Miracle-dz/UniFAR-FTUs huggingface dataset:
PHYTOCHEMICAL ASSESSMENT FROM CALLUS AND SHOOT CULTURES OF A POTENTIAL MEDICINAL HERB: PHYLLANTHUS AMARUS (Schum. & Thonn)

This study was undertaken to evaluate the performance of cut Dendrobium pink sunshine under various holding solutions at post graduate lab Department of Horticulture, Faculty of Agriculture, Annamalai University, Tamil Nadu, India, during February 2016. The treatments with three replications were carried out in completely randomized design with 3 spikes in each replication. Nine chemical preservative solutions were used for extending the vase life and the treatments are T1 8-HQC (200-ppm), T2 Citric Acid (200-ppm), T3 STS (200-ppm), T4 GA (200-ppm), T5 Al2 (SO4)3 (10-ppm), T6 8-HQC (200-ppm) + 5% sucrose, T7 Citric Acid (200- ppm + 5% sucrose, T8 STS (200-ppm) + 5% sucrose, T9 GA (200-ppm) + 5% sucrose,T10 Al2(SO4)3 (200-ppm) + 5% sucrose and T11 Distilled water. Postharvest observations including bud open (%), flower drop (%), flower colour retention (days), flower diameter (cm), vase life and water uptake (ml). Among the treatments (T10) Al2(SO4)3 (200-ppm) + 5% sucrose recorded maximum performances in the postharvest studies followed by (T8) STS (200-ppm) + 5% sucrose. This study reports the phytochemical production from the callus and shoot cultures of Phyllanthus amars. Callus and shoot cultures were established from the nodal explants. Murashige and Skoog medium supplemented with N 6 - benzylaminopurine was found best medium for multiple shoot regeneration from the node explant and further maintenance of the shoot cultures. Callus induction was favoured on medium supplemented with 4.50 µM 2,4- dichlorophenoxy acetic acid. In vitro regenerated shoots and callus cultures were dried at room temperature and after measuring the dry biomass subjected for phytochemical extraction by using methanol and water. The extracts were quantified tested for total phenolics and flavonoids. The methanolic extract of in vitro and in vivo shoot showed the antioxidant activity.
JIGSAW: Efficient and Scalable Path Constraints Fuzzing

Coverage-guided testing has shown to be an effective way to find bugs. If we model coverage-guided testing as a search problem (i.e., finding inputs that can cover more branches), then its efficiency mainly depends on two factors: (1) the accuracy of the searching algorithm and (2) the number of inputs that can be evaluated per unit time. Therefore, improving the search throughput has shown to be an effective way to improve the performance of coverage-guided testing.In this work, we present a novel design to improve the search throughput: by evaluating newly generated inputs with JIT-compiled path constraints. This approach allows us to significantly improve the single thread throughput as well as scaling to multiple cores. We also developed several optimization techniques to eliminate major bottlenecks during this process. Evaluation of our prototype JIGSAW shows that our approach can achieve three orders of magnitude higher search throughput than existing fuzzers and can scale to multiple cores. We also find that with such high throughput, a simple gradient-guided search heuristic can solve path constraints collected from a large set of real-world programs faster than SMT solvers with much more sophisticated search heuristics. Evaluation of end-to-end coverage-guided testing also shows that our JIGSAW-powered hybrid fuzzer can outperform state-of-the-art testing tools.
Blockchain-based Planetary Level Autonomous Systems

The advent of the internet ushered in extraordinary growth on many technological fronts, from e-commerce to social networking. But we are yet to find effective solutions to global problems such as plastic cleaning, pandemic management, and energy distribution. These problems are inherently prevalent worldwide and typically addressed by centralized authorities with numerous trust intermediaries, which incur significant overhead and inefficiencies. Moreover, the stakeholders and participants of these large systems are culturally, socially, and geographically diverse. Building a system connecting them to solve a global problem demands enormous trust. This trust is currently realized in the financial world using centralized intermediaries such as banks and clearinghouses. The release of Bitcoin in 2009 [1] changed this situation with its blockchain protocol (infrastructure) by enabling decentralized trust intermediation. Ethereum’s smart contracts [2] followed up this innovation to strengthen trust through verification and validation. These two advances collectively enabled transactions among unknown participants anywhere, opening up opportunities and laying the foundation for addressing planetary level problems. This paper discusses the enabling features in realizing planetary-level autonomous systems and how blockchain technology elegantly incorporates these features.
Choosing Fair Committees and Budgets: Proportionality in Multi-Winner Elections and Participatory Budgeting

Participatory budgeting is a way to allow citizens to have a say in deciding how to spend public funds. It is a generalisation of multi-winner voting, where a committee is to be elected based on the preferences of multiple voters. An important topic in both multi-winner voting and participatory budgeting is fairness of committees or budgets. One way of expressing fairness and increasing the inﬂuence of minorities in a participatory budgeting project or multi-winner election, is requiring proportionality of a voting rule. But what is proportionality? Proportionality, although somewhat intuitive, is a complex concept, and can be deﬁned in many different ways. We bring more structure in the landscape of proportionality axioms, and show the existence or non-existence of logical relations between different axioms. Furthermore, we investigate the axiomatic properties of some of the most important existing and some promising new rules for computing proportional committees or budgets and we systemati-cally identify which axioms are satisﬁed by which voting rules, both in the multi-winner voting and the participatory budgeting setting.
Autonomous Landing of Fixed-Wing UAV in Net suspended by Multirotor UAVs - A Fixed-Wing Landing System

This thesis presents an autonomous landing system designed for an fixed-wing unmanned aerial vehicle (UAV) to land in a net suspended by multirotor UAVs. Ordinary landing of an fixed-wing UAV usually involves landing on a long runway, but by landing in a net suspended below multirotor UAVs, operations with fixed-wing UAV can be performed from ships or other platforms with confined space where no runway is available. The focus in this thesis is placed on the fixed-wing UAV part of the landing, and involves design and implementation of a guidance and control system, together with a system for coordinating a landing path between the fixed-wing UAV and the multirotor UAVs. The autonomous landing system uses the Pixhawk autopilot for low-level control, and a software toolchain from Underwater Systems and Technology Laboratory (LSTS), Porto, for high-level guidance and control, mission review and communication. The guidance and control system developed in this thesis is implemented in the LSTS toolchain, and consist of a decoupled lateral and longitudinal line-of-sight (LOS) guidance scheme for pitch and roll angle commands, and a speed controller for throttle command. The system is tested both in software-in-the-loop (SITL) simulations and flight experiments at Agdenes airfield, where successful tests demonstrated both the feasibility and the good performance of the system using a Skywalker X8 fixed-wing UAV.
Gaze Complements Control Input for Goal Prediction During Assisted Teleoperation

Shared control systems can make complex robot teleoperation tasks easier for users. These systems predict the user's goal, determine the motion required for the robot to reach that goal, and combine that motion with the user's input. Goal prediction is generally based on the user's control input (e.g., the joystick signal). In this paper, we show that this prediction method is especially effective when users follow standard noisily optimal behavior models. In tasks with input constraints like modal control, however, this effectiveness no longer holds, so additional sources for goal prediction can improve assistance. We implement a novel shared control system that combines natural eye gaze with joystick input to predict people's goals online, and we evaluate our system in a real-world, COVID-safe user study. We find that modal control reduces the efficiency of assistance according to our model, and when gaze provides a prediction earlier in the task, the system's performance improves. However, gaze on its own is unreliable and assistance using only gaze performs poorly. We conclude that control input and natural gaze serve different and complementary roles in goal prediction, and using them together leads to improved assistance.
Ferry: State-Aware Symbolic Execution for Exploring State-Dependent Program Paths

Symbolic execution and fuzz testing are effective approaches for program analysis, thanks to their evolving path exploration approaches. The state-of-the-art symbolic execution and fuzzing techniques are able to generate valid program inputs to satisfy the conditional statements. However, they have very limited ability to explore the finite-state-machine models implemented by real-world programs. This is because such state machines contain program-state-dependent branches ( state-dependent branches in this paper) which depend on earlier program execution instead of the current program inputs. explore state-dependent branches. state-dependent in their arbitrary state-dependent branches a of dependency between current program and inputs in class of important programs. Ferry , symbolic execution engine by recognizing program exploring state-dependent different programs the comprehensive dataset Google higher and branch
D ENSITY E STIMATION F OR C ONSERVATIVE Q-L EARNING

Batch Reinforcement Learning algorithms aim at learning the best policy from a batch of data without interacting with the environment. Within this setting, one difﬁculty is to correctly assess the value of state-action pairs far from the data set. Indeed, the lack of information may provoke an overestimation of the value function, leading to non-desirable behaviours. A compromise between enhancing the performance of the behaviour policy and staying close to it must be found. To alleviate this issue, most existing approaches introduce a regularization term to favor state-action pairs from the data set. In this paper, we reﬁne this idea by estimating the density of these state-action pairs to distinguish neighbourhoods. The resulting regularization guides the policy toward meaningful unseen regions, improving the learning process. We hence introduce Density Conservative Q-Learning (D-CQL), a sound batch RL algorithm that carefully penalizes the value function based on the information collected in the state-action space. The performance of our approach is outlined on many classical benchmark in batch RL.
UvA-DARE (Digital Academic Repository) Cigarette craving in virtual smoking environments

One way to overcome limitation is to use Virtual (VE) in CET, where people transported the real environment to a technology-based VE. In VE, real-life situations and responses are mimicked through presence. Real-life differences in cigarette craving between neutral and smoking cues can not only be replicated in VE Abstract Cue-Exposure Therapy (CET) is considered an effective strategy to combat cigarette cravings and smoking relapses, but evidence is mixed. In this lab-based experimental study, we manipulated levels of realism for smoking scenarios in Virtual Environments (VE) and randomly exposed smokers and recent-quitters to one of two versions (low versus high realism) of these scenarios. Prior and after scenario exposure, valid measures of cigarette craving were obtained. Prior to exposure, we assessed nicotine dependence and smoking status (current smokers versus recent-quitter). Within-subject repeated measures analysis of covar-iance showed that there was an interaction of experimental condition with smoking status on cigarette craving. Amongst recent-quitters, high realistic scenarios produced stronger increases in craving than low realistic scenarios, but this effect was reversed in current smokers. It is concluded that VE technologies are a potentially relevant tool for smoking CET that warrant further exploration.
ADOPTION OF SUSTAINABLE REAL ESTATE DEVELOPMENT IN SRI LANKA: A GAP ANALYSIS

The real estate sector in Sri Lanka has been experiencing more remarkable growth during the past decades. The emergence of innovative local and foreign investors, favourable government policies, urbanisation, enhancement of housing requirements and other socio-economic factors have driven the scope of the real estate sector to a greater extent. The drastic demand for investments and economic policies has transformed traditional real estate values into an innovative range while intersecting globalisation. The vast competition in the market, innovative trends, expansion of infrastructure, direct ownership opportunities, enrichment of living standards and growth of human needs have inspired real estate investors to achieve greater profits. Although such investment processes are empowered by governmental laws, regulations, policies and standards, the present nature of real estate investments exhibits a tendency not to sustain the future needs of Sri Lanka when achieving profits. Moreover, according to the literature and existing case studies, it is evident that investors avoid considering long-term solutions in real estate. Hence, this investigation demonstrates the necessity of adopting sustainable approaches to define a baseline for real estate investors’ financial perspectives within the Sri Lankan context. It examines the nature of practice in real estate to address the necessity of sustainable real estate development in Sri Lanka. The study has adopted a qualitative approach and aimed at obtaining data via secondary sources. The analysis of data was subjected to be undertaken through a content analysis for a better overview. The research findings are significant in assessing investment opportunities, value-added real estate developments, sustainable necessities, and future trends in real estate development in Sri Lanka.
Underwater Robot-To-Human Communication Via Motion: Implementation and Full-Loop Human Interface Evaluation

—Autonomous underwater vehicles (AUVs) have long lagged behind other types of robots in supporting natural communication modes for human-robot interaction. Due to the limitations of the environment, most AUVs use digital displays or topside human-in-the-loop communications as their primary or only communication vectors. Natural methods for robot-to-human communication such as robot “gestures” have been proposed, but never evaluated on non-simulated AUVs. In this paper, we enhance, implement and evaluate a robot-to-human communication system for AUVs called Robot Communication Via Motion (RCVM), which utilizes explicit motion phrases (kinemes) to communicate with a dive partner. We present a small pilot study that shows our implementation to be reasonably effec- tive in person followed by a large-population study, comparing the communication effectiveness of our RCVM implementation to three baseline systems. Our results establish RCVM as an effective method of robot-to-human communication underwater and reveal the differences with more traditional communication vectors in how accurately communication is achieved at different viewpoints and types of information payloads.
Research on on-line monitoring data cleaning of transformer oil chromatogram based on machine learning and neural networks

Transformer oil chromatogram on-line monitoring device is a widely used sensing device to monitor the operation state of transformer. Due to the lack of hardware verification means, the on-line chromatographic data generally has the problem of drift. In actual operation, it is mostly used as the basis for trend judgment, but can not reflect the effective value of chromatographic measurement. In this paper, the LSTM neural networks is used to correct the on-line monitoring data of transformer oil chromatogram. The off-line test data combined with neural networks data cleaning technology are used to repeatedly feed back and calibrate the online data, so that the corrected chromatographic on-line number is steadily close to the off-line detection data, which greatly improves the availability and accuracy of on-line chromatographic data. The research results of this paper have important reference significance for the data cleaning of on-line monitoring and measurement of electrical equipment.
Ultra-short-term Forecast of Multi-energy load for Integrated Energy System based on Attention Mechanism and BiLSTM

Under the background of the continuous promotion of the construction of the integrated energy system (IES), accurate and rapid multi-type energy load prediction is of great significance to the safe and economic operation of IES. Traditional forecasting models are difficult to model complex fluctuations of multi-energy loads, cannot consider the correlation among different types of energy loads. For this reason, this paper presents an ultra-short-term prediction method for multi-energy load of integrated energy system based on attention mechanism and bi-directional long short-term memory (BiLSTM) network. It uses deep learning model to learn the feature information of time series, and uses attention mechanism and BiLSTM to improve the effectiveness of features. The validity of the algorithm is validated in the example section. The results show that the proposed method reduces the prediction error effectively compared with traditional methods.
Behavior Model Analysis of Pedestrians and Cyclists Mode Selection Base on Active Transportation on Universitas Jember Tegalboto Campus

The University of Jember as one of the campuses that have a high traffic volume is trying to realize a “Green Campus” through the “Ring the Bell” program. However, this vision has not been implemented optimally because there are still many campus residents who do not use active modes of transportation (walking & cycling) even though sidewalk facilities are available. This indicates that the behavioral preferences for choosing active transportation modes for the residents of the Jember University campus are still unclear. Therefore, this study aims to analyze the behavioral model of pedestrian and cyclists mode selection based on active transportation at the Tegalboto Campus, Jember University. The primary data collection of residents’ preferences was obtained through the TPB (Theory Of Planned Behavior) questionnaire. SEM model (Structural Equation Model) was applied in the behavioral analysis process to determine the interaction between the independent variables of TPB as a whole. The results of behavioral modeling show that intention is the variable that most influences the behavior of campus residents in using active transportation modes, while attitude is the variable that most influences intentions than subjective norms and perceptions of behavioral control.
Future Frame Synthesis for Fast Monte Carlo Rendering

dataset by collecting or purchasing model and scene files and render them using the Unreal Engine or Blender Cycles. Our dataset contains many animation sequences with a variety of animation characters, background scenes and camera motion. This dataset allows us to train and test our neural future frame synthesis method. Our experiments show that our method is able to drastically reduce the number of rays that need to be sampled to produce frames while maintaining high rendering quality.
Back Propagation in a Cli(cid:11)ord Algebra

Recent work has shown that in some cases the phase information of synaptic signal is important in the learning and representation capabilities of networks. Modelling such information with complex valued activation signals is possible, and indeed complex back-propagation algorithms have been derived [3]. Cli(cid:11)ord algebras give a way to generalise complex numbers to many dimensions. This paper presents a back propagation for feed-forward networks with Cli(cid:11)ord activation values.
The Effect of Information Content and Length on Name Recollection

Memorable function and variable names are useful for developers: they reduce the need to re-check how objects are named when one wants to use them, and they enhance comprehension when encountered when reading code. We look at the possible interplay between the information contained in names and how memorable they are. We show in two independent experiments involving a total of 190 subjects that informative names are usually easier to recollect than similar-length names which contain less focused information. Interestingly, we find that less-experienced and female participants are better at remembering the less informative names. We also find that short names, which are not just abbreviated but actually contain less information, are significantly more memorable. Hence a good choice would be to use the the shortest name that includes the most focused and pertinent information.
Nanocomposite ZnO/g-C 3 N 4 for Improved Degradation of Dyes under Visible Light: Facile Preparation, Characterization, and Performance Investigations

In this study, ZnO/g-C 3 N 4 nanocomposites were prepared via a physical mixing-calcination process for improved degradation of dyes under visible light irradiation. The BET surface area, pore volume, crystal size, and pH pzc of the ZnO/g-C 3 N 4 composite were 3.9 m 2 /g, 0.034 cm 3 /g, 18.1 nm, and 7.7, respectively. Although the morphology of the ZnO/g-C 3 N 4 composite was very different from that of pure g-C 3 N 4 , their average pore sizes were similar. The E g of the ZnO/g-C 3 N 4 composite (3.195 eV) was slightly lower than that of ZnO (3.195) but much higher than that of g-C 3 N 4 (2.875). The interface interaction of ZnO and g-C 3 N 4 , which was revealed by oscillations of Zn-C, benefited the transport of photoinduced charge carriers and reduced the recombination of electron-hole. As the result, the ZnO/g-C 3 N 4 composite had higher photocatalytic activity than ZnO and g-C 3 N 4 . Its degradation efficiency ( DE ) value for methylene blue (MB) in 90 min and rate constant were 93.2 % and 0.025 min ‑ 1 , respectively. In addition, the effects of ZnO/urea molar ratio, catalyst dosage, solution pH, and concentration of dye on photocatalytic degradation of MB were completely investigated. The photocatalytic performance of the ZnO/g-C 3 N 4 composite was evaluated by the degradation of other persistent organic compounds, also compared to other catalysts in the literatures.