Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalization on Text-based Games using Structured Belief Representations

Text-based games are complex, interactive simulations where a player is asked to process the text describing the underlying state of the world to issue textual commands for advancing in a game. Playing these games can be formulated as acting in a partially observable Markov decision process (POMDP), as the player needs to issue actions to reach the goal, by optimizing rewards, given textual observations that may not fully describe the underlying state. Previous art has focused on developing agents to achieve high rewards or faster convergence to the optimal policy for single games. However, with the recent advances in reinforcement learning and representation learning for language we argue it is imperative to start looking for agents that can play a set of games drawn from a distribution of games rather than single games at a time. In this work, we will be looking at TextWorld [17] as a testbed for developing generalizable policies and benchmarking them against previous work. TextWorld is a sandbox environment for training and evaluating reinforcement learning agents on text-based games. TextWorld is suitable to check the generalizability of agents as it enables us to generate hundreds of unique games with varying levels of difficulties. Difficulty in text-based games are determined by a variety of factors like the number of locations in the environment and length of the optimal walkthrough to name a few. Playing text-based games requires skills in sequential decision making and processing language. In this thesis we evaluate the learnt control policies by training them on a set of games and then observing their scores on unseen games during the training phase. We check for the quality of the policies learnt, their ability to generalize on a distribution of games and their ability to transfer on games from different distributions. We define game distributions based on the difficulty level parameterized by the number of locations in the game, number of objects, etc. We propose generalizable and transferrable policies by extracting structured information from the raw textual observations describing the state. Additionally, our agents learn these policies in a purely data-driven fashion without using any handcrafted component – a common practice found in prior work. Specifically, we learn dynamic knowledge graphs from raw text to represent our agents’ beliefs. The dynamic belief graphs a) allow agents to extract relevant information from text observations and, b) act as memory to act optimally in the POMDP. Experiments on 500+ different games from the TextWorld suite show that our best agent outperforms previous baselines by an average of 24.2%.
Histogram validated object removal strategy using spatiogramic technique

— Image degradation usually occurs when certain image losses the stored information due to conversion or digitalization that decreases the quality of the image. Variation in brightness of color information in images is usually called as noise. Image usually subjects to noise of many types. Gaussian noises that occur in the image are concentrated. The principle of Fourier transformation is used so as to decompose the image into sine and cosine components for representing the image in particular frequency. Image histograms are an important technique for inspecting images that are used to spot the background and grey value range at a glance. The concepts of spatiogram with both first order and second order are to enhance the image with high definition clarity thereby removing the degradation in image. In addition, the images were pre-evaluated before image manipulation process .
Designing the Elements of a Fuzzy Hashing Scheme

The goal of “fuzzy hashing” is to identify near duplicates and similar documents using hashes or digests. We consider a range of design elements that can be used with fuzzy hashing schemes. We examine the criteria for evaluating fuzzy hashes, and develop an approach for optimising a scheme to respect those criteria. We apply this approach to select appropriate design elements and parameter values. This results in surprising choices, the preference of skip-ngrams for adversarial problem domains; and that 2 bit vectors are preferred to 1 bit vectors.
Four Decades of Canadian Earnings Inequality and Dynamics across Workers and Firms

This paper studies the evolution of individual earnings inequality and dynamics in Canada from 1983 to 2016 using tax files and administrative records. Linking individual tax filers to their employers (and rich administrative records on firms) beginning in 2001, it also documents the relationship between the earnings dynamics of workers and the size and growth of their employers. It highlights three main patterns over this period: First, with a few exceptions (sharp increase in top 1% and declining gender gap), Canada has experienced relatively modest changes in overall earnings inequality, volatility, and mobility between 1983 and 2016. Second, earnings inequality and the distribution of earnings growth vary substantially over the business cycle. Third, the earnings dynamics of individuals are strongly related to the size and employment growth of their employers.
Comparison between effects of yoga postures and foam rolling exercises for improving hamstring flexibility among young population

Introduction: Yoga practices enhance muscular strength and body flexibility. Self Myo-fascial release is a popular intervention used by both rehabilitation and fitness professional to enhance myo-fascial mobility. Common SMR tools include the foam roller. Aim: To compare effects on hamstring flexibility by foam rolling exercises and yoga postures using 90-90 straight leg raising test in young population. Procedure: A study was conducted on 100 students between age group of 18-25 for duration of 4 weeks. Group A and B each consisted of 50 subjects. Group A were given yoga postures and Group B were given foam roller. Results were analysed using 90-90 straight leg raising test and using goniometer at the end of the 4 week. Results: Data was analysed by using paired t test and unpaired t test. Within the groups(pre and post) paired t test was used. And in between the Groups unpaired t test was used. There was no dropout of the patents from the study. Conclusion: Yoga postures and foam roller are clinically significant for improving hamstring flexibility after hamstring flexibility has improved in both A and B Groups. We concluded that yoga postures may be more effective as compared to foam roller and can be given without any assistive devises but under observation by professional yoga instructor after proper evaluation of a patient by a physiotherapist.
Real-Time Selﬁe Video Stabilization Supplementary Material

t Frame index Mt Foreground mask Pt Background feature points Qt Correspondence of Pt−1 in frame t Ft Face vertices Q̂t Target coordinate of Qt v Coordinate of a pixel W (v;Qt, Q̂t) Rigid MLS warping function v̂ Warped coordinate of pixel v qi ith column of Qt wi MLS weight of qi,t to pixel v α MLS parameter c Weighted centroid of Qt ĉ Weighted centroid of Q̂t qi Vector from c to qi,t q̂i Vector from ĉ to q̂i Ai Transformation matrix of q̂i gj jth grid vertex G Grid vertices enclosing v D Bilinear weights of v with respect to G
A COMPARATIVE STUDY OF BANK SOUNDNESS METHODS TO CORPORATE VALUE (Commercial Banks Listed on Indonesia Stock Exchange For 2017-2018 Period))

In this study, the author wants to compare the bank soundness methods, namely the CAMELS and RGEC methods, by using corporate value as the performance indicator. This study uses 10 variables consisting of CAR, EAQ, OCOI, ROE, LDR, BETA which are the ratios of the CAMELS method, NPL, IRR, NIM, ROA, namely the ratios of the RGEC method, and Tobin’s Q ratio to calculate the corporate value. The population of this study is all commercial banks listed on the Indonesia Stock Exchange (IDX) during the 2017-2018 period. The sample was selected by using a purposive sampling method. This study has a total sample of 45 companies. The analysis technique in this study uses multiple regression The results of this study indicate that the OCOI and ROE ratios in the CAMELS method have a positive and significant effect on corporate value. In contrast, the NPL, IRR, and ROA ratios on the RGEC method also have a positive and significant effect on corporate value. From the results shown in this study, the author concludes that the RGEC method is more accurate and suitable for predicting the corporate value.
Comprehensive Analysis of Two Malicious Arabic-Language Twitter Campaigns

Fake malicious accounts are one of the primary causes of the deterioration of social network content quality. Numerous such accounts are generated by attackers to achieve multiple nefarious goals, including phishing, spamming, spoofing, and promotion. These practices pose significant challenges regarding the availability of credible data that reflect realworld social media interactions. This has led to the development of various methods and approaches to combat spammers on social media networks. Previous studies, however, have almost exclusively focused on studying and identifying English-language spam profiles, whereas the problem of malicious Arabic-language accounts remains under-addressed in the literature. In this paper, therefore, we conduct a comprehensive investigation of malicious Arabic-language campaigns on Twitter. The study involves analyzing the accounts of these campaigns from several perspectives, including their number, content, social interaction graphs, lifespans, and day-to-day activities. In addition to exposing their spamming tactics, we find that these spam accounts are more successful in avoiding Twitter suspensions that has been previously reported in the literature. Keyword—Social network security; social spammers; arab twitter users; malicious campaigns on twitter; data mining
Generalized Saliency Prediction for the Segmentation of Biomedical Images

: The recent interest for artiﬁcial intelligence (AI) can mostly be explained by the success of deep learning techniques especially in computer vision. State-of-the-art methods based on deep neural networks can obtain good results when applied to biomedical images for tasks such as pattern recognition, object detection and segmentation. Unfortunately, all these methods require a large amount of good-quality annotated training examples to provide eﬃcient prediction models. The speciﬁcity of biomedical applications, is that obtaining annotated is often complicated due to ethical issues about medical private information. Moreover, obtaining reliable annotations from specialists is often long and very expensive. Hence, this thesis addresses the problem of developing generalized saliency prediction models for image segmentation to deal with the problem of insuﬃcient training dataset. Our aim is to develop and assess in real-world application scenarios (e.g. tumor segmentation), several learning paradigms related to Human perception, transfer learning and data augmentation.
STEROID HORMONE REGULATION OF NEURONAL REMODELING IN DROSOPHILA

The steroid hormone 20-hydroxyecdysone acts through a heterodimeric receptor composed of two nuclear receptors, EcR and USP, to control neuronal remodeling during metamorphosis in Drosophila. The ecdysone receptor (EcR) gene encodes three isoforms (EcRA, EcR-B1, and EcR-B2) with stage and tissue specific expression during metamorphosis. Studies performed in the nervous system utilizing EcR-B mutants and dominant negative constructs for the three EcR isoforms suggest EcR-B is required for neuronal pruning while EcRA may be required for neuronal re-growth. We have tested the hypothesis that EcR-A is required for dendritic re-growth but not for initial dendritic out-growth or pruning in the Tv neurons of Drosophila by examining neuronal growth in EcR-A mutants. Of the two EcR-A mutant alleles examined, both alleles had initial dendritic out-growth and underwent dendritic pruning with the EcR-A allele exhibiting an overpruning phenotype suggesting that EcR-A is not required for the initiation of dendritic outgrowth or pruning. The EcR-A allele exhibited no dendritic regrowth whereas the EcR-A allele exhibited a variable phenotype of no re-growth observed to full re-growth, suggesting that functioning EcR-A is needed to regulate dendritic re-growth. Whole animal developmental comparisons, dissections of the central nervous system (CNS) and lethal analysis studies of the EcR-A mutants supported the hypotheses that the lack of dendritic re-growth seen in the EcR-A mutants is neither due to developmental delays nor early stage lethality. Our whole animal analysis of EcR-A mutants establishes that EcR-A is not required for initiation of dendritic pruning but is required for the regulation of dendritic re-growth of the Tv neurons in Drosophila and complements previous experiments performed using dominant negative constructs of EcR-A.
Comment on hess-2021-154 Anonymous Referee # 2 Referee comment on " Uncertainty Estimation with Deep Learning for Rainfall – Runoff Modelling

This paper focuses on the use of several −mostly new for hydrology− concepts and methods from the machine and deep learning fields for uncertainty quantification in rainfall-runoff modelling. Specifically, it presents a large-scale application of these concepts and methods under a new framework. This large-scale application can be used as a guide for future works wishing to apply these (or similar) concepts and methods.
A Model for Users' Profile Recognition based on their Behavior in Online Applications

In this article we propose a users' recognition model in online applications based on their behavior characteristics. For establishing the user profile recognition, there are defined the behavior characteristics in the case of online applications and these characteristics will be followed within some testing applications. Based on the identified characteristics, there are performed measurements for each user. The measurements are saved in a database and by calculation of an average, a profile is achieved. In the case of future user’ authentications in the application it is compared the behavior measured in the current session with the profile saved in the database. The purpose of this paper is to determine patterns of user behavior analysis and to compare the current behavior with the profile from the database using the Euclidean distance calculation formula.
Et4N[NO3(SnClPh3)2(SnPh3NO3)]: a trinuclear organostannate complex and related derivatives

Abstract Four new di- and triorganonitrato stannate complexes and related derivatives have been synthesized and characterized by infrared and Mössbauer spectroscopy. The structure of Et4N[NO3(SnClPh3)2(SnPh3NO3)] (1) has been determined by single-crystal X-ray diffraction analysis. In the trinuclear organostannate complex, each SnIV atom is five-coordinated and adopts a trigonal-bipyramidal trans-OXSnC3 (X=O or Cl) geometry. The overall coordination environment of the SnIV atoms is trans-octahedral for 2 and 3 or cis-trigonal-bipyramidal for 4. In the related derivatives, the NO3- anions behave as monodentate ligands.
Meso- and Macroporous Co3O4 Nanorods for Effective VOC Gas Sensors

The synthesis of crystalline nanorod metal oxides with porous structure is an important issue in gas sensor technology, which aims to increase their sensitivity and stability. In addition, the development of a facile method to synthesize large yields of nanorods is a key factor in reducing the cost of products in thick film gas sensor technology. Large yields of crystalline meso-/macroporous Co3O4 nanorods are fabricated using facile hydrothermal method from CoCl2 and urea precursors, without using any surfactants or template-directing agents. The gas-sensing properties of synthesized nanorods are tested to volatile organic compounds (VOCs) such as benzene, acetone, and ethanol. Results reveal that crystalline meso- and macroporous Co3O4 nanorods have the highest sensitivity to acetone with a fast response and a recovery time of one minute. Moreover, the sensing properties of crystalline meso-/macroporous Co3O4 nanorods depend strongly on carrier (reference) gases, whereas preadsorbed oxygen plays an impo.
Novel antimicrobial films based on ethylene-vinyl alcohol copolymers for food packaging application

This PhD dissertation thesis has been focus on the development and characterization of antimicrobial packaging films based on the incorporation in the polymer matrix or on the attachment to the film surface of naturally occurring antimicrobial compounds with the purpose of inhibiting the proliferation of microorganisms and extend the microbiological shelf life of packaged food products. The studied active films are based on the use of ethylene vinyl copolymers (EVOH) containing 29% (EVOH29) or 44% (EVOH44) molar percentage of ethylene as polymeric vehicle for the incorporation of several antimicrobial compounds -oregano essential oil (OEO), citral, ethyl lauroyl arginate (LAE), epsilon-polylysine (EPL), green tea extract (GTE) and lysozyme. These antimicrobial agents have been incorporated in the film-forming solution or immobilized to the film surface by covalent bonding. Prior to the preparation of the active films, the antimicrobial activity of the selected compounds against selected microorganism was demonstrated, confirming that they could be good candidates to be used as preservatives for active food packaging applications, and an alternative to synthetic additives. The effect of the incorporation of the antimicrobial agents on relevant functional properties of the developed EVOH films was studied. In general, the polymer properties as materials for food packaging were not relevantly affected. In order to evaluate the potential of EVOH matrices as sustain release systems of active compounds, the release kinetics of the active compounds from the film to different media was evaluated; for that the agent release rate and extend into food simulants was monitored, and it was concluded that the agent concentration, release temperature, type of EVOH, interaction of EVOH with the food simulant, and the solubility of the active compound in the release media were the main controlling factors. EVOH matrices have also shown good properties to be used for the attachment of active molecules. In this regard, lysozyme was successfully immobilized on the film surface of EVOH. Several experiments were conducted to determine the antimicrobial properties of the resulting films in vitro against different microorganisms responsible for foodborne illness and in vivo with real foods ? minimally-process salad, infant milk, surimi sticks and chicken stock- to enhance their preservation. All the materials presented a strong in vitro antimicrobial activity. Although the results obtained through in vivo tests showed activity reductions caused by food matrix effects, all materials presented significant microbial inhibition and, therefore, great potential to be used in the design of active food packaging. They can be applied as an inner coating of the packaging structure, releasing the active agent or acting by direct contact, producing a great protection against contamination with a prolongation of the microbiological food shelf life.
Green and Efficient Synthesis, Characterization and Anti- Bacterial Activity of Copper (II) Complexes with Unsymmetrical Bidentate Schiff Base Ligands

A rapid, efficient, clean and environmentally benign exclusive synthesis of Schiff bases as new ligands and their complexes with Cu(II) have been developed using condensation of salicylaldehyde and amoxicillin (L 1 ), cephalexin (L 2 ), sulphamethoxazole (L 3 ) and trimethoprim (L 4 ) efficiently in analcoholic suspension medium using alkali catalyst with excellent yields under microwaves irradiation. This method provides several advantages such as environmental friendliness, simple work-up procedure, short reaction times, non-hazardous and excellent yield of products.The results are compared with conventional methods for their yield and reaction time. The Schiff base ligands and the complexes were characterized by micro-analytical, thermo-gravimetric, magnetic and spectroscopic studies.Allthe Schiff bases were bidentate (NO donor) ligands. Salicylidenesulphamethoxazole-Cu(II) monohydrate was five co-ordinate whereas all other complexes were found to be six co-ordinate dihydrates and ML2 (1:2 (metal: ligand) ratio) type. The complexes are coloured and stable in air. All the complexes under investigation possess antibacterial activity. The antibacterial activity showed the following trend: Cu(II)-complexes ˃ Schiff base ligands ˃ parent drugs.
Unique DNA-barcoded aerosol test particles for studying aerosol transport

ABSTRACT Data are presented for the first use of novel DNA-barcoded aerosol test particles that have been developed to track the fate of airborne contaminants in populated environments. Until DNATrax (DNA Tagged Reagents for Aerosol eXperiments) particles were developed, there was no way to rapidly validate air transport models with realistic particles in the respirable range of 1–10 μm in diameter. The DNATrax particles, developed at Lawrence Livermore National Laboratory (LLNL) and tested with the assistance of the Pentagon Force Protection Agency, are the first safe and effective materials for aerosol transport studies that are identified by DNA molecules. The use of unique synthetic DNA barcodes overcomes the challenges of discerning the test material from pre-existing environmental or background contaminants (either naturally occurring or previously released). The DNATrax particle properties are demonstrated to have appropriate size range (approximately 1–4.5 μm in diameter) to accurately simulate bacterial spore transport. Here, we describe details of the first field test of the DNATrax aerosol test particles in a large indoor facility. Copyright © 2016 American Association for Aerosol Research
Distribution of Pb, Cr, Cu and Zn in the marine-coastal region of Zanzibar (Tanzanian archipelago, East Africa)

ABSTRACT In Tanzania, a large amount of domestic and industrial wastes are daily released to the aquatic environment without prior treatment. The present paper establishes the status of distribution and environmental implications of heavy metals in water, sediment, suspended particulate matter and biota (Anadara antiquata) from two marine coasts in Zanzibar, Tanzania. These metals are derived from the industrial areas through rivers and storm water outflows. Heavy metal concentrations in the collected samples were determined using atomic absorption spectrophotometry. Concentrations were significantly higher in the Malindi compared to the Fumba coast (ANOVA, p = .001). Calculation of metal partition coefficients shows that the relative importance of the particulate and the water phases varies in response to water parameters and suspended solid content, but that most elements achieve a conditional equilibrium in these coasts. Higher metal concentrations found in Malindi suggest a pollution effect related to anthropogenic activities. Mean metal burdens in the tissues of A. antiquata from both Malindi and Fumba coasts were significantly higher than in other compartments analysed (ANOVA, p <.05). Maximum values of contamination factor and contamination degree for metals were noticed for sediments from both Malindi and Fumba coasts. It is imperative to understand that metal concentrations of coastal environments depend not only on industrial and household waste inputs but also on the geochemical composition of the area.