Miracle-dz/UniFAR-FTUs huggingface dataset:
Auto-Encoder based Deep Representation Model for Image Anomaly Detection

Image anomaly detection is to distinguish a small portion of images that are different from the user-defined normal ones. In this work, we focus on auto-encoders based anomaly detection models, which assess the probability of anomaly by measuring reconstruction errors. One of the critical steps in image anomaly detection is to extract robust and distinguishable representations that could separate abnormal patterns from normal ones. However, current auto-encoder based methods fail to extract such distinguishable representations because their optimization objectives are not tailored for this specific task. Besides, the architectures of those models are unable to capture features that are robust to irrelevant distortions but sensitive to abnormal patterns. In this work, two auto-encoder based models are proposed to address the aforementioned issues in optimization objectives and model architectures, respectively. The first model learns to extract distinct representations for abnormal patterns by imposing sparse regularizations on the latent space during the optimization process. This sparse regularization makes the extracted abnormal features unable to be represented as sparse as the normal ones. The second model detects abnormal patterns using Asymmetric Convolution Blocks (ACB), which strengthens the crisscross part of the convolutional kernel, making the extracted features less sensitive to geometric transformations. The experimental results demonstrate the superiority of both proposed models over other auto-encoder based anomaly detection models on popular datasets. The proposed methods could also be easily incorporated into most anomaly detection methods in a plugand-play manner.
Survey on Securing Data using Homomorphic Encryption in Cloud Computing

-Cloud computing is technique which has become today’s hottest research area due to its ability to reduce the costs associated with computing. In today’s Internet world, it is most interesting and enticing technology which is offering the services to various users on demand over network from different location and using range of devices. Since Cloud Computing stores the data and propagate resources in the open environment, security has become the main obstacle which is hampering the deployment of Cloud environments. So To ensure the security of data, we proposed a method which uses Multilayer Security for securing Data using Homomorphic Encryption in Cloud Computing. Homomorphic encryption is a form of encryption that perform computations on ciphertext thus generating an encrypted result which, when decrypted, matches the result of operations performed on the plaintext. This System helps to secure Data for various cloud users.
Deep Q-Learning Framework for Improving Spectral Efficiency in D2D Communication

—Device-to-device (D2D) communication is expected to play a big role in 5G, in order to enable new applications in the mobile system such as vehicular-communications (V2X) and internet of things (IoT). This should increase the bandwidth demand, which makes higher spectral efﬁciency ever more desirable. This paper proposes a Deep Q-Learning power allocation framework for maximizing spectral efﬁciency in D2D communication, in underlay mode, while satisfying the mobile user’s quality of service (QoS) requirements.
The Effects of Inquiry-Based Learning and Student Achievement The Effects of Inquiry-Based Learning and Student Achievement in the Science Classroom in the Science Classroom

Research has demonstrated inquiry-based learning (IBL) engages students in the processes of scientific discovery and can make science relevant toward their real-world concerns. However, in most science classrooms, teachers still use traditional learning, or direct methods of instruction for scientific terminology and other types of discrete knowledge students need to master for standardized testing. Existing research and studies have identified the various impacts of inquiry-based learning in the science classroom and its relationship between student achievement, student motivation and long-term knowledge retention. Research has shown implementation of inquiry-based learning has a positive and direct relationship to student achievement. Planning and developing inquiry-based learning lessons can be time consuming and resources can be limited. However, students can make direct connections and experience deeper learning through hands-on and experiential learning which has an overall positive benefit for student achievement, knowledge recall and retention. This research study examined existing studies and research to understand the relationship between inquiry-based learning and student achievement and success in the science classroom and the varying benefits and methods to implement inquiry-based learning in the classroom.
Technical Efficiency of Smallholder Farmers Wheat Production: The Case of Debra Libanos District, Oromia National Regional State, Ethiopia

This study aimed to examine the technical efficiency of smallholder farmers in wheat production in the Debra Libanos district. Two stages sampling technique was used to select 150 sample farmers to collect primary data using crosssectional data of 2018/19 production season. Data analysis was carried out using descriptive statistics like mean, minimum, maximum, frequency and standard deviation and econometric models such as stochastic production frontier and two-limit Tobit regression models to estimate level and identify factors affecting technical efficiency respectively. The estimated stochastic production frontier model indicated that land, labor, seed and chemical fertilizer were positively and significantly affects farmers’ wheat production level. The study indicated that the average technical efficiency level of wheat producing farmers was 78.5% implying that there was technical efficiency variation among smallholder farmers in the study area. In other words, the average wheat yield loss due to technical efficiency variation was 5.13 qt per ha. Therefore, these results implied that there is a room to increase the efficiency of wheat production in study area. Moreover, the two-limit Tobit regression model results showed that age, family size, livestock size, frequency of extension contact and frequency of ploughing had positive and significant effect on technical efficiency. Hence, attention should be given to improve the efficiency level of those less efficient farmers by adopting the practices of relatively efficient farmers in the study area. Besides this, policies and strategies of the government should be directed towards the above mentioned determinants.
Recognition of the theme “Social Responsibility” in Higher Education Institutions. Analysis of undergraduate courses in Business Administration in the state of Rio de Janeiro

Luiz Antonio Jucá Serao SEBRAE Nacional Serviço Brasileiro de Apoio às Micro e Pequenas Empresas, Rio de Janeiro – RJ, Brazil Fátima Bayma de Oliveira Fundação Getulio Vargas / Escola Brasileira de Administração Pública e de Empresas, Rio de Janeiro – RJ, Brazil Mirian Albert Pires Universidade Federal do Espírito Santo / Centro de Ciências Jurídicas e Econômicas – CCJE; Departamento de Ciências Contábeis DCC, Vitória ES, Brazil Anderson de Souza Sant’Anna Fundação Dom Cabral, Nova Lima – MG, Brazil Abstract This paper aims to analyze the recognition of the theme “social responsibility” in undergraduate courses in Business Administration offered by higher education institutions (HEIs) in the State of Rio de Janeiro, from the viewpoint of course coordinators, students and student organizations that undertake social practices. A qualitative survey was conducted by applying 14 (fourteen) in-depth interviews with the coordinators, directors, professors and representatives of students social organizations. In addition, 15 (fifteen) questionnaires were completed by students who worked in the surveyed social organizations. The empirical results reveal that there is still a long way to go for Social Responsibility to be effectively included in the mandatory study program of the surveyed courses in Business Administration. In most cases, disciplines addressing social responsibility are optional (in private HEIs) or non-existent (in public HEI). Nevertheless, there is a notable increase in the number of students working in student organizations and engaged in social practices, which represent an opportunity for students to get work experience in the subject. However, this is still a small percentage, considering the amount of students registered on undergraduate courses in Business Administration. The findings also reveal that the main causes of this poor involvement of students in social entities are associated with bureaucracy as well as with the low recognition HEI give to social organizations.
Collaborative and Cooperative Learning through Social Media

Social media has influenced all fields including education. Literally, internet is replacing the traditional library as it a vital source of information. Employers also seek graduates with IT skills and are able to work with social media and internet in general and especially in management discipline. With learners’ perceptions, the aim hereto is to investigate and provide a critical review of the social media usage for collaborative and cooperative learning among graduates with focus on management students. Adopting mixed methods, combination of theoretical and empirical findings the data collection is through interviews of students.
Model-based Credit Assignment for Model-free Deep Reinforcement Learning

—The combination of model-free reinforcement learning method and deep neural network brings signiﬁcant progress, which makes the so-called deep reinforcement learning is able to solve a lot of real-world problems However, its performance is not ideal for complex tasks and the computation cost is usually high. These shortcomings limit its further application. In this paper, we propose a novel framework that utilizes the model-based method to assign credits for hundreds of thousands of non-terminal state-action pairs, in order to overcome those drawbacks. Speciﬁcally, we ﬁrst abstract the states and actions of the original problem into a compact representation, which reduces the problem to a tractable size. Then, we solve the abstract problem to obtain the optimal value function, which is the expected returns of future rewards. Finally, we use the derived value function to assign credits for state-action pairs of the original problem. We conduct extensive experiments on three different scenarios, from small to large, from single-task to multi-task. The experiment results have demonstrated that our agent outperforms previous state-of-the-art methods both on the ﬁnal performance and the training efﬁciency with a large margin. Based on the novel framework, we trained an agent to participate in an online video game competition and achieved the 2nd place in the ﬁnal.
Low Resolution Face Recognition Using Generative Adversarial Network (GAN)

Face recognition has a lot of use on smartphone authentication, finding people, etc. Nowadays, face recognition with a constrained environment has achieved very good performance on accuracy. However, the accuracy of existing face recognition methods will gradually decrease when using a dataset with an unconstrained environment. Face image with an unconstrained environment is usually taken from a surveillance camera. In general, surveillance cameras will be placed on the corner of a room or even on the street. So, the image resolution will be low. Low-resolution image will cause the face very hard to be recognized and the accuracy will eventually decrease. That is the main reason why increasing the accuracy of the Low-Resolution Face Recognition (LRFR) problem is still challenging. This research aimed to solve the Low-Resolution Face Recognition (LRFR) problem. The datasets are YouTube Faces Database (YTF) and Labelled Faces in The Wild (LFW). In this research, face image resolution would be decreased using bicubic linear and became the low-resolution image data. Then super resolution methods as the preprocessing step would increase the image resolution. Super resolution methods used in this research are Super resolution GAN (SRGAN) [1] and Enhanced Super resolution GAN (ESRGAN) [2]. These methods would be compared to reach a better accuracy on solving LRFR problem. After increased the image resolution, the image would be recognized using FaceNet. This research concluded that using super resolution as the preprocessing step for LRFR problem has achieved a higher accuracy compared to [3]. The highest accuracy achieved by using ESRGAN as the preprocessing and FaceNet for face recognition with accuracy of 98.96 % and Validation rate 96.757 %.
DO NATURAL DISASTERS AFFECT ECONOMIC GROWTH

This paper investigates the relationship between natural disasters and economic growth in Bangladesh from 1960 to 2014. Moreover, the paper focuses on the long-run impact of natural disasters on economic growth in Bangladesh. As control variables, this study includes capital stock, trade openness, and population growth. In addition, the actual path and counterfactual path will be included to observe the path of economic growth. The autoregressive distributed lag (ARDL) model will be applied to examine the relationship between natural disasters and economic growth. The results of natural disasters have a negative relationship on economic growth in the long-run. Surprisingly, the result of the error correction term for the counterfactual path is significant at -1.13. This suggested an overcorrection of 13% in Bangladesh. The government of Bangladesh should reserve money to reconstruct their economic growth in the event that natural disasters occur.
A Multistage Stochastic Inventory Model in the Presence of Commodity Futures Market

The proliferation of commodity markets in recent years has provided manufacturers an extra flexibility to deal with price uncertainty in raw material procurement. Although manufacturers have been using commodity derivative instruments for risk hedging for decades, such hedging programs are conducted purely as a financial exercise and are usually separated from manufacturers’ inventory planning and production activities. This paper describes the optimal inventory planning problem of make-to-order manufacturers in the presence of commodity futures market. By purchasing in the spot market and hedging in the commodity futures market, such a manufacturer will aim at minimizing the overall procurement cost while fulfilling multistage stochastic customer demand. Schwartz’s two-factor model is applied to explore the stochastic behaviour of commodity prices, and the inventory planning problem is formulated as a multistage stochastic program. Numerical simulations of the Schwartz’s two factor model and the proposed multistage stochastic inventory model are also presented in this paper.
First European Conference on Earthquake Engineering and Seismology

During November 2004 – June 2005 a digital seismological network was deployed in the eastern part of central Greek mainland, in an area seismically active in the 20 century, particularly in the decade of fifties. The strongest earthquake (M=7.0) occurred in 1954, while the last strong one in 1980 (M=6.5). In total 18 Reftek digital loggers (both 72A–07 and R–130) were installed assembled with fifteen guralp CMG40T broadband and three Le_1Hz seismometers. The average spacing between stations was of the order of 20 km to ensure earthquake depth accuracy. Local earthquakes with P– and S– arrivals at four or more stations were located using HYPOINVERSE computer program. Data analysis using the Double Difference technique did not change considerably the spatial distribution of the earthquake foci. The best–recorded earthquakes were used to define a reasonable crustal structure. Lateral variations of the crustal model taken into account, calculating time delays for each station. By this way, earthquakes inside the network or close to its boundaries were located with high accuracy in both the epicenter and focal depth. In addition, focal mechanisms of earthquakes with proper azimuthal coverage were computed. Seismicity covers most of the area and is distributed mainly in clusters along active structures. A magnitude M=3.9 earthquake, was the largest local one recorded by this network (5 December 2004, 17:58 UTC), close to the focal area of the 1980 strong earthquake. Several cross sections striking normal to the trend of the clusters of the epicenters reveal the geometry of the active structures as well as the width of the seismogenic layer. Most of the focal mechanisms exhibit normal faulting and were used along with the microseismicity foci in the cross sections for the definition of the properties of the faults that activated during the experiment.
Neutron powder diffraction studies of two spin transition FeII complexes under pressure

The pressure dependences of the lattice parameters of two spin transition compounds have been derived from neutron powder diffraction measurements at ambient temperature. The study of Fe(PM–AzA)2(NCS)2 [PM is N-20-pyridylmethylene and AzA is 4-(phenylazo)aniline] has been used to validate this new type of investigation of spin crossover compounds, and the study of Fe(PM–BiA)2(NCS)2 (BiA is 4-aminobiphenyl) has allowed the atypical spin crossover behaviour of this compound under pressure to be explained. In addition, this complex exhibits a pressure-induced structural transition with an associated symmetry change, inducing the transformation of Fe(PM–BiA)2-(NCS)2 into a different polymorph that avoids a first-order spin transition in favour of continuous transition.
The auditory Mismatch Negativity reflects accelerated aging in adults with Down's Syndrome

Down's Syndrome (DS) is associated with premature and accelerated ageing and a propensity for early-onset Alzheimer's disease (AD). The early symptoms of dementia in people with DS may reflect frontal lobe vulnerability to amyloid deposition. The Mismatch Negativity (MMN) is a frontocentral component elicited by auditory violations of expected sensory input and it reflects sensory memory and automatic attention switching. In the typically developing (TD) population, the MMN response has been found to decrease with age. In the cross-sectional phase of this study the MMN was used to investigate the premature neurological ageing hypothesis of DS. In the longitudinal phase, we evaluated the MMN as a potential predictor of cognitive decline. The study found that age predicted MMN amplitude in DS but not in those who are TD, showing that the MMN reflects accelerated ageing in DS. However, a follow-up of 34 adults with DS found that neither amplitude nor latency of the MMN predicted cognitive decline one year later
Early IFNÎ² secretion determines variable downstream IL-12p70 responses upon TLR4 activation in health and disease.

The IL-12 family of cytokines comprises the only heterodimeric cytokines mediating diverse functional effects. We previously observed a bi-modal IL-12p70 response to LPS in healthy donors of the Milieu Interieur cohort. Herein, we demonstrate that IFN{beta} expression serves as an upstream determinant of variable IL-12p70 production. Integrative modelling of proteomic, genetic, epigenomic and cellular data confirmed IFN{beta} as key for regulation of LPS induced IL12A and IL-12p70 variability. The clinical relevance was supported by reduced and variable IL-12p70 responses in individuals infected with the hepatitis C virus (HCV), and findings that IFN-based therapy for HCV is more likely to fail in those patients with dysregulated pre-treatment IL-12p70 responses. In sum, our systems immunology approach has defined a better understanding of IL-12p70 and IFN{beta} in healthy and infected persons, providing insights into how common genetic and epigenetic variation may impact immune responses to bacterial infection in health and disease.
Social disparities in the first wave of COVID-19 infections in Germany: A county-scale explainable machine learning approach

Background Little is known about factors correlated with this geographic spread of the first wave of Covid-19 infections in Germany. Given the lack of individual-level socioeconomic information on COVID-19 cases, we resorted to an ecological study design, exploring regional correlates of COVID-19 diagnoses. Data and Method We used data from the Robert-Koch-Institute on COVID-19 diagnoses by sex, age (age groups: 0-4, 5-14, 15-34, 35-59, 60-79, 80+), county (NUTS3 region) differentiating five periods (initial phase: through 15 March; 1st lockdown period: 16 March to 31 March; 2nd lockdown period: from 1 April to 15 April; easing period: 16 April to 30 April; post-lockdown period: 1 May through 23 July). For each period we calculated age-standardized incidence of COVID-19 diagnoses on the county level, using the German age distribution from the year 2018. We characterized the regions by macro variables in nine domains: "Demography", "Employment", "Politics, religion, and education", "Income", "Settlement structure and environment", "Health care", "(structural) Poverty", "Interrelationship with other regions", and "Geography". We trained gradient boosting models to predict the age-standardized incidence rates with the macro structures of the counties, and used SHAP values to characterize the 20 most prominent features in terms of negative/positive correlations with the outcome variable. Results The change in the age-standardized incidence rates over time is reflected in the changing importance of features as indicated by the mean SHAP values for the five periods. The first Covid-19 wave started as a disease in wealthy rural counties in southern Germany, and ventured into poorer urban and agricultural counties during the course of the first wave. The negative social gradient became more pronounced from the 2nd lockdown period onwards, when wealthy counties appeared to be better protected. Population density per se does not appear to be a risk factor, and only in the post-lockdown period did connectedness become an important regional characteristic correlated with higher infections. Features related to economic and educational characteristics of the young population in a county played an important role at the beginning of the pandemic up to the 2nd lockdown phase, as did features related to the population living in nursing homes; those related to international migration and a large proportion of foreigners living in a county became important in the post-lockdown period. Discussion In the absence of individual level data, explainable machine learning methods based on regional data may help to better understand the changing nature of the drivers of the pandemic. High mobility of high SES groups may drive the pandemic at the beginning of waves, while mitigation measures and beliefs about the seriousness of the pandemic as well as the compliance with mitigation measures put lower SES groups at higher risks later on.