Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards AI-Assisted Smart Training Platform for Future Manufacturing Workforce

—With the fast development of Industry 4.0, the ways in which manufacturing workers handle machines, materials, and products also change drastically. Such changes post several demanding challenges to the training of future workforce. First, personalized manufacturing will lead to small batch and fast changing tasks. The training procedure must demonstrate agility. Second, new interfaces to interact with human or robots will change the training procedure. Last but not least, in addition to handling the physical objects, a worker also needs to be trained to digest and respond to rich data generated at the manufacturing site. To respond to these challenges, in this paper we describe the design of an AI-assisted training platform for manufacturing workforce. The platform will collect rich data from both machines and workers. It will capture and analyze both macro and micro movement of trainees with the help of AI algorithms. At the same time, training for interaction with robot/cobot will also be covered. Mixed reality will be used to create in-situ experiences for the trainee.
Lane Detection Using Python

For vehicles to have the option to drive without anyone else, they have to comprehend their encompassing outside as human being chauffeurs, so they can explore their aisle in roads, delay at gorge gestures and marching signals, also try not to hit snags, for example, different vehicles and people on foot. In light of the issues experienced in identifying objects via independent vehicles an exertion has been made to exhibit path location utilizing OpenCV library. The explanation and methodology for picking grayscale rather than shading, distinguishing edges in a picture, choosing locale of weal, embedding Hough Variation also picking basal directions ceased kateezijan facilitates has been examined.
Evaluating word embeddings for computational pragmatics

Word embeddings are a popular set of machine learning techniques that are used in natural language processing to create a semantic model of a corpus, representing semantic relationship within a geometric space. In this space every word is mapped to a vector so that words with similar meanings are represented close to one another while words with different meanings are distant. In this work we briefly present a novel use of word embedding for computational pragmatics in which we used word2vec Skip-Gram (henceforth SG) to explore the inferential model generated by algorithmic personalization on Facebook. We used their experiment in a recent study in which we implemented Eco's model reader as an inferential model. In a nutshell, we found that we can simulate the model reader with word embedding to create computational inferential models, that are then compared to identify a possible divergence of interpretation paths. Our experiment conducted on Facebook showed that algorithmic personalization influences model readers, differentiating inferences across different timelines. Though for pragmatic goals, word embeddings have two significant issues that are 1) accuracy 2) variability. The standard way of evaluating the accuracy of word embeddings is to use intrinsic evaluation, using predefined analogies and similarities such as WordSim-353 (Finkelstein et al. 2002) and SimLex. However, this accuracy is quite hard to estimate while inquiring pragmatic aspects as we do not have any predefined pragmatic output. This work aims to review these methods for a computational pragmatic approach, highlighting possible limitations and adaptations. The review will cover three aspects:
The effect of trivalent cations on the major lower mantle silicates

I have investigated the effect of composition, especially ferric iron and aluminum, on the equations of state and phase stability of perovskite and post-perovskite. The presence of trivalent cations decreases the bulk modulus of perovskite at pressures corresponding to the upper lower mantle. Ferric iron in perovskite undergoes a spin-pairing transition from the high spin state to low spin in the octahedral site. Ferric iron in the dodecahedral site remains high spin. In the absence of aluminum, the spin transition is gradual between 0 and 55 GPa, and bulk modulus increases at the completion of the spin transition. In the presence of aluminum, there is an abrupt increase in the amount of low spin ferric iron near 70 GPa, likely the result of site mixing. The high compressibility of the structure below 70 GPa results in the volume nearing that of magnesium endmember, MgSiO 3 , perovskite. Concurrent with the spin transition in aluminum-bearing perovskite, the structure stiffens. The increase in density and bulk modulus at -70 GPa results in an increase in bulk sound speed that may be related to heterogeneities in bulk sound speed observed seismically at 1200-2000 km depth in the Earth. The effect of composition on the perovskite to postperovskite phase transition was also investigated. No change in the spin state of ferric iron was found at the perovskite to post-perovskite phase transition: ferric iron is low spin in the octahedral site and high spin in the dodecahedral site. At the phase transition, ferric iron only slightly broadens the perovskite plus post-perovskite mixed phase region while ferrous iron and aluminum were each found to significantly broaden the mixed phase region to hundreds of kilometers thick. The effect of background mineral phases was assessed for a basaltic system, rich in aluminum. The coexisting minerals were found to significantly reduce the effect of the aluminum, producing a boundary that is potentially sharp enough for seismic detection in silicon-rich systems, such as basalt. Thesis Supervisor: Sang-Heon Shim Title: Associate Professor
Anaerobic Digestion and Hot Water Pretreatment of Tropically Grown C4 Energy Grasses: Mass, Carbon, and Energy Conversions from Field Biomass to Fuels

The efficacy of C4 grasses as feedstocks for liquid fuel production and their climate mitigation potential remain unresolved in the tropics. To identify highly convertible C4 grasses, we measured final fuels and postprocess biomass produced in two laboratory-scale conversion pathways across 12 species and varieties within the Poaceae (grass) family. Total mass, carbon, and energy in final fuels and postprocess biomass were assessed based on field mass and area-based production. Two lignocellulosic processes were investigated: (1) anaerobic digestion (AD) to methane and (2) hot water pretreatment and enzymatic hydrolysis (HWP-EH) to ethanol. We found AD converted lignocellulose to methane more efficiently in terms of carbon and energy compared to ethanol production using HWP-EH, although improvements to and the optimization of each process could change these contrasts. The resulting data provide design limitations for agricultural production and biorefinery systems that regulate these systems as net carbon sources or sinks to the atmosphere. Median carbon recovery in final fuels and postprocess biomass from the studied C4 grasses were ~5 Mg C ha−1 year−1 for both methane and ethanol, while median energy recovery was ~200 MJ ha−1 year−1 for ethanol and ~275 MJ ha−1 year−1 for methane. The highest carbon and energy recovery from lignocellulose was achieved during methane production from a sugarcane hybrid called energycane, with ~10 Mg C ha−1 year−1 and ~450 MJ ha−1 year−1 of carbon and energy recovered, respectively, from fuels and post-process biomass combined. Carbon and energy recovery during ethanol production was also highest for energycane, with ~9 Mg C ha−1 year−1 and ~350 MJ ha−1 year−1 of carbon and energy recovered in fuels and postprocess biomass combined. Although several process streams remain unresolved, agricultural production and conversion of C4 grasses must operate within these carbon and energy limitations for biofuel and bioenergy production to be an atmospheric carbon sink.
A Study On Fuzzy Queue With N Policy By Triangular Fuzzy Numbers

Fuzzy queue tries to reduce the length of the queues in many practical situation. In this paper we studied about the fuzzy queue with N-policy. Hence we discuss about the characteristics ofKN-policy fuzzy queue under vague data. Hence we used triangular fuzzy numbers as input parameters and used markov chain approach to find the expected waiting time in the queue and the expected number of customers in the system under fuzzy environment.
The Genetic Relationship of Piper nigrum L. of Central Highlands and South Vietnam Assessed by rbcL gene

The genetic relationships of Vietnamese black pepper varieties in the Central Highlands and Southern were evaluated by comparing the rbcL gene sequences of the collected black pepper varieties and the reference sequences on the base source NCBI data. Analysis results of nucleotide gene sequence variable rbcL of black pepper varieties showed Vietnamese varieties have much variable in nucleotide sequence compared to Asian varieties. The important characteristic variable areas of the Vietnam II black pepper group occurred at position 59292 (except for varieties SDL, VLDN5 and VLDN6), positions 59282 and 59295, variable appeared in 5 varieties (VLPQ1, VLVT1, VLGL3, VLDN4 and VLDN5), accounting for 41.67% of total samples collected. Genetic distances of black pepper varieties collected in the Central Highlands and the Southern, Vietnam (Vietnam II) (0.024 ± 0.005) with the Asian group were higher than those of black pepper group researched (Vietnam I) (0.001 ± 0.001). The phylogenetic tree demonstrated the difference between the two groups of Vietnamese black pepper. Among the varieties in the Vietnam II group, only the VLDN6 pepper variety has a close relationship with the Asian group. Analyzing the variable positions, the group Vietnam II was divided into 3 groups: Group I: VLPQ3; Group II: VLGL3, VLDN4 and VLDN5; Group III: SDL, VLVT1, VLVT2, VLDS1, VLGL1, VLPQ1 and VLPQ2. Thus, black pepper in the Central Highlands and Southern regions of Vietnam will be potential region for genetic diversity research in Asian pepper populations.
Environmental Sustainability through Green Banking

Economic development of all the nations depends upon industrialization. But on the hand to resulted in exploitation of the natural resources which in turn has distrusted to the ecological development. The distributed on ecological balance for adversely the human and yon sounding environment. In even industrialization caused industrial or indirect in the last few decades. It has created a caution among the environmental, government and organization from all over the world. Sustainable development has become an important agenda in the international community. Various conferences and seminars are being organized to create awareness about this alarming issue. Based on this, the governments, as well as organization, take the green initiatives for environmental protection and sustainability. Environmental sustainability plays a vital role in sustainable development, with the integration of social, environmental protection. Most of the organization considered environmental protection as an important strategy and started offering environment friendly or green products and services to the society. But the concept of environmental sustainability and green product and services is new to developing countries like India.
Minimizing Effort and Risk with Network Change Deployment Planning

Networks undergo continuous changes to introduce new services and improve existing ones. Network change deployment involves carefully deciding when each change activity will be executed and who will be executing the change. This is a complex process because each service group has to plan its activities following a set of operational and technological constraints. Besides, multiple groups may be working on the same or dependent nodes at the same time, and they must coordinate their deployment plans. If they do not co-ordinate, conflicting change execution could result in unexpected impacts. Traditionally, change deployment has been a tedious and time-consuming task. To address this, we propose an innovative solution Zapper that aims for minimal human effort to coordinate the changes, minimal risk to service quality, and efficient plans to rapidly deploy the changes. Zapper maps change scheduling constraints into mathematical equations and then uses optimization algorithms to generate conflict-free change plans that satisfy all constraints across service groups. We have deployed Zapper at a large service provider and it is being used regularly by the network operations teams for more than two years to schedule over 4.5 million change activities.
Some asymptotics for elemental subsets in regression with applications

In a linear regression model, elemental subsets consist of the minimum number of observations needed to estimate the regression parameter where the resulting estimators are called elemental estimators. Elemental estimators have a long history in statistics; many estimators are functions of elemental estimators and elemental estimators are used for outlier detection as well as for computation of highly robust estimators. In this paper, we consider some asymptotic theory for elemental subsets in linear regression. In particular, we derive the limiting distribution of the elemental subsets that produce “good” elemental estimators. A number of applications of this theory are also given, including a diagnostic for homoscedasticity, a heuristic for sampling elemental subsets in the computation of least median of squares estimates, and a diagnostic for the Powell estimator in a censored linear regression model.
The Subjective Well-Being of Small Societies

So-called “small societies” such as Amish communities and traditional tribal groups offer a unique opportunity to chart the conceptual territory of subjective well-being (SWB). First, these groups offer an alternative to standard cross-national comparisons that are so common in happiness research. Because of their smaller size and relative isolation, each small society represents a relatively homogenous experience of happiness that can shed light on the ways that social ties, community norms and practices might influence SWB. This chapter explores both culture and methods as they relate to SWB. Three diverse “case studies” are discussed including the Amish, Tonga Islanders, and the Maasai. Finally, recommendations are given for improving SWB research.
Marginal Likelihood Gradient for Bayesian Neural Networks

Bayesian learning of neural networks is attractive as it can protecting against over-ﬁtting and provide automatic methods for inferring important hyperparameters by maximizing the marginal probability of the data. However, existing approaches in this vein, such as those based on variational inference, do not perform well. In this paper, we take a diﬀerent approach and directly derive a practical estimator of the gradient of the marginal log-likelihood for BNNs by combining local reparametrization of the network w.r. t. the prior distribution with the self-normalized importance sampling estimator. We show promising preliminary results on a toy example and on vectorized MNIST classiﬁcation where the new method results in signiﬁcantly improved performance of variational inference compared to existing approaches to tune hyperparameters.
CFD SIMULATION OF COMBUSTION AND WATER INJECTION IN A GAS

Heavy duty gas turbines are one of the most used technologies for the power generation. Nowadays has been crucial, especially for the power generation sector, to comply a new sustainable development. The main objective of this thesis is to define a 3D CFD water injection model optimizing the computational costs required by this kind of simulation. Water injection strategy represent an answer to decrease the NOx emissions produced by a heavy-duty gas turbine. The water inside the combustor works as a heat sink, lowering the temperature of the flame with the direct consequence on the NOx abatement. Also, the water introduced is an additional working fluid inside the combustor and that produce a turbine power output increase. The main cost to pay by introducing water inside the combustor is an overall efficiency decrease. There are many possibilities to simulate the water injection inside the combustor of a gas turbine. The first one is represented by the VOF (Volume of Fluid) simulation, the second one by modelling the spray using ELSA spray atomization strategy. In this work, to avoid the computational effort required by these two strategies, a study of water injection as a simple heat sink is proposed. A first preliminary study on the NASA LDI gas turbine has been conducted to study how the ELSA spray atomization works and to have the reference to reproduce the water injection as a boundary condition. From this analysis, the water injection study has been moved on the TG 20 gas turbine real combustor with a process of optimization concerning the concentration of water and the influence of this boundary condition on the primary recirculation zone. The model has been validate comparing the results in terms of NOx, UHC, CO coming from experimental results. Index
Preparation and Characterization of Composite Fruit Leather of Peach and Tomato

In human diet fruits play a very important role and are very good for health due to their beneficial effects and great nutritional value. Peach and tomato have high nutritional content and are very favorable for the growth, treatment of various diseases and for maintaining good health. Shelf-life and nutritional value of fruits can be preserved by making different products that contained low moisture content and preservation of fruits by dehydration is the best method to preserve fruit. Fruit leather is a meticulous produce which are made by using the puree of the fruits (like apple, mango, peach, tomato etc.) and are eaten as snack food. In present study, composite fruit leather, with improved nutritional value, was produced by using peach and tomato puree. The physicochemical analysis of fruit leather including proximate, TSS, pH, color, TPC and lycopene content were carried out. There was increase in moisture and fat content of leather and there was decrease in amount of ash. The pH and TSS of samples was decreased from 4.04 to 3.29 and 13.00 to 9.87 from T0 to T5. Results showed high TPC and antioxidant activity in all treatments. There was also observed increase in lycopene content in all the treatments ranging from 0.14 to 5.59. Sensory evaluation shows that the control is the most liked treatment. Thus, the results indicated that by incorporating tomato puree it is possible to improve nutritional quality and improved lycopene content of composite leather. [Hira Ijaz, Manazza Ayub, Ayesha Waqar, Atka Afzal, Khadija Kausar, Ali Akbar Gill, Komal Rehman. Preparation and Characterization of Composite Fruit Leather of Peach and Tomato. J Am Sci 2021;17(3):2835]. ISSN 1545-1003 (print); ISSN 2375-7264 (online). http://www.jofamericanscience. org. 4. doi:10.7537/marsjas170321.04.
Diffeomorphic Explanations with Normalizing Flows

Normalizing ﬂows are diffeomorphisms which are parameterized by neural networks. As a result, they can induce coordinate transformations in the tangent space of the data manifold. In this work, we demonstrate that such transformations can be used to generate interpretable explanations for decisions of neural networks. More speciﬁcally, we perform gradient ascent in the base space of the ﬂow to generate counterfactuals which are clas-siﬁed with great conﬁdence as a speciﬁed target class. We analyze this generation process theoretically using Riemannian differential geometry and establish a rigorous theoretical connection be-tween gradient ascent on the data manifold and in the base space of the ﬂow.
OPTIMIZED INTRUSION DETECTION CLASSIFICATION METHOD USING MACHINE LEARNING

Securing a network from the attackers is a challenging task at present as many users involve in variety of computer networks. To protect any individual host in a network or the entire network, some security system must be implemented. In this case, the Intrusion Detection System (IDS) is essential to protect the network from the intruders. The IDS have to deal with a lot of network packets with different characteristics. A signature-based IDS is a potential tool to understand former attacks and to define suitable method to conquest it in variety of applications. Data Mining techniques are used in the process of knowledge discovery for many domains’ problems. Feature Selection plays a vital role for a large number of datasets. This paper discusses on the classification of attacks in the network with the assistance of the proposed Optimized Intrusion Detection Classification technique. In this proposed technique, the DBN hidden layers weights are optimized by using evolutionary Genetic algorithm. This GA is utilized to enhance the classification accuracy by applying the hidden layers of Restricted Boltzmann Machine (RBM). The comparative results show that the proposed classifier gives the improved accuracy, specificity, precision, Sensitivity, and reduced false positive rate, miss rate.
Semi-Supervised Siamese Network for Identifying Bad Data in Medical Imaging Datasets

Noisy data present in medical imaging datasets can often aid the development of robust models that are equipped to handle real-world data. However, if the bad data contains insufficient anatomical information, it can have a severe negative effect on the model's performance. We propose a novel methodology using a semi-supervised Siamese network to identify bad data. This method requires only a small pool of 'reference' medical images to be reviewed by a non-expert human to ensure the major anatomical structures are present in the Field of View. The model trains on this reference set and identifies bad data by using the Siamese network to compute the distance between the reference set and all other medical images in the dataset. This methodology achieves an Area Under the Curve (AUC) of 0.989 for identifying bad data. Code will be available at https://git.io/JYFuV.
Portfolio Management with Reinforcement Learning

Portfolio management is a crucial trading task for investment companies in the market. In this work, reinforcement learning (RL) incorporating the transformer structure is combined with deep learning (DL) to build an automated portfolio management model. The proposed method uses the Sharpe ratio along with trans­ action cost as the reward and build an environment that contains the whole A­share market to train the RL agent. The result demonstrates that the trained strategy out­ performs The Shanghai Composite Index and tradition baselines.
Broadcast Encryption with Fine-grained Delegation and its Application to IoT

Broadcast encryption schemes allow senders to distribute data to selected receivers securely. Broadcast encryption schemes have been widely used in designing revocation protocols in publish-subscribe systems. However, existing broadcast encryption schemes do not support delegation, which is essential for secure communication in IoT systems. We extend tree-based broadcast encryption schemes to support delegation, which allows subscribers to delegate their keys to other subscribers in a ﬁne-grained, distributed way. We design a revocation protocol for IoT systems based on the delegable broadcast encryption scheme. By incorporating our revocation protocols and other designs, we propose JEDI [49] ( J oining E ncryption and D elegation for I oT), a many-to-many end-to-end encryption protocol for IoT. JEDI encrypts and signs messages end-to-end while conforming to the decoupled communication model typical of IoT systems. This report only focuses on the part of the JEDI design that includes the delegable broadcast encryption and the revocation protocol. Please refer to the JEDI paper [49] for more details.