benefiting their careers. Originality/Value: This is one of the first studies to sequentially investigate how individuals’ characteristics (motivation and ability) develop their social network and personal reputation at work. Moreover, theories of political influence, social capital, and signaling were jointly used for the first time ever.
Robot-mediated intervention can assist children with autism to develop visual perspective taking skills

Abstract In this work, we tested a recently developed novel methodology to assist children with Autism Spectrum Disorder (ASD) improve their Visual Perspective Taking (VPT) and Theory of Mind (ToM) skills using the humanoid robot Kaspar. VPT is the ability to see the world from another person’s perspective, drawing upon both social and spatial information. Children with ASD often find it difficult to understand that others might have perspectives, viewpoints and beliefs that are different from their own, which is a fundamental aspect of both VPT and ToM. The games we designed were implemented as the first attempt to study if these skills can be improved in children with ASD through interacting with a humanoid robot in a series of trials. The games involved a number of different actions with the common goal of helping the children to see the world from the robot’s perspective. Children with ASD were recruited to the study according to specific inclusion criteria that were determined in a previous pilot study. In order to measure the potential impact of the games on the children, three pre- and post-tests (Smarties, Sally–Anne and Charlie tests) were conducted with the children. Our findings suggest that children with ASD can indeed benefit from this approach of robot-assisted therapy.
Transfer Learning to Extract Features for Personalized User Modeling

Personalized Recommender Systems help users to choose relevant resources and items from many choices, which is an important challenge that remains actuality today. In recent years, we have witnessed the success of deep learning in several research areas such as computer vision, natural language processing, and image processing. In this paper, we present a new approach exploiting the images describing items to build a new user’s personalized model. With this aim, we use deep learning to extract latent features describing images. Then we associate these features with user preferences to build the personalized model. This model was used in a Collaborative Filtering (CF) algorithm to make recommendations. We apply our approach to real data, the MoviesLens dataset, and we compare our results to other approaches based on collaborative filtering
Influence des polymères de type superplastifiants et agents entraineurs d’air sur la viscosité macroscopique des matériaux cimentaires

Depuis quelques annees, le beton connait une periode de mutation. Les tendances actuelles concernant la formulation des betons a hautes performances et a faibles impacts environnementaux montrent que la fraction volumique solide est de plus en plus elevee. Cette augmentation de la fraction volumique solide est cependant difficilement compatible avec une fluidite importante. La these presentee ici a donc pour but de proposer des solutions permettant de contourner le probleme de viscosite des ces nouveaux betons. Nous nous attachons ainsi a identifier les mecanismes d'action de certains polymeres a l'origine d'une diminution de la viscosite macroscopique de pâtes de ciment concentrees. Les polymeres que nous etudions appartiennent a deux familles differentes : les super plastifiants et les agents entraineurs d'air. Alors que le chapitre 1 a pour objectif d'expliquer le contexte actuel et de justifier l'interet des recherches menees durant cette these, le chapitre 2 presente les procedures experimentales utilisees. Nous proposons des protocoles permettant de faire la distinction entre les effets des polymeres etudies sur la contrainte seuil et leurs effets sur l'autre parametre du comportement :la viscosite. Dans le chapitre 3, nous mettons en evidence certains mecanismes d'action des polymeres adsorbants de type super plastifiants. Nous observons que deux polymeres peuvent avoir un effet different sur la dissipation visqueuse d'une pâte de ciment pour une contrainte seuil donnee. Nous suggerons alors que les molecules de polymere adsorbe modifient l'etat de floculation du systeme, et donc la facon dont le cisaillement se concentre entre les grains. Dans le meme temps, les molecules de polymere non adsorbe modifient la viscosite du fluide interstitiel. La viscosite macroscopique resulte alors de la competition entre ces deux mecanismes. Dans le chapitre 4, nous nous interessons aux effets des agents entraineurs d'air. Grâce a des mesures sur pâtes de ciment et mortiers, nous montrons que, suivant la consistance du systeme etudie, l'entrainement d'air peut diminuer ou non la viscosite. Nous suggerons qu'un tel comportement trouve son origine dans la competition entre la tension de surface qui tend a empecher la deformation des bulles et la consistance du systeme en ecoulement qui tend a les deforme
Looking for the Hardest Hamiltonian Cycle Problem Instances

: We use two evolutionary algorithms to make hard instances of the Hamiltonian cycle problem. Hardness, or ﬁtness, is deﬁned as the number of recursions required by Vandegriend-Culberson, the best known exact backtracking algorithm for the problem. The hardest instances, all non-Hamiltonian, display a high degree of regularity and scalability across graph sizes. These graphs are found multiple times through independent runs and in both algorithms, suggestion the search space might contain monotonic paths to the global maximum. The one-bit neighbourhoods of these instances are also analyzed, and show that these hardest instances are separated from the easiest problem instances by just one bit of information. For Hamiltonian-bound graphs, the hardest instances are less uniform and substantially easier than their non-Hamiltonian counterparts. Reasons for these less-conclusive results are presented and discussed.
XCSF for Automatic Test Case Prioritization

Testing is a crucial part in the development of a new product. Due to the change from manual testing to automated testing, companies can rely on a higher number of tests. There are certain cases such as smoke tests where the execution of all tests is not feasible and a smaller test suite of critical test cases is necessary. This prioritization problem has just gotten into the focus of reinforcement learning. A neural network and an XCS classifier system have been applied to this task. Another evolutionary machine learning approach is the XCSF which produces, unlike XCS, continuous outputs. In this work we show that XCSF is superior to both the neural network and XCS for this problem.
A Visual Intelligence Scheme for Hard Drive Disassembly in Automated Recycling Routines

As the state-of-the-art deep learning models are taking the leap to generalize and leverage automation, they are becoming useful in real-world tasks such as disassembly of devices by robotic manipulation. We address the problem of analyzing the visual scenes on industrial-grade tasks, for example, automated robotic recycling of a computer hard drive with small components and little space for manipulation. We implement a supervised learning architecture combining deep neural networks and standard pointcloud processing for detecting and recognizing hard drives parts, screws, and gaps. We evaluate the architecture on a custom hard drive dataset and reach an accuracy higher than 75% in every component used in our pipeline. Additionally, we show that the pipeline can generalize on damaged hard drives. Our approach combining several specialized modules can provide a robust description of a device usable for manipulation by a robotic system. To our knowledge, we are the pioneers to offer a complete scheme to address the entire disassembly process of the chosen device. To facilitate the pursuit of this issue of global concern, we provide a taxonomy for the target device to be used in automated disassembly scenarios and publish our collected dataset and code.
The Role of an Enterprise Social Networking Tool on Organisational Knowledge Dynamics

Enterprise social networking (ESN) technologies aim to have a profound impact on knowledge management within companies in making things simpler, more fluid and dynamic for users. Their implementation within organizations raises many questions on their ability to (1) promote knowledge sharing, and (2) create the needed support for knowledge creation. Our study was conducted using Design methodology and carried out within an international company. The company had chosen an ESN tool to develop a dynamic knowledge management system. Three key results have been identified so far. First, the emergence of a virtuous and/or vicious circle of Knowledge Management. Second, well managed gamification can facilitate knowledge diffusion. Third, this type of technology requires a more federative governance, especially at its implementation and initial stages, to build the knowledge management system.
Comparing Creativity, User-experience and Communicability Linked to Digital Tools during the Fuzzy Phases of Innovation

Innovation is defined by a range of activities having different goals but driven by the same purpose. For example, in the ending phases, the aim will be to put forward precise concepts, while upstream of innovation, the activities are defined by the will to investigate the subject and increase the area of knowledge and concepts helpful for the conception of new products. This study takes place in the latter contexts because these are the ones where tools are the most variable and de facto, the least normalised. Our aim was to study user experience felt by the usage of these tools as well as their impact on creativity and ideas’ communicability. To do this, we led an experimental study with 79 participants comparing four tools: pen & paper, Virtual-Reality (VR) drawing, VRCAD, and traditional CAD. Thanks to the UEQ (Laugwitz et al., 2008) and judges method of Cropley and Cropley (2008), we measured the user-exprience and the creativity. Then we compared the level of creativity, user-experience and communicability induced by each tool. The results reveal that the user experience arising from the tool influences the amount and the quality of the ideas. Moreover, we show that the fewer standardises interactions the tools have, the greater the communicability of ideas.
Collaborative Virtual Reality as an Adaptable Boundary Object in the Design Phase of Facility Life Cycle

Although the large majority of costs of buildings incur in the later operation and maintenance phase, major decisions affecting these costs are made in the early design and construction phases. Virtual Reality (VR) and Collaborative Virtual Reality (CVR) have been noticed to have significant potential in involving the expertise and needs of various stakeholders into the early design phases, increasing the quality of building designs and reducing related costs. Boundary Object Theory has been noticed useful in better understanding and improving the knowledge transfer of actors with different backgrounds and expertise. VR and CVR remain yet little studied as boundary objects. We will address this research gap in this study by aiming to understand how CVR can act as an adaptable boundary object in the building design phase of the facility life cycle. We have made use of a qualitative approach, consisting of a multiple case study approach and semi-structured interviews in Finnish AEC industry companies and organisations. We contribute to academic research by providing a deeper understanding of how CVR functions as a boundary object, which enhances the transfer of knowledge in new ways between various stakeholders in the building design phase.
Towards an Ontology for Representing a Student's Profile in Adaptive Gamified Learning System

: Learner and learning content are the key factors contributing towards the success of any adaptive learning system. Each learner searches for an adequate environment to his needs which offers personalized and adaptive content that provides a learning experience to be more successful and more useful to him. Moreover, he likes to study in a fun and entertaining environment that gives them a sense of engagement and motivation. Education research shows that considering student profile is effective in adapting courses and profile modeling is an important process that aims to give as complete representation as possible of all the aspects related to the user's features. With regard to motivation, some studies have approved that gamification is a good solution to enhance student engagement and that there is a strong link between it and motivation. Therefore, this article presents our contribution through a SPOnto ontology for representation of students profile, by combining the two concepts “adaptive learning” and “gamification” to provide a personalized gamified experience. We propose a student profile ontology, to benefit from semantic web technologies, which presents a global model of the student based on many important characteristics in order to help decision-making in the different academic contexts and to motivate him to achieve his learning goals.
Exploring the Texture of Ocean-Atmosphere Redox Evolution on the Early Earth

The evolution of oxygenic photosynthesis has dramatically reshaped the chemistry of the surface Earth, and the presence of significant quantities of O2 in the atmosphere and ocean now drives the fundamental dynamics of nearly all quantitatively significant biogeochemocal cycles (C, S, P, N, Fe). Whether by direct consumption through the metabolic demands of large, complex organisms, or through the recycling of essential substrates within microbial ecosystems, biologically produced O2 provides nearly all of the compounds used in metabolic electron transfer on a global scale. Although it is widely accepted that the partial pressure of O2 in Earth's atmosphere has increased through time (with attendant, although somewhat complex, changes in ocean ventilation), there is still much debate surrounding the timing of the emergence of oxygenic photosynthesis and little is known about the detailed tempo and mode with which this metabolic innovation came to shape early Earth surface chemistry. This dissertation explores the early oxygenation of Earth's atmosphere and the relationship between atmospheric oxygen levels and ocean ventilation from a variety of perspectives. First, empirical data based on an integrated suite of paleoredox proxies is used to suggest that biological oxygen production emerged and began exerting significant effects on Earth surface chemistry at least 100 million years prior to the initial accumulation of large quantities of O2 in the atmosphere (often referred to as the Great Oxidation Event , approximately 2.4 billion years ago). The implications of this time lag between metabolic innovation and large-scale biogeochemical reorganization are explored through a series of quantitative models, focusing on the thermodynamics and kinetics of mineral reactions under various Earth surface conditions, regional oceanographic modeling of surface ocean O2 cycling, and a global sulfur isotope mass balance model that explicitly incorporates rare sulfur isotope systematics ( 33 S, ( 36 S) and the dynamics of sedimentary recycling on long timescales. Finally, the dynamics of ocean ventilation following the initial accumulation of oxygen in the atmosphere are explored by combining a large trace metal database with a spatially explicit mass balance model that exploits the differing redox behavior and surface cycling of molybdenum (Mo) and chromium (Cr).