Miracle-dz/UniFAR-FTUs huggingface dataset:
Selecting texture resolution using a task‐specific visibility metric

In real‐time rendering, the appearance of scenes is greatly affected by the quality and resolution of the textures used for image synthesis. At the same time, the size of textures determines the performance and the memory requirements of rendering. As a result, finding the optimal texture resolution is critical, but also a non‐trivial task since the visibility of texture imperfections depends on underlying geometry, illumination, interactions between several texture maps, and viewing positions. Ideally, we would like to automate the task with a visibility metric, which could predict the optimal texture resolution. To maximize the performance of such a metric, it should be trained on a given task. This, however, requires sufficient user data which is often difficult to obtain. To address this problem, we develop a procedure for training an image visibility metric for a specific task while reducing the effort required to collect new data. The procedure involves generating a large dataset using an existing visibility metric followed by refining that dataset with the help of an efficient perceptual experiment. Then, such a refined dataset is used to retune the metric. This way, we augment sparse perceptual data to a large number of per‐pixel annotated visibility maps which serve as the training data for application‐specific visibility metrics. While our approach is general and can be potentially applied for different image distortions, we demonstrate an application in a game‐engine where we optimize the resolution of various textures, such as albedo and normal maps.
Fish catch patterns and estimation of maximum sustainable yield for sustainable fish catch in the fishing grounds off Gulf of Mannar, India

Fish catch patterns with seasonal variation of the three traditional fishing grounds off Tiruchendur coast, Gulf of Mannar was studied for the period of two years (2009-10). The main objective of the study was to estimate the maximum sustainable yield (MSY) for the sustainable harvesting. A total of 31 commercially important fishes were caught from the sampling boats, which included 9 species of pelagic, 16 species of demersal, 4 species of crustaceans and 2 species were available in mid water and benthic region. A total of 15395 kg and 15013.5 kg of fish catch were recorded at station 1 and 2 respectively. Comparatively, more fish catch about 17073.65 kg was recorded at station 3 during the study. Fish catch and CPUE showed a similar trend during the study, which indicates that fishing effort has positive impact on catch. The predicted maximum sustainable yield (MSY) for station 1 was about 665.7kg with optimum effort of 10.6 kg/h. In station 2, MSY was about 699 kg with optimum effort of 9 kg/h. At station 3, the MSY was 976 kg with an optimum effort of 11.16kg/h. The results of Schaefer model revealed that the fishery has already attained the sustainable level and also suggested that the current levels of effort are optimal.
Drivers of the Distribution of Ecological Species Groups in Temperate Deciduous Managed Forests in the Western Carpathian Mountains

Managed broadleaf deciduous forests are an important type of forest vegetation in Central Europe, also in the Western Carpathians. These forests are both economically and environmentally valuable. However, little is known about ecological species groups and the inter-specific associations of dominant species in temperate deciduous managed forests in Central Europe. Since the forest stands are in a managed landscape, they are not consistent with the traditionally recognized and used vegetation associations in the Western Carpathians. For these reasons our research contributes to understanding the consequences of broadleaf deciduous forest management. The aim of this research was the determination of ecological species groups and an investigation into the main environmental drivers, in order to explain the distribution of ecological species groups. The numerical TWINSPAN classification was selected to distribute 146 relevés to the five ecological species groups. Of these, 77 relevés were divided into two groups with Fagus sylvatica dominant, while 63 relevés were Quercus petraea dominant. Carpinus betulus, Tilia cordata and Fraxinus excelsior were dominant in 19 relevés. Constrained Analysis of Principal Components was used to explain the vegetation–environment relationship on three transects in the Male Karpaty Mountains. Altitude, pH, Ca, C, K and Mg were selected as the significant environmental drivers responsible for a large part of the species group variability (31.8%). The main requirement for sustainable forest management is knowledge of the vegetation–environment relationship and this research was focused on gaining such understanding. This knowledge can be used as a decision support tool for sustainable management in managed deciduous forests.
A Comparitive Analysis of EAP Authentication Mechanism for WLAN

— In recent years, WLANs have been developing rapidly and are increasingly being used in many applications. The extensive application of WLAN has been using an authentication framework widely called as Extensible Authentication Protocol (EAP). The requirements for EAP methods (i.e. Authentication mechanisms built on EAP) in WLAN authentication have been defined in RFC 4017 are issues also increasingly receiving widespread attention. To achieve user efficiency and robust security, lightweight computation and forward secrecy, not included in RFC 4017, are also desired in WLAN authentication. However, all EAP methods and authentication protocols designed for WLANs so far do not satisfy all of the above properties. With detailed analysis of all EAP Methods and authentication protocols designed for WLANs, this article pointed out properties of all EAP method.
Transformer based Grapheme-to-Phoneme Conversion

Attention mechanism is one of the most successful techniques in deep learning based Natural Language Processing (NLP). The transformer network architecture is completely based on attention mechanisms, and it outperforms sequence-to-sequence models in neural machine translation without recurrent and convolutional layers. Grapheme-to-phoneme (G2P) conversion is a task of converting letters (grapheme sequence) to their pronunciations (phoneme sequence). It plays a significant role in text-to-speech (TTS) and automatic speech recognition (ASR) systems. In this paper, we investigate the application of transformer architecture to G2P conversion and compare its performance with recurrent and convolutional neural network based approaches. Phoneme and word error rates are evaluated on the CMUDict dataset for US English and the NetTalk dataset. The results show that transformer based G2P outperforms the convolutional-based approach in terms of word error rate and our results significantly exceeded previous recurrent approaches (without attention) regarding word and phoneme error rates on both datasets. Furthermore, the size of the proposed model is much smaller than the size of the previous approaches.
Characterization of the sugarcane silage effluent and the potential of environmental pollution

Introduction Brazil is a major supplier of animal protein for the world. Recent estimative by FAO (2010) have projected a 29% growth in Brazilian meat exports until 2019. This growth brings with it several issues that must be studied, especially environmental issues. The use of sugarcane silage has increased in recent years, because of several advantages over other types of grasses, mainly the productivity of more than 35 t DM ha -1 . However, the inadequate utilization of this forage as silage may lead to the formation of effluent during the ensiling process, with unknown pollution potential. Thus, this study aimed to quantify and evaluate the pollutant potential of sugarcane silage effluents.
Learning Robot Policies Using a High-Level Abstraction Persona-Behaviour Simulator

Collecting data in Human-Robot Interaction for training learning agents might be a hard task to accomplish. This is especially true when the target users are older adults with dementia since this usually requires hours of interactions and puts quite a lot of workload on the user. This paper addresses the problem of importing the Personas technique from HRI to create fictional patients’ profiles. We propose a Persona-Behaviour Simulator tool that provides, with high-level abstraction, user’s actions during an HRI task, and we apply it to cognitive training exercises for older adults with dementia. It consists of a Persona Definition that characterizes a patient along four dimensions and a Task Engine that provides information regarding the task complexity. We build a simulated environment where the high-level user’s actions are provided by the simulator and the robot initial policy is learned using a Q-learning algorithm. The results show that the current simulator provides a reasonable initial policy for a defined Persona profile. Moreover, the learned robot assistance has proved to be robust to potential changes in the user’s behaviour. In this way, we can speed up the fine-tuning of the rough policy during the real interactions to tailor the assistance to the given user. We believe the presented approach can be easily extended to account for other types of HRI tasks; for example, when input data is required to train a learning algorithm, but data collection is very expensive or unfeasible. We advocate that simulation is a convenient tool in these cases.
Destination 4 Destination 5 Destination 6 Destination 7 Source NAT FW Proxy NAT FW FW Proxy Proxy Proxy Proxy

Mobile Edge Computing (MEC) reforms the cloud paradigm by bringing unprecedented computing capacity to the vicinity of mobile users at the mobile network edge. This provides end-users with swift and powerful computing, energy efficiency, storage capacity, mobilityand context-awareness support. Furthermore, provisioning virtualized network services in MEC can improve user service experience, simplify network service deployments, and ease network resource management. However, user requests usually arrive into the system dynamically and different user requests may have different resource demands. How to optimize and guarantee the performance of MEC is of significant importance and challenging. In this paper, we study the problem of online NFV-enabled multicasting in an MEC network with resource capacity constraints on both cloudlets and links. We first devise an approximation algorithm for the cost minimization problem for a single NFV-enabled multicast request admission. We then propose an online algorithm with a provable competitive ratio for the online throughput maximization problem where NFV-enabled multicast requests arrive one by one without the knowledge of future request arrivals. We admit the requests through placing or sharing VNF instances of network functions in their service chains to meet their computing and bandwidth resource demands, and we introduce a novel cost model to capture the dynamic usages of different resources and perform network resource allocations based on the proposed cost model. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.
Exploring Trusted Relations among Virtual Interactions in Social Networks for Detecting Influence Diffusion

Recently, social networks have shown huge potential in terms of collaborative web services and the study of peer influence as a result of the massive amount of data, datasets, and interrelations generated. These interrelations cannot guarantee the success of online social networks without ensuring the existence of trust between nodes. Detecting influential nodes improves collaborative filtering (CF) recommendations in which nodes with the highest influential capability are most likely to be the source of recommendations. Although CF-based recommendation systems are the most widely used approach for implementing recommender systems, this approach ignores the mutual trust between users. In this paper, a trust-based algorithm (TBA) is introduced to detect influential spreaders in social networks efficiently. In particular, the proposed TBA estimates the influence that each node has on the other connected nodes as well as on the whole network. Next, a Friend-of-Friend recommendation (FoF-SocialI) algorithm is addressed to detect the influence of social ties in the recommendation process. Finally, experimental results, performed on three large scale location-based social networks, namely, Brightkite, Gowalla, and Weeplaces, to test the efficiency of the proposed algorithm, are presented. The conducted experiments show a remarkable enhancement in predicting and recommending locations in various social networks.
DEVELOPMENT OF A DATABASE OF PLANT PROTECTION PRODUCTS LOADS TO INLAND SURFACE WATERS IN GREECE TO SUPPORT THE MODELLING OF PESTICIDES POLLUTION

The work presented in this paper is related to the identification of the pressures on surface water bodies, as a result of diffuse pollution from the Plant Protection Products (PPPs) used during the agricultural activity in Greece. The pressures are quantified and expressed as loads from the PPPs’ active substances that enter surface water bodies by implementing the methodology presented by the FOrum for the Co-ordination of pesticide fate models and their Use (FOCUS Surface Waters). The methodological approach and its implementation have been conducted in the framework of the Switch-On project. Α database of loads to surface waters, for the most common active substances of PPPs in Greece, has been developed through the use of numerical simulations in accordance to the methodology presented by the FOCUS Surface Waters group for establishing relevant Predicted Environmental Concentrations in surface water bodies. The database includes 15 different active substances which were identified as the most significant for Greece. Two out of ten sets of modelling parameters scenarios, developed by the FOCUS SW group as a series of standard agriculturally relevant scenarios for the European Union, were used. The relevancy of these scenarios was estimated to be more than 30% for the Greek agricultural conditions, reaching up to 56% under certain conditions. According to the simulation results, all runoff scenarios resulted to less than 1% of the substance applied to the plants entering surface waters, whereas drainage could result up to 9%. On average, the percentage of a substance that reaches the surface waters is of the order of 1% of the quantity applied, when the drainage scenarios are implemented. In the case of runoff, the amount is lower and does not exceed, on average values, 0.3%. Although drift is a small transport component as to the total amount of substance transported into surface waters, it can result to greater concentrations in the water body than drainage or runoff.
Effect of g arlic ( Allium sativum ) supplemented diets on growth , feed utilization and survival of red tilapia ( Oreochromis sp . )

The present study evaluated the effect of varying levels of garlic (Allium sativum) inclusion on the growth, feed utilization and survival of red tilapia (Oreochromis sp.). One hundred twenty red tilapias with initial average body weight of 7.36 ± 0.75 g were randomly introduced into 12 aquaria in 4 groups with 10 fish per aquarium. Treatments were fed with experimental diets for six weeks. The various levels of garlic powder (0, 1%, 1.5% and 2%) was added to commercial diet. At the end of the experiment, growth, survival rates, and feed utilization were evaluated. There was no significant difference in the growth of the fish. The highest survival rate was recorded from 1.5% garlic supplementation that showed significant difference compared to the control diet (P < 0.05). In terms of feed utilization, lowest feed conversion ratio (FCR) was recorded from 1% garlic inclusion that showed significant difference compared to the control diet (P < 0.05). This study showed that addition of 1% to 1.5% garlic powder in the diet improved the feed utilization and survival rate of red tilapia.
A closer look at scalar diversity using contextualized semantic similarity

We take a closer look at van Tiel et al.’s (2016) experimental results on diversity in scalar inference rates. In contrast to their finding that semantic similarity had no significant effect on scalar inference rates, we show that a sufficiently fine-grained notion of semantic similarity does have an effect: the more similar the two terms on a scale, the lower the scalar inference rate. Moreover, we show that a context-sensitive notion of semantic similarity (in particular ELMo; Peters et al., 2018) can explain more of the variance in the data, but only modestly, only for stimuli that contain informative context words, and only when the scalar terms themselves are sufficiently context-sensitive.
ETHYLMETHANESULFONATE MUTAGENESIS OF CUCUMBER FOR LARGE-SCALE MUTANT SCREENS

To broaden the genetic resources for cucumber (Cucumis sativus L.) breeding, and to accelerate the systematic functional analysis of cucumber genes, a cucumber mutant library was constructed by using Ethylmethanesulfonate (EMS) mutation in this study. A total of 2,200 seeds were treated with EMS solutions, and 421 M2 families were obtained, which showed a mutant frequency of 19.1%. Some visible mutant phenotypes, such as having few spines, long fruits, short fruits, pale green fruit color, and plants without flowers were identified in this study. This study will be helpful for the identification of causal genes and for the determination of interesting mutant phenotypes of cucumber in the future.
Determination and Evaluation of Factors Associated with Sustainable Land Management (SLM) Practices by Farmers in Osun State, Nigeria

The study identified the sustainable land management practices being used by farmers in Osun State. The analysis determined the factors associated with the use of the land management practices and the best of the practices being used. Multistage sampling technique was used to select 256 farmers from eight Local Government Areas (LGAs) of the State. Eight different enterprise farmers were selected from the LGAs and four farmers were purposively selected from each enterprise. Data collected were described with frequency, mean and standard deviation while factor and regression analyses were used to make inferences for the study. The results revealed that the mean age of the farmers was 48.0±10.3, majority was married and about 60% were male. The mean household size was 6.6±2.8 and majority was able to read and write. The study identified 35 sustainable land management practices which were known to the farmers, out of which 28 were being practiced. Seven factors associated with the use of sustainable land management practices are: farming experience, economic, educational, accessibility to inputs, personal and family characteristics as well as soil fertility status. These factors were statistically significant (P < .05) to the use of soil management practices. We concluded that the level of awareness and use of sustainable land management practices in the state was still below the expected standard.
Towards a Comprehensive Deep Neural Network for Capturing Genomic Variant Features Final Report by Wong Yat Sing Supervised

Human beings has started to explore the mystery of heredity since the beginning of history. There is also popular belief that a person’s traits are closely related to the parents. Today, we know that heredity, the passing of traits to one’s offspring, is highly related to our “genome”, or more commonly known as its component, DNA. This project presents Clair, a neural network model based on the existing work Clairvoyante (https://github.com/aquaskyline/Clairvoyante) that uses LSTM layer as its main component. It achieved 94.96% and 18.87% F-1 score on SNPs and indel respectively for the Oxford Nanopore data on the sample NA12878. This projects also presents the analysis of data with t-SNE, which shows a limitation of the network model is the data quality (especially in homopolymer region) and a potential method to further improve the network is to modify the output design.
A Comparative Genre Analysis of Chinese and American Hotel Introductions

With the fast development of China’s travel industry, the publicity campaign of the hotels has become a frequently discussed question. The English introductions of Chinese hotels are usually the direct translation of the Chinese version ignoring the differences in the interior cognition of language and the exterior social and cultural influences. These of course will weaken the effect of our publicity efforts. The purpose of this study is to identify the communicative purposes and generic structures of Chinese and English hotel introductions (CHIs/EHIs) through analysis of collected samples in order to find the similarities and differences of them and explore the underlying social cultural reasons in order to provide some meaningful suggestions for those involved in the job of composing or translating hotel introductions in China.
Mutual Involvement in Families Living with Type 2 Diabetes: Using the Family Toolbox to Address Challenges Related to Knowledge, Communication, Support, Role Confusion, Everyday Practices and Mutual Worries

Family involvement plays a key role in diabetes management. Challenges related to type 2 diabetes (T2D) often affect the whole family, and relatives are at increased risk of developing diabetes themselves. Creating family involvement in families living with T2D is a complex matter. This article studies potential effects of working with dialogue tools specifically developed to create family involvement. The data consist of 18 semi-structured family interviews. The data were analyzed using radical hermeneutics and theories on family identity and healthcare authenticity. The analysis revealed five themes: (1) Working with the tools created better and broader intra-familial involvement; (2) the tools enabled new roles and self-understandings for all family members; (3) the tools facilitated mutual insights into each other’s thoughts and worries; (4) after working with the tools, it was easier to discover potential challenges and possible behavior change; and (5) gaining new knowledge and the motivation to seek more knowledge was easier after working with the tools. Working with the tools changed how the families perceive themselves and the ways in which they can affect their own T2D-related health behavior together. This has direct implications for healthcare practitioners working with people with T2D.
Obstacle avoidance of a humanoid mobile robot based on multi-sensor information

This paper deals with the modeling and experimentation of obstacle avoidance of a humanoid robot. At first, the hybrid obstacle avoidance algorithm is proposed by utilizing the advantages of the line following, tangent bug and wall following algorithms. Low cost range sensors and overhead web camera data are used as input to the algorithm to find obstacle free path. The algorithm is implemented on the LABVIEW platform. Second part of the work deals with the modeling of the legged robot. The robot walking mechanism is modelled as the oscillating cylinder mechanism. Two DC motors are used for actuation of the prismatic legs of the robot. The MATLAB software is used for modeling of the legged robot. The physical model of the legged robot is also developed by using NXT controller. The proposed obstacle avoidance algorithm is implemented on the legged robot. The single rectangular shape obstacle is considered for obstacle avoidance by the robot during simulation and experimental work. At last, the validation of the simulation and experimentation work is considered.