Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Deep Learning with Optimization Algorithm for Emotion Recognition in Social Networks

—Emotion recognition, or computers' ability to interpret people's emotional states, is a rapidly expanding topic with many life-improving applications. However, most image-based emotion recognition algorithms have flaws since people can disguise their emotions by changing their facial expressions. As a result, brain signals are being used to detect human emotions with increased precision. However, most proposed systems could do better because electroencephalogram (EEG) signals are challenging to classify using typical machine learning and deep learning methods. Human-computer interaction, recommendation systems, online learning, and data mining all benefit from emotion recognition in photos. However, there are challenges with removing irrelevant text aspects during emotion extraction. As a consequence, emotion prediction is inaccurate. This paper proposes Radial Basis Function Networks (RBFN) with Blue Monkey Optimization to address such challenges in human emotion recognition (BMO). The proposed RBFN-BMO detects faces on large-scale images before analyzing face landmarks to predict facial expressions for emotional acknowledgment. Patch cropping and neural networks comprise the two stages of the RBFN-BMO. Pre-processing, feature extraction, rating, and organizing are the four categories of the proposed model. In the ranking stage, appropriate features are extracted from the pre-processed information, the data are then classed, and accurate output is obtained from the classification phase. This study compares the results of the proposed RBFN-BMO algorithm to the previous state-of-the-art algorithms using publicly available datasets derived from the RBFN-BMO model. Furthermore, we demonstrated the efficacy of our framework in comparison to previous works. The results show that the projected method can progress the rate of emotion recognition on datasets of various sizes
SDG 4 on Quality Education in Colombia between 2019 and 2021

-Colombia ranks sixth among 24 countries in the Latin America and Caribbean region in the implementation of the 2030 Agenda. Despite its preferential position in the region, Colombia has not met any of the seventeen Sustainable Development Goals (SDGs). In this context of Sustainable Development, emphasis is placed on SDG 4 on Quality Education, from which the research question is formulated: what were Colombia's results in the fulfillment of SDG 4 concerning the countries of Latin America and the Caribbean between 2019 and 2021? To answer it, this research design was guided by the positivist stance and was implemented through the non-experimental method of the causal case study. In the SDG Index between 2019 and 2021, Colombia has maintained a moderate level of progress at a rate above 50% of the expected trend in SDG 4, but below the necessary trajectory for the fulfillment of SDG 4 in 2030. Therefore, Colombia has a critical lag in SDG 4, with a medium risk of not meeting this SDG by 2030.
Shine a (night)light: Decentralisation and economic development in Burkina Faso

Decentralisation has been one of the most prominent public sector reforms endorsed by international institutions. It has been initiated in a large number of developing economies, particularly in sub-Saharan Africa. To date, few studies propose a quasi-experimental evaluation of its capacity to contribute to local development, or do so but only focus on specific components (e.g. decentralised programmes). We suggest a unique assessment of decentralisation in its essence by exploiting the progressive implementation of the decentralisation agenda at the commune level in Burkina Faso starting in 1995. We use satellite information on night-time light density as a proxy for local development levels, which has the advantage of being measured and comparable over time and space. The communes that were decentralised first can be compared to the others after the reform relative to the pre-reform situation. The difference-in-difference approach includes commune fixed effects and inverse propensity score reweighting to account for time-varying differences across communes. We find a positive impact of decentralisation on the nightlight intensity trends of the early-decentralised communes. We provide extensive checks regarding the possibility of confounding dynamics associated with the communes that were decentralised first. We also provide suggestive evidence that decentralisation did not lift all boats: heterogeneity analyses show that the gains from decentralisation depend more on the ability to generate local own-source revenues than on the capacity to attract state transfers. Series
Una nueva especie de Stegotherium Ameghino (Xenarthra, Dasypodidae, Stegotheriini) del Mioceno de la provincia de Santa Cruz (Argentina)

Resumen. En esta contribucion se describe una nueva especie de Stegotheriini (Xenarthra, Dasypodidae), Stegotheriumtauberi, a base de materiales previamente asignados a Stegotheriumtessellatum Ameghino y otros recientemente descubiertos. Todos los materiales provienen de afloramientos de la Formacion Santa Cruz (Edad Santacrucense, Mioceno temprano tardio) ubicados entre los estuarios de los rios Coyle y Gallegos (provincia de Santa Cruz, Argentina). Los materiales reasignados son los restos mas completos para la tribu Stegotheriini. Asimismo, Stegotherium tauberi es el Stegotheriini de distribucion mas austral conocido. Abstract. A NEW SPECIES OF STEGOTHERIUM AMEGHINO (XENARTHRA, DASYPODIDAE, STEGOTHERIINI) FROM THE MIOCENE OF SANTA CRUZ PROVINCE (ARGENTINA). A new species of Stegotheriini (Xenarthra, Dasypodidae), Stegotheriumtauberi is described here based on materials previously attributed to Stegotheriumtessellatum Ameghino, as well as additional specimens discovered recently. All the specimens came from outcrops of the Santa Cruz Formation (Santacrucian Age, late early Miocene) located between the estuaries of the rivers Coyle and Gallegos (Santa Cruz Province, Argentina). The materials reassigned here represent the most complete for the tribe Stegotheriini. Moreover, Stegotheriumtauberi is the southernmost Stegotheriini discovered up to now.
Computationally Efficient Approximations Using Adaptive Weighting Coefficients for Solving Structural Optimization Problems

With rapid development of advanced manufacturing technologies and high demands for innovative lightweight constructions to mitigate the environmental and economic impacts, design optimization has attracted increasing attention in many engineering subjects, such as civil, structural, aerospace, automotive, and energy engineering. For nonconvex nonlinear constrained optimization problems with continuous variables, evaluations of the fitness and constraint functions by means of finite element simulations can be extremely expensive. To address this problem by algorithms with sufficient accuracy as well as less computational cost, an extended multipoint approximation method (EMAM) and an adaptive weighting-coefficient strategy are proposed to efficiently seek the optimum by the integration of metamodels with sequential quadratic programming (SQP). The developed EMAM stems from the principle of the polynomial approximation and assimilates the advantages of Taylor’s expansion for improving the suboptimal continuous solution. Results demonstrate the superiority of the proposed EMAM over other evolutionary algorithms (e.g., particle swarm optimization technique, firefly algorithm, genetic algorithm, metaheuristic methods, and other metamodeling techniques) in terms of the computational efficiency and accuracy by four well-established engineering problems. The developed EMAM reduces the number of simulations during the design phase and provides wealth of information for designers to effectively tailor the parameters for optimal solutions with computational efficiency in the simulation-based engineering optimization problems.
The Database and Information System Research Group at the University of Ulm

The University of Ulm was founded in 1967 with focus on medicine and natural sciences. In 1989 the University established two new faculties: Engineering Sciences and Computer Science. This enlargement took place within the framework of the so-called Science City Ulm. In a joint effort, the State of Baden-Württemberg, industrial companies, the University, and the City of Ulm successfully established a research and development infrastructure at or nearby the university campus consisting of the university's research labs, university-related research institutes like the Research Institute for Applied Knowledge Processing (FAW), and industrial research and development labs, especially a large research center of Daimler-Benz AG.
A new experiment to test parity symmetry in cold chiral molecules using vibrational spectroscopy

We present a brief review of our progress towards measuring parity violation in heavy-metal chiral complexes using mid-IR Ramsey interferometry. We discuss our progress addressing the main challenges, including the development of buffer-gas sources of slow, cold polyatomic molecules, and the frequency-stabilisation of quantum cascade lasers calibrated using primary frequency standards. We report investigations on achiral test species of which promising chiral derivatives have been synthesised.
Land use and sedimentation impacts on coral reefs in the Eastern Caribbean

Excess sedimentation is known to be detrimental to corals in small-scale experiments, yet we still have a limited understanding of effects of sediment on coral at regional scales. I conducted a systematic search of unmanipulated field studies that focused on the effects of sedimentation on corals. Only 10 studies provided spatially replicated data that could be included in a meta-analysis of the relationship between sedimentation rate and coral cover at a global scale. The overall effect size r was small (r = -0.11) and not significantly different from zero. Re-analyses of Caribbean studies suggest that shifts in coral community composition may occur with increased sedimentation in the absence of changes in total coral cover. Sediment characteristics and issues with field measurements of deposition rates may obscure coral responses to sedimentation. This study identifies research priorities to allow an assessment of the importance of increasing sedimentation as a regional cause of ecological change on coral reefs.
Conservazione ex situ e in situ della biodiversità vegetale dell’Area Marina Protetta di Capo Carbonara (Sardegna sud-orientale)

The first results of the project "Plant Biodiversity Conservation in the Protected Marine Area of Capo Carbonara (PMA)" are illustrated. This initiative, promoted by the CCB and the Municipality of Villasimius (CA), was funded for three-year period (2005/07) by the Ministry of the Environment and Territory protection. The project provides the study, monitoring and conservation of the coastal habitat, principally on all the Sites of Communitary Importance (pSIC) of the Villasimius Municipality territory, in order to plan a harmonic development between coastal and sea areas unifying the environmental protection secured by the PMA with that relatives to the Natura 2000 network. During the first year there were started ecological, floristic-sociological and chorological studies, in order to value the actual state of conservation and have a possibility to propose adequate measures for a sustainable management. In order to guarantee the effective plant biodiversity conservation, the following ex situ actions have been made: collection, study and conservation for a long time of the seed of selected species in the structures of the Sardinian Seed Bank (BG-SAR), according to international procedures and protocols. At the end of the first year the seed harvest was completed relatively to all the species with a greater risk of extinction. The project provides also actions directed to sensitize the population, local and non, on the thematics of the conservation and the sustainable development of the territory.
Assessing hydraulic modifications on vallisneria americana in Peoria Lake, Illinois - A pilot study using data sharing protocols to intergrate legacy models

Abstract : A pilot modeling study was conducted to assess the utility of implementing the Land Management System's (LMS) Level II Protocols for the efficient sharing of data among legacy models and GIS tools. The modeling was directed toward an investigation of potential population increase of the submersed macrophyte Vallisneila americana as a consequence of the construction of a hypothetical levee in the north-south direction in Upper Peoria Lake, Illinois. Numerical models were used to simulate the behavior of the lake in terms of hydrodyamics, sediment concentration, and aquatic plant biomass production for one growth season in 1996 from 1 May to 31 October. Two scenarios were modeled: 1) the current condition without levee, and 2) a hypothetical situation with a levee created in the north-south direction. The model results indicated that the presence of the levee would increase the maximum plant biomass of Vallisneria moderately to greatly in the northern part and along the shores of Upper Peoria Lake, but decreased it in the southern part and along the edges of Lower Peoria Lake. It also would allow the presence of considerably higher tuber numbers at the end of the growth season. The latter would enable a larger plant population to form the subsequent year. Thus, the results of this pilot study indicate that the creation of a levee in the north-south direction may lead to an increase in the Vallisneria population in Peoria Lake. It must be noted, however, that without the levee the water column remains turbid longer than in this modeled situation, because the predominant sediment class has a lower fall velocity and the sediment is resuspended in this wind-exposed lake. The integrated models and tools used in the study were readily available and merely required specific data in appropriate, model-specific formats. A Level II architecture was implemented that promoted efficient data exchange between models and GIS tools.
When Smoke Ran Like Water: Tales of Environmental Deception and the Battle Against Pollution

Background. Long-term vegetation changes in hyper-arid areas have long been neglected. Mortality, recruitment and change in populations of the ecologically and culturally important and drought persistent Acacia tortilis and Balanites aegyptiaca are therefore estimated in the Eastern Desert of Egypt, and are related to the primary agents of change, water conditions and human intervention. Methodology. A change analysis using high-resolution CORONA images (1965) in combination with field data (2003) is the basis for recruitment, mortality and change estimates. For assessing the influence of water conditions on patterns in recruitment and survival, different types of generalized linear models are tested. Conclusions. The overall trend in population size in that part of the Eastern Desert studied here is negative. At some sites this negative trend is alarming, because the reduction in mature trees is substantial (.50%) at the same time as recruitment is nearly absent. At a few sites there is a positive trend and better recruitment. Frequent observations of sprouting in saplings indicate that this is an important mechanism to increase their persistence. It is the establishment itself that seems to be the main challenge in the recruitment process. There are indications that hydrological variables and surface water in particular can explain some of the observed pattern in mortality, but our results indicate that direct human intervention, i.e., charcoal production, is the main cause of tree mortality in the Eastern Desert.
ACABELLA: Automated (Crypt)analysis of Attribute-Based Encryption Leveraging Linear Algebra

Attribute-based encryption (ABE) is a popular type of public-key encryption that enforces access control cryptographically, and has spurred the proposal of many use cases. To satisfy the requirements of the setting, tailor-made schemes are often introduced. However, designing secure schemes---as well as verifying that they are secure---is notoriously hard. Several of these schemes have turned out to be broken, making them dangerous to deploy in practice. To overcome these shortcomings, we introduce ACABELLA. ACABELLA simplifies generating and verifying security proofs for pairing-based ABE schemes. It consists of a framework for security proofs that are easy to verify manually and an automated tool that efficiently generates these security proofs. Creating such security proofs generally takes no more than a few seconds. The output is easy to understand, and the proofs can be verified manually. In particular, the verification of a security proof generated by ACABELLA boils down to performing simple linear algebra. The ACABELLA tool is open source and also available via a web interface. With its help, experts can simplify their proof process by verifying or refuting the security claims of their schemes and practitioners can get an assurance that the ABE scheme of their choice is secure.
Large-Scale Analysis of Wikipedia’s Link Structure and its Applications in Learning Path Construction

As the largest encyclopedia in history, Wikipedia represents an unprecedented unification of the world’s knowledge. Its internal links are an invaluable resource for understanding the relationships between concepts and information organization on the Web. However, such link structures are not thoroughly examined and barely visualized. In this paper, we take a graph-theoretic approach to investigate the link structure of English Wikipedia, providing an up-to-date snapshot of its knowledge organization, including degree distributions, strongly connected components, and disconnected subgraphs. To the best of our knowledge, we also perform the first k-core visualization over all of Wikipedia. Our results suggest Wikipedia is highly connected, with 90.05% of articles reachable from one another. Inbound links are found to be a better measure of an article’s importance than outbound links and demonstrate a more centralized mode of connection. Based on our observations, we propose a novel, end-to-end framework for automatically constructing learning paths, using Wikipedia links to recursively shortlist and rank prerequisite concepts for understanding new topics.
Rainfall Forecasting with Variational Autoencoders and LSTMs

In this paper we present a case study using a novel machine learning system for rainfall forecasting in a localized area over Colombia, South America. We explore a new forecasting approach inspired by established techniques used in computer vision and generative modeling to create a predictive model for precipitation maps. Using an ensemble made of a Variational Autoencoder and a stacked LSTM we were able to create a system which learns the spatial and temporal features of weather patterns in an integrated way, but also allows them to be measured and studied independently. Such a system introduces practical benefits in applications such as detection of rare weather patterns or analysis of anomalous events in addition to its regular forecasting capabilities.
TransScale: Combined-Approach Elasticity for Stream Processing in Fog Environments

Real-time data processing is a standard requirement in Fog Computing. Dynamically adapting data stream processing frameworks is an essential functionality to handle time-varying workloads efficiently and to optimize resource consumption. However, horizontal scaling alone, by adapting the parallelism and number of provisioned nodes, faces limits when available compute resources are scarce. We propose TransScale, a combined-approach auto-scaler that combines horizontal scaling to approximation computing, controlling it through transprecision computing. We design TransScale to make the approximation method transparent to the system and support context-specific requirements through QoS-driven re-configuration decisions. Based on the policy’s objective, we show that it can reduce re-configuration occurrences, optimize resource utilization and sustain high workloads in resource-constrained environments.
An Approach to Maintain the Balance between Exploitation and Exploration of the Evolutionary Process in Multi-objective Algorithms

Multi-objective optimization has been applied in many fields of science, including engineering, economics, finance, and logistics, where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. There are several techniques to solve multi-objective optimization problems in which evolutionary algorithms are often used because they simulate the principle of natural evolution and work on population. In evolutionary algorithms, to ensure the ability to find solutions globally and quickly find the optimal solution, we must maintain the exploratory and exploitative capacities of the evolution, which also means the exploration and the exploitation of algorithms. In multi-objective optimization, population quality in diversity and convergence is essential to achieve the best possible solution set. The analysis showed that the relationship between the properties of the population directed by evolution and the ability to explore and exploit the evolutionary process is quite clear. This research evaluated the population quality according to generations of the evolutionary process based on popular measures and adjusted the algorithm to create an equilibrium transformation of those metrics, thereby better maintaining the balance between the exploration and exploitation of the population. Experiments performed on the direction-based multi-objective evolutionary algorithm with typical benchmark sets showed that the results bring good performance both in terms of solution quality and balance of the evolutionary process.
Using BERT to Understand TikTok Users’ ADHD Discussion

The rapid rise in popularity of the social media platform TikTok has allowed information to reach a wider audience at a faster rate compared to traditional news outlets. With this growth, more young people are connected with each other, allowing discussion on issues of mental health, a once stigmatized topic, to grow among young people. However, not all information about mental health issues is done properly or accurately. A commonly discussed mental health issue on TikTok is Attention-Deficit/Hyperactivity Disorder, known as ADHD. In this paper, we briefly describe how ADHD is portrayed on TikTok. Then, we analyze ADHD discussion around themes of self-diagnosis, symptoms, and self-help through text analysis using BERT. We analyze the common responses to ADHD TikToks and set up a multi-label classifier to understand the general range of responses.