Miracle-dz/UniFAR-FTUs huggingface dataset:
Extended Abstract: Simulation of Interactions between Beehives

The interdisciplinary EU project HIVEOPOLIS aims to develop a new generation of intelligent beehive which might help bees in coping with adverse environmental factors. As a part of the HIVEOPOLIS project, this extended abstract reports on our ongoing work on the simulation of a decision-making process based on interactions between HIVEOPOLIS units and bee colonies which are not equipped with HIVEOPOLIS systems using the Mesa simulation framework.
Towards an Ontology-Driven Approach for Digital Twin Enabled Governed IT Management

The Digital Transformation of our society requires IT infrastructures to be more agile, more adaptive, and more connected than ever. At the same time, the owners of such infrastructures are confronted with an increase in regulatory pressure (e.g., the GDPR). These developments put a lot of stress on IT management and governance. To enable IT management and governance to better deal with these challenges, we propose to digitally transform IT governance and management itself by using a Digital Twin based approach. In line with this, we aim to create on ontology-driven Digital Twin for Governed IT Management (DT4GITM) framework. The goal of this framework is to act as a reference architecture for a Digital Twin based infrastructure that connects three interrelated systems: the IT governance processes, the governed IT management processes, and the managed organizational IT assets. The core of the framework involves a generic Governed IT Management (GITM) Domain Ontology, which is planned to be operationalized by a Knowledge Graph based approach that realizes an integrated view on the heterogenous data streams originating from the IT governance and management processes, and the managed IT assets. In this paper, we start by outlining the planned DT4GITM framework and the pivotal role of the GITM Domain Ontology within this. We then elaborate our incremental, and scenario-/case-driven strategy towards the development of the framework as a whole, and the GITM Domain Ontology in particular. This is followed by the elaboration of a speciﬁc DT4GITM scenario which serves as a ﬁrst proof of principle.
FO Rewritability for OMQ using Beth Definability and Interpolation

We study ﬁrst-order (FO) rewritability for query answering in ontology mediated querying (OMQ) in which ontologies are formulated in Horn fragments of description logics (DLs). In general, OMQ approaches for such logics rely on non-FO rewriting of the query or on analogous completion of the data (ABox). In this paper, we study the problem of the existence of FO rewritings in terms of Beth deﬁnabil-ity, and show how Craig interpolation can then be used to eﬀectively construct the rewritings, when they exist, from the Clark’s completion of Datalog-like programs encoding a given DL TBox and optionally a query. We show how our construction can also be seen as an alternative to deriving perfect rewritings in the DL-Lite setting.
EXIST2021: Detecting Sexism with Transformers and Translation-Augmented Data

This paper describes a system created for the EXIST 2021 shared task, framed within the IberLEF 2021 workshop. We present an approach mainly based in ﬁne-tuned BERT models and Data Augmentation with translation and backtranslation. We show an approach to face multilingual problems augmenting the data without the overﬁtting that an aggressive backtranslation can generate. Our models far outperform the baselines and achieve results close to the state-of-the-art.
Classification of high-voltage power line structures in low density ALS data acquired over broad non-urban areas

Airborne laser scanning (ALS) has gained importance over recent decades for multiple uses related to the cartography of landscapes. Processing ALS data over large areas for forest resource estimation and ecological assessments requires efficient algorithms to filter out some points from the raw data and remove human-made structures that would otherwise be mistaken for natural objects. In this paper, we describe an algorithm developed for the segmentation and cleaning of electrical network facilities in low density (2.5 to 13 points/m2) ALS point clouds. The algorithm was designed to identify transmission towers, conductor wires and earth wires from high-voltage power lines in natural landscapes. The method is based on two priors i.e. (1) the availability of a map of the high-voltage power lines across the area of interest and (2) knowledge of the type of transmission towers that hold the conductors along a given power line. It was tested on a network totalling 200 km of wires supported by 415 transmission towers with diverse topographies and topologies with an accuracy of 98.6%. This work will help further the automated detection capacity of power line structures, which had previously been limited to high density point clouds in small, urbanised areas. The method is open-source and available online.
A novel approach for intrusion detection systems: V-IDS

: An intrusion detection system (IDS) is a security mechanism that detects abnormal activities in a network. An ideal IDS must detect intrusion attempts and maybe categorize them for further research and keep false-positive analysis at a very low level. IDSs are used in the analysis of network traﬀic data at all sizes. Studies on this subject focused on machine learning techniques. Even though the performance rates are high, it is seen that processes such as data understanding, preprocessing, and consistency tests are time-consuming and laborious. For this reason, the use of deep learning (DL) models that automatically perform the mentioned steps has become very popular. In this study, a high-performance approach that can be applied in real-time systems is proposed: visual IDS (V-IDS). NSL-KDD dataset, one of the large-scale datasets, is used. Data visualization techniques were applied in order to determine geometric relationships between records, and the data were classified by using the DL model. The model achieved 98% accuracy in total and even higher in some intrusion categories.
Emotion Detection for Spanish with Data Augmentation and Transformer-Based Models

In this paper we describe the participation of Yeti team in IberLEF EmoEvalEs task, which is based on the Spanish Semantic Analysis in TASS 2020 version, and proposes as separate task for 2021 in Iber-LEF. We introduce the methods we used in the emotion detection task and the results obtained. First, we used back-translation data augmentation technology to solve the problems of data scarcity and data imbalance. Our method is based on transfer learning using the BETO language model for sentiment classiﬁcation in Spanish. This system showed excellent performance and ﬁnally achieved an accuracy score of 0.7125. We won third place in the ﬁnal result, which is only 0.0151 points away from the best result.
"Do Users Need Human-like Conversational Agents?" - Exploring Conversational System Design Using Framework of Human Needs

Thefascinatingstoryofhumanevolutioncanbeattributedtoourabilitytospeak, write,andcommunicatecomplexthoughts. Whenresearchersenvisionaperfect, artificiallyintelligentconversationalsystem, theywantthesystemtobehuman-like. In other words, the system should converse with the same intellect and cognition as humans. Now, the question which we need toaskisifweneedahuman-likeconversationalsystem? Beforeweengageinthecomplexendeavorofimplementinghuman-likecharacteristics,weshoulddebateifthepursuitofsuchasystemislogicalandethical. Weanalyzesomeofthesystem-level characteristics and discuss their merits and potential of harm. We review some of the latest work on conversational systems to understand how design features are evolving for Conversational Agents. Additionally, we look into the framework of human needs to assess how the system should assign relative importance to user requests, and prioritize user tasks. We draw on the peer work in human-computer interaction, sentiment analysis, and human psychology to provide insights into how future conversational agents should be designed for better user satisfaction.
LargeGraph

Many out-of-GPU-memory systems are recently designed to support iterative processing of large-scale graphs. However, these systems still suffer from long time to converge because of inefficient propagation of active vertices’ new states along graph paths. To efficiently support out-of-GPU-memory graph processing, this work designs a system LargeGraph. Different from existing out-of-GPU-memory systems, LargeGraph proposes a dependency-aware data-driven execution approach, which can significantly accelerate active vertices’ state propagations along graph paths with low data access cost and also high parallelism. Specifically, according to the dependencies between the vertices, it only loads and processes the graph data associated with dependency chains originated from active vertices for smaller access cost. Because most active vertices frequently use a small evolving set of paths for their new states’ propagation because of power-law property, this small set of paths are dynamically identified and maintained and efficiently handled on the GPU to accelerate most propagations for faster convergence, whereas the remaining graph data are handled over the CPU. For out-of-GPU-memory graph processing, LargeGraph outperforms four cutting-edge systems: Totem (5.19–11.62×), Graphie (3.02–9.41×), Garaph (2.75–8.36×), and Subway (2.45–4.15×).
The Role of LSP P3 Junior Spa Therapist Competency Certification on Work Readiness and Absorption of the Tourism Industry

The fundamental problem in business is the low readiness of occupational therapists as experts. In addition, the low job readiness of therapists is exacerbated by the low absorption of prospective therapists. LSP P3 competency certification is an essential program for improving spa therapists' work readiness and labour absorption in the tourism industry. This study aims to analyze how much influence the competence of the LSP P3 Junior Spa certification on Asesi's work readiness and Asesi's absorption in the spa tourism industry. Ex-post facto research with a quantitative approach. A total of 66 respondents of spa therapist assessment were included in this study as a sample. Data were collected through a questionnaire with a 4 Likert scale questionnaire instrument. Data analysis was performed using regression analysis and SEM analysis. The results showed that the LSP P3 Junior Spa Therapist competency certification had a significant effect on Asesi's work readiness and Asesi's absorption in the tourism industry in the spa sector. The certification program must improve the quality of its implementation. Educational and training institutions must organize various competency certification programs that are even more optimal, especially when competency testing must be carried out in-depth material by the perceptions of the spa therapist workforce.
Soft-sensor models to estimate the efficiency of H2S removal from an oil refinery stream of nonphenolic sour water

ABSTRACT A large set of results of concentration of hydrogen sulfide in the feed and bottom product streams of a sour water stripper found in a typical oil refinery was experimentally obtained. The readings of H2S concentrations were at several different operating conditions in terms of the main process variables that classically have significant effects on the efficiency of H2S removal (E). In particular, the considered factors were the mass flow rate, and temperature of sour water fed into the stripper, the mass flow rate of external steam injected into the reboiler, the difference between the temperature of the product stream leaving and entering the reboiler, and the difference of pressure at the two ends of the tower. Three different soft-sensor models were suggested to describe the observed variation in E from 63 to 97%, namely, an equilibrium, statistical and an artificial neural network model. The best of them was the neural network one with three input variables, four neurons in the one-hidden layer, and a hyperbolic tangent function for both the output and one-hidden layers. The mean absolute relative deviation between measured and calculated E by involving this model was only approximately 2.5% with negligible tendency for the residuals. It confirms the reliability of this approach as a tool to inferential estimation of the efficiency of removal of H2S from the sour water by stripping.
Introvert and extrovert personality: Is it correlated with academic achievement of Physical Education, Health and Recreation students at university level?

Psychological elements, namely introverted and extroverted personalities, are currently one of the keys to determining success in academics at the University level, but it is not clear which type is more correlated with academic achievement. The purpose of this study is to investigate the correlation between introverted and extroverted personalities with student academic achievement. The subjects in this study come from the PJKR Department of the University of Suryakancana (N=20) and Halu Oleo (N=20). Data analysis uses SPSS version 25 to find descriptive statistics, normality, data linearity and person product moment correlation. The level of significance used is 0.05. The results of the study finds that introverted personality is significantly correlated with academic achievement in the high category (r= 0.749**), and extroverted personality is not significantly correlated with academic achievement (r= -0.120). Thus, it can be concluded that the personality of students with introverted type is far superior to extroverts in academic achievement at the University level. The contribution in this study contributes to knowledge in the field of physical education psychology, so that later lecturers can pay more attention and optimize academic achievement in students with both types, especially the extroverted type.
Visualizing Feature Maps for Model Selection in Convolutional Neural Networks

Convolutional neural networks (CNN) are increasingly being used to achieve state-of-the-art performance for various plant phenotyping and agricultural tasks. While con-structing such CNN models, a common problem is over-parameterization, which may lead to a model becoming overfit on a training dataset. This problem is particularly relevant for plant datasets with limited variation and/or small samples sizes. Inspection of the loss and accuracy curves is a common way to detect overfitting in a CNN model, but it provides little insight into how the model could be improved. There are several reasons contributing to the overfitting of a CNN model; however, in this paper, we aim at explaining overfitting in a CNN classification model by analyzing the features learned at various depths of the model. We use three plant phenotyping datasets in our experimental studies. Our comparative analysis between the visualizations of the feature maps obtained from overfit and balanced models reveals that the image background often influences an overfit model’s behavior. Researchers with limited deep learning domain knowledge often attempt to build deeper layer models with the hope of improving per-formance. Using Guided Backpropagation, we show how the pairwise similarity matrix between the visualization of the features learned at different depths can be leveraged to pave a new way to potentially select a better CNN model by removing redundant layers.
Obedience to authority, cognitive and affective responses and leadership style in relation to a non-normative order: the Milgram experiment

The influence of the context on behavioral and emotional reactions to a war crime situation military cadets (N = 315) is analyzed. The study is based on Milgram’s experience and the tragedy of My Lai.It examines personal and peer obedience to an anti-normative order (asking participants whether they would obey an order to shoot unarmed civilians) in five vignettes or scenarios that reproduce Milgram’s conditions and MyLai scenario. This is an experimental between-within study of five scenarios by two conditions (Milgram, 1974). Personal and collective obedience of other military, emotional reactions and values of Schwartz (2012) were measured. Showing enhancement of self-bias it is reported that the pairs would be more likely to shoot than one would. Replicating Milgrams’s results, obedience is greater when the order is given directly by an authority, and lower when there is conflict between authorities and peers rebel. Confirming that identification with humanity and not just with the in-group may prompt respondents to reject an anti-normative order, values of transcendence of the self are associated with less obedience and congruent emotional reactions. Self-perceived transformational leadership was associated with positive emotions towards peer that disobey to fire. However a transformational style perceived in the superior was associated to positive emotions by respect to soldier who open fire, adding information on the potential dark side of this leadership style. The relevance of personal values, leadership style and affectivity in military context is discussed.
Updates in treatment of depression in children and adolescents

Purpose of review To examine recent evidence that informs the treatment of depression in children and adolescents. Recent findings There are no new leads in the prevention and early intervention of depression in children and adolescents. For acute treatment of major depressive disorder, talking therapies are moving increasingly to internet-based platforms. Family therapy may have a slight edge over individual psychotherapy in the short-term. Patients with severe depression with endogenous features have a more robust response to pharmacotherapy than do patients with mild-to-moderate depression. Findings in relation to reward sensitivity and changes in brain-derived neurotrophic factor levels contradict research conducted in adults, suggesting developmental differences in the mechanisms underlying depression. Ketamine infusion could have a role for adolescents with treatment refractory depression. There was no new evidence concerning relapse prevention. Summary Most new findings have been concerned with moderators and mediators of treatment.
Arm crank power and hyperammonemia in respones to L-Aspartic Acid supplementation

PURPOSE: To examine the effect of L-Aspartic Acid (L-Asp) supplementation on short-term arm-crank power (i.e. W·min) and exercise-induced hyperammonemia in two gender-specific investigations. METHODS: The male investigation (MALE) used nine highly-trained intercollegiate water polo players (WP) and nine moderately-trained college students (MOD); the female investigation (FEM) used 18 recreationally-trained college students. All exercised to exhaustion on two occasions following counterbalanced double-blind ingestion of 12.5g x 2 of L-Asp or placebo (P). The warmup phase began at 25 W·min and increased in 25 W·min stages every 30 seconds (constant 100 rpm). Performance began (i.e. the subjects turned the crank ‘as fast as possible for as long as possible’) with stage completion of rating of perceived exertion of 17 (6-20 scale) in MOD and FEM and at the 4-minute mark (200 W·min) for WP. Individual warm-up time in session 1 for MOD and FEM was used as warm-up time for session 2, respectively. Venous blood samples were drawn at 3 minutes post-exercise and assayed for blood NH3 [μM/L]. A 2 x 2 ANOVA with repeated measures was used for MALE, and a one-way ANOVA with repeated measures for FEM, with Tukey’s t-test for post hoc differences. RESULTS: Blood NH3 [μM/L] decreased with L-Asp (vs. P) in WP (241.0 ± 18.4 vs. 200.6 ± 20.5, p < 0.01) and MOD (219.7 ± 20.8 vs. 185.6 ± 27.5, p < 0.05). Performance power (W·min) with L-Asp improved (p < 0.01) in WP (270.2 ± 19.7 vs. 227.6 ± 14.3) but not (p > 0.05) in MOD (220.1 ± 9.1 vs. 204.4 ± 8.3). There was no treatment effect in FEM. CONCLUSION: These results suggest that 12.5 g x 2 QD of L-Asp reduces hyperammonemia and enhances short-term power in intercollegiate water polo players. Conversely, in MOD and FEM, 12.5 g x 2 QD of L-Asp does not attenuate exercise-induced hyperammonemia sufficiently to improve power.
Analysis of Blood Serum Bilirubin Concentration among Moderate Intensity Exercise Performers; A Randomized Control Trails

Purpose: A randomized control trails were conducted to evaluate the effects of moderate intensity exercise on blood serum bilirubin level. Methods: A participants of the study were divided in two groups i.e. experimental group (EG= 10) and control group (CG=10). 5ml blood were taken from all the subjects and each blood sample was marked with separate identification code. Twelve weeks exercise (moderate intensity exercise) protocols was employed on the selected subjects. The collected blood samples were tested and thus the collected results were processed through Statistical Package for Social Sciences (SPSS, Version, 24) by applying Mean and Standard deviation etc. Result: Based on analysis the researcher concluded that moderate intensity exercise has significant impact on Serum Bilirubin Concentration of the subjects. In addition significant difference was found in pre and post test result of experimental group (t4= 1.000, Sig. = .374 > α= 0.05).
Effects of extruded flaxseed and dietary rumen undegradable
protein on reproductive traits and the blood metabolites
in Baluchi ewes

The profitability of the sheep industry depends chiefly on conception and fecundity rates and can be increased by nutritional flushing of the ewes before mating. Flushing diets act via the hypothalamuspituitary-gonadal axis affecting the secretion of hormone including insulin, progesterone and estrogen (Scaramuzzi et al., 2006). Flaxseed is a good source of plant unsaturated fatty acids (FA) like α-linolenic acid (ALA; C18:3n-3, an omega-3 FA that constitutes 57% of the total fats in flax oil) and linoleic acid (LA: C18:2n-6, an omega-6 FA that constitutes 16% of the total fats in flax oil), and has been found to improve production and reproduction performance of animals in addition to enhancing the quality of animal products. Adding flaxseed to dairy cows’ diet changed the FA composition of blood plasma, follicular fluid and reproductive tissues (Zachut et al., 2010) as well as improved the metabolic and physiological status of the uterus, oocyte and embryo (Marei et al., 2009; Zachut et al., 2010). Wonnacott et al. (2010) reported that diets contrasting in n-3 and n-6 polyunsaturated fatty acids (PUFAs) were effective in altering the FA composition of both granuABSTRACT. This study was carried out to compare the effects of flushing diets with different levels of extruded flaxseed (EFS) and rumen undegradable protein (RUP) on reproductive performance and blood metabolites in Baluchi ewes. Forty-eight ewes (2–3 years old) were fed one of four different treatment diets: 1) no EFS + 20% RUP, 2) no EFS + 40% RUP, 3) 10% EFS + 20% RUP and 4) 10% EFS + 40% RUP. Blood concentrations of glucose (24 h after removing controlled internal drug release (CIDR) containing 0.3 g of progesterone) and cholesterol (24 h before removing CIDR) were different among the dietary treatments (P < 0.05). Blood urea-N (24 h after removing CIDR and 9 days after mating) was also impacted by the dietary treatments (P < 0.05). Higher blood estrogen levels occurred in response to EFS supplementation plus 40% RUP in comparison to non-EFS diets both 24 h before and after removing CIDR. Blood progesterone in ewes fed 10% EFS and 40% RUP diet was increased 9 days after mating in comparison to ewes fed non-EFS diets (P < 0.05). 10% EFS and 40% RUP in the diets of ewes improved the fecundity rate (P < 0.05). Follicle and corpus diameter (24 h after CIDR removal and 9 days after mating, respectively) were higher in EFS diets than the other groups (P < 0.05). So, the use of 10% EFS as a source of omega-3 fatty acids with 40% RUP in the flushing diet can improve ewe reproduction performance, which is important in the sheep industry, especially in developing countries. Received: 18 March 2021 Revised: 26 May 2021 Accepted: 17 June 2021