Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effectiveness of Audio Media in Enhancing Farmers' Knowledge: The Case of Smallholder Banana Farmers in Western Uganda

Although audio media has been presented by previous research as a suitable medium for conveying agricultural information, there is limited research in assessing its effectiveness in assisting farmers’ gain agricultural knowledge. This study thus undertook to carry out that investigation by involving 1000 smallholder banana farmers in the study which involved a baseline survey, an intervention, and an end line survey. The preliminary survey results showed that 456 farmers had relevant knowledge of farming practices and were removed from the study. However, 541 farmers were lacking the required knowledge regarding proper banana farming practices. This group was assigned to an intervention of radio broadcasts for one month. All broadcasted content was recorded on CDs and circulated to the participants for continued revision. This was followed by the end line survey and the two datasets were compared to establish the change in knowledge levels of the farmers. The tests of variances before the intervention and after the intervention indicated that the two groups being compared are assumed to be approximately equal (p >0.05). Results showed a significant difference in the mean knowledge scores before and after the intervention. Furthermore, independent t-test results reveal a statistically significant difference in the pre- and post-knowledge tests of farmers. In addition, results from Pearson Correlations show that farmers’ characteristics such as age, gender, and level of education do not influence the uptake of knowledge on farming practices by farmers. However, results reveal a statistically significant association between the source of information (Radio Broadcast & audio CDs) and knowledge gain regarding most banana farming practices by farmers. This study provides further evidence that audio media in the form of radio broadcasts and audio CDs is a vital source for agricultural information to the rural farmers. Moreover, it can be concluded that farmers’ characteristics do not influence knowledge gain for banana farming practices when audio media is used as a medium of information dissemination. And thus, audio media remains a vital source of information for resource-poor farmers and can greatly enhance their agricultural knowledge when audio media is used as an intervention.
Incentive Scheme for Cyber Physical Social Systems Based on User Behaviors

Cyber-physical social system (CPSS) has emerged as a new paradigm to help social users share and exchange data by the close association with the cyberspace and physical world. To further improve the performance of CPSS, the incentive computing scheme to provide efficient crowd sourcing in the CPPS becomes a challenge. Therefore, in this paper we propose a novel incentive scheme for CPSS based on the reputation of social users. First, we present a framework to provide crowd sourcing service in CPSS by dividing social users into three types, which are malicious users, speculative users and honest users, respectively. Second, based on the reputation of social users, an incentive scheme is proposed to encourage users to contribute sourcing data. Next, an auction game model is developed to help CPSS select the optimal social user to obtain the needed data. Finally, simulation results show that the proposal can obtain a lower cost and higher data accuracy than other conventional methods.
Measuring the Vulnerability of a Multi-Agent Pathfinding Solution

Multi-agent pathfinding is the problem of finding a non-interfering paths for a set of agents, such that if the agents follow these paths then each agent will reach its desired destination. Recent years have shown tremendous advances in this field, with optimal and suboptimal algorithms that are able to plan paths for over 100 agents in reasonable time. However, autonomous mobile agents are prime targets for cyber-security attacks, where an adversary may take control over an agent to disrupt the agents execution of their plan. This threat raises two questions. The first question is how much damage can an agent do if it does not follow its plan. The second question is how can one plan a-priori to be as robust as possible to such cyber-attacks. In this work, We provide an answer to both questions. To compute the maximal amount of damage that an adversary agent can do, we define a corresponding graph search problem and solve this problem with A*. Then, we provide a very simple method to choose a solution that is robust to such damages. We demonstrate both algorithms in simulation over standard multi-agent pathfinding domains.
Algorithm Selection in Optimization and Application to Angry Birds

Consider the MaxScore algorithm selection problem: given some optimization problem instances, a set of algorithms that solve them, and a time limit, what is the optimal policy for selecting (algorithm, instance) runs so as to maximize the sum of solution qualities for all problem instances? We analyze the computational complexity of restrictions of MaxScore (NP-hard), and provide a dynamic programming approximation algorithm. This algorithm, as well as new greedy algorithms, are evaluated empirically on data from agent runs on Angry Birds problem instances. Results show a significant improvement over a hyper-agent greedy scheme from related work.Solomon Eyal ShimonyAvinoam Yehezkel
Robust multi-contact dynamical motion planning using contact wrench set

In this thesis, we seek to plan a robust motion for robot with multiple non-coplanar contact on the environment. When the robot interacts with the environment through contact, it relies on the contact forces to generate the desired acceleration. The contact forces have to satisfy some physical constraints, such as lying within the friction cones. These constraints limit the robot acceleration. The robustness of the motion can be measured as the margin to the boundary of these constraints. By planning motion with a large preserved margin, we enable the robot to withstand large disturbance in the online motion execution. In this thesis, we adopt the notion of contact wrench set to approximate the constraints on the robot dynamics. The margin of such set measures the capability of the motion to perfectly resist external wrench disturbance. We plan robust motion to increase this contact wrench set margin. We present two planners to improve this robustness metric. For the first simplemodel planner, we pre-specify the contact locations, and it generates a Center of Mass trajectory and an angular momentum trajectory, by solving a convex optimization problem. We show that this planner has similar output as the widely-used walking pattern generator that relies on Zero Moment Point (ZMP) on flat ground. Moreover, it can plan feasible motion on uneven ground with friction cone limits, while the ZMP planner fails. For the second planner with robot whole-body model, we will search for the contact location and the robot whole-body motion simultaneously. We show that we can improve the robustness metric through certain non-convex optimization techniques. We apply our planner to three problems: 1) force closure grasp optimization, 2) static posture optimization, 3) trajectory optimization, achieving improved performance for all of them. Thesis Supervisor: Russ Tedrake Title: Professor
One substrate, two creoles

In this paper, I compare the segmental inventories of Haitian and St. Lucian with those of their contributing languages, in order to evaluate the impact of the substrate languages on the development of these systems. I show that the segmental inventories of the creoles display many features that were inherited from the Gbe languages through transfer, which suggests a massive impact of the substrate in the development of the segmental inventories of the creoles. In a nutshell, the phonological systems of Haitian and St. Lucian appear as a compromise between those of their contributing languages, Fongbe and other Gbe languages (the substrate), and French (the superstrate). While both creoles have essentially the same vowel inventory as the substrate, their consonantal inventory is closer to the superstrate. This hybrid character of the creoles is similar to what is found in an interlanguage, in the more canonical cases of second language acquisition. I propose scenarios where the differences between the two creoles and their superstrate result from phonological and phonetic processes that are typical of interlanguages – e.g., segmental substitution under phonological equivalence, transfer of L1 allophonic rules. The scenarios assume that the emergence of a creole is a particular case of second language acquisition (Andersen 1983). They also assume the Full Transfer/Full Access model of Second Language Acquisition (Schwartz & Sprouse 1996; White 2000), according to which second language learners assume the grammar of L1 as a starting point – Full Transfer – and progressively restructure this initial grammar using principles and parameters of Universal Grammar – Full Access. It is shown that the features of the substrates have guided the acquisition (or non acquisition) of the segmental inventories of French, even in cases where Universal Grammar may have been at play.
Adaptive hybrid force-position control for redundant manipulators

An adaptive control scheme for manipulators with redundant degrees of freedom is presented. The control objective is to achieve a desired interaction force between the end-effector and the environment as well as to regulate the robot tip position in Cartesian space. This control approach does not require measurement of the joint acceleration or the force derivative. Global convergence results are obtained in the sense that all signals remain bounded and the force and position tracing errors converge to zero. <<ETX>>
Risk-based assessment for distribution network via an efficient Monte Carlo simulation model

Modern active distribution networks make use of intelligent switching actions to restore supply to end users after faults. This complicates the reliability analysis of such networks, as the number of possible switching actions grows exponentially with network size. This chapter proposes an approximate reliability analysis method where switching actions are modelled implicitly. It can be used graphically as a model reduction method, and simulated using timesequential or state sampling Monte Carlo methods. The method is illustrated on a simple distribution network, and reliability indices are reported both as averages and distributions. Large speedups result from the use of biassed non-sequential Monte Carlo sampling – a method that is hard to combine with explicit switching models.
Design of Special Protection System for an Offshore Island With High-PV Penetration

An intelligent load-shedding strategy was designed and embedded in the special protection system (SPS) to enhance the system stability for an offshore island with high penetration of photovoltaic (PV) systems. To prepare the training dataset for the artificial neural network (ANN), the transient stability analysis of the isolated power system was executed to determine the minimum amount of load to be interrupted to prevent the tripping of diesel generators for the emergency shutdown of PV systems. By selecting various combinations of PV penetration levels, total system load demand, and the frequency decay rate at the instant of PV system tripping as the input neurons of the ANN, the proper load-shedding scheme is derived and stored in the decision knowledge base of the SPS. When the intelligent energy management system detects the tripping of a PV system, the SPS will be triggered to determine the amount of loss to be disconnected and executes the corresponding load interruption. By applying the proposed ANN-based load-shedding scheme in SPS, the amount of customer loading to be interrupted has been reduced dramatically for the restoration of system stability after the emergency shutdown of high-penetration PV system.
AN EMPIRICAL INVESTIGATION OF FACTORS AFFECTING THE ACCEPTANCE OF PERSONAL DIGITAL ASSISTANTS BY INDIVIDUALS

Portable computing devices with wireless links are new means for computing and communication. PDAs, cell phones, and portable computers are expected to drive mobile commerce. Among them personal digital assistants (PDAs) are considered to have the most potential. While there is strong indication that PDA adoption is increasing, little systematic research has been done to understand the determinants of PDA adoption and use by individuals. In this study we empirically examine PDA adoption using widely employed Technology Acceptance Model (TAM). The findings of the study show that TAM predicts PDA adoption well.
Singular Value Decomposition-Based Personalized Review Recommendation

With the boom of reviews available in e-commerce websites, it is time-consuming for customers to find their interested reviews. Motivated by this situation, we propose a framework named Rev Rec Sys based on singular value decomposition(SVD) for personalized review recommender systems. Our framework first constructs user-feature matrix and review-feature matrix, then it adopts matrix factorization techniques to compress these two matrices into latent factors, finally it matches a user's latent factor vector space and a review's latent factor vector space to achieve review recommendation. To evaluate the proposed framework, we conduct experiments on a real-life data set. The experimental results report that our method can achieve a better performance than the baseline methods.
Origami Wheel Transformer: A Variable-Diameter Wheel Drive Robot Using an Origami Structure.

Abstract A wheel drive mechanism is simple, stable, and efficient, but its mobility in unstructured terrain is seriously limited. Using a deformable wheel is one of the ways to increase the mobility of a wheel drive robot. By changing the radius of its wheels, the robot becomes able to pass over not only high steps but also narrow gaps. In this article, we propose a novel design for a variable-diameter wheel using an origami-based soft robotics design approach. By simply folding a patterned sheet into a wheel shape, a variable-diameter wheel was built without requiring lots of mechanical parts and a complex assembly process. The wheel's diameter can change from 30 to 68 mm, and it is light in weight at about 9.7 g. Although composed of soft materials (fabrics and films), the wheel can bear more than 400 times its weight. The robot was able to change the wheel's radius in response to terrain conditions, allowing it to pass over a 50-mm gap when the wheel is shrunk and a 50-mm step when the wheel is enlarged.
A Bus Tracking Information System using Consumer Grade GPS: A Case Study

A real-time tracking of public bus movement may help passengers plan their travel and reduce stress due to uncertainty. Several bus tracking information systems have been proposed, but costly to implement and also take longer time to setup. In this paper, a bus tracking information system using standard mobile Internet connection and consumer grade GPS is proposed, called UBAIS (University Bus Arrival Information System). The system will continuously update the estimated arrival time based on the actual location of the buses in the route leading to the next bus stop. With graphical information provided to the passenger via a mobile phone, passengers will be able to view the actual location of the buses in the route. A prototype for Universiti Tun Hussein Onn Malaysia’s bus service has been implemented – consisting of buses equipped with Android devices that will continuously send current GPS locations of the buses to a centralized server and read by clients (fixed or mobile) with various update intervals. Several testing has been performed on the current prototype and it demonstrates good accuracy and increased satisfaction among passengers
Qualité de l'interaction homme machine : interfaces auto-explicatives par ingénierie dirigée par les modèles

In Human-Computer Interaction, quality is an utopia. Despite all the design efforts, there are always uses and situations for which the user interface is not perfect. This thesis investigates self-explanatory user interfaces for improving the quality perceived by end users. The approach follows the principles of model-driven engineering. It consists in keeping the design models at runtime so that to dynamically enrich the user interface with a set of possible questions and answers. The questions are related to usage (for instance, "What's the purpose of this button? ", "Why is this action not possible"? ) as well as to design rationale (for instance, "Why are the items not alphabetically ordered? "). This thesis proposes a software infrastructure UsiExplain based on the UsiXML metamodels. An evaluation conducted on a case study related to a car shopping webiste confirms that the approach is relevant especially for usage questions. Design rationale will be further explored in the future.STAR
A Semi-Supervised Clustering Algorithm for Application Protocol Recognition Based on NetFlow

The traditional protocol recognition algorithms cannot adapt to the current complex and changing network environment,especially in the P2P applications,which use a wide range of dynamic port.Therefore,the limitation of the traditional port classification method,presents the semi-supervised recognition algorithm based on NetFlow.With NetFlow technology,through the sub-space clustering,select the main properties set,using semi-supervised algorithm on NetFlow data for identification application protocol.The results show that semi-supervised clustering algorithm for application protocol recognition base on NetFlow,the measurement accuracy is better than the traditional recognition algorithms to effectively solve the problem of today's complex network protocols recognition.
Two Novel Subthreshold Logic Families for Area and Ultra low-Energy Efficient Applications: DTGDI & SBBGDI

Two novel area and ultra low-energy efficient subthreshold logic families: Dynamic Threshold Gate Diffusion Input (DTGDI) and Swapped Body Bias Gate Diffusion Input (SBBGDI) are introduced. These logic families examine the effectiveness of GDI based circuits over the Conventional CMOS (C-CMOS) logic circuits using subthreshold optimal area overhead free body biasing schemes. The basic logic gates OR, AND and XOR are designed using the proposed DTGDI and SBBGDI logic. To analyze the performance, a full adder cell is implemented. The simulations are performed in Cadence 45nm technology with 0.2V supply voltage. The simulation results show that the proposed DTGDI full adder circuit with layout area of only 6. 891µm 2 offers more than 41% savings in energy ,78% savings in EDP than the Conventional CMOS (C-CMOS) and more than 12% energy savings, 27% savings in EDP than the GDI. Whereas, the SBBGDI full adder circuit with layout area of only 5.654 µm 2 offers more than 47% savings in energy ,90% savings in EDP than the C-CMOS and more than 24% energy savings, 67% savings in EDP than the GDI.
Condensed Nearest Neighbor Rules Based on Rough Set Technique

Nearest neighbor(NN) algorithm is a simple practical supervised classification algorithm.When it classifies an instance without class label,the whole training set must be stored in computer memory,and its distance to each one of the training set is computed.The NN algorithm suffers from a problem:very high requirements of memory space and response time.To overcome this drawback,P.Hart proposed the condensed nearest neighbor rules algorithm,with which a consistent subset(CS) is found from the whole training set(the CS is a subset that can classify all instances in training set correctly). The complexity of the CNN is also very high.Especially,it is extremely expensive to find a CS from a large database.In order to solve this problem,a CNN algorithm based on rough set technique was proposed in this paper,which consists of three stages.Firstly,to remove the superfluous attributes an attribute reduct is computed with rough set method.Secondly,the instances within boundary regions are selected.Meanwhile the redundant instances are removed.Finally,a CS is found from the selected instances.The proposed algorithm can simultaneously reduce data in horizontal and vertical directions.The experimental results show that the proposed method is effective and efficient.
Application of Knowledge Representation in Govenment Administrative System

The conventional methods lack the flexibility and the ability to express human knowledge.By analyzing and categorizing the knowledge elements in the government context,a new model for knowledge representation-three valued neural logic network(Neulonet) is introduced.The duality of Neulonet rule(semantic and numeric features) endow it with great expressive power.Its greatest merit in expressing government knowledge is the capability to represent subjective and regular human knowledge as well as formalized document knowledge,which often co-exist in the routine work of government.
Genome-Wide Identification and Characterization of Long Non-Coding RNAs in Peanut

Long non-coding RNAs (lncRNAs) are involved in various regulatory processes although they do not encode protein. Presently, there is little information regarding the identification of lncRNAs in peanut (Arachis hypogaea Linn.). In this study, 50,873 lncRNAs of peanut were identified from large-scale published RNA sequencing data that belonged to 124 samples involving 15 different tissues. The average lengths of lncRNA and mRNA were 4335 bp and 954 bp, respectively. Compared to the mRNAs, the lncRNAs were shorter, with fewer exons and lower expression levels. The 4713 co-expression lncRNAs (expressed in all samples) were used to construct co-expression networks by using the weighted correlation network analysis (WGCNA). LncRNAs correlating with the growth and development of different peanut tissues were obtained, and target genes for 386 hub lncRNAs of all lncRNAs co-expressions were predicted. Taken together, these findings can provide a comprehensive identification of lncRNAs in peanut.
Rain downpours affect survival and development of insect herbivores: the specter of climate change?

Abstract Changes in the frequency, duration, and intensity of rainfall events are among the abiotic effects predicted under anthropogenic global warming. Heavy downpours may profoundly affect the development and survival of small organisms such as insects. Here, we examined direct (physically on the insects) and indirect (plant‐mediated) effects of simulated downpours on the performance of caterpillars of two lepidopteran herbivores (Plutella xylostella and Pieris brassicae) feeding on black mustard (Brassica nigra) plants. Host plants were exposed to different rainfall regimes both before and while caterpillars were feeding on the plants in an attempt to separate direct and indirect (plant‐mediated) effects of rainfall on insect survival and development. In two independent experiments, downpours were simulated as a single long (20 min) or as three short (5 min) daily events. Downpours had a strong negative direct effect on the survival of P. xylostella, but not on that of P. brassicae. Direct effects of downpours consistently increased development time of both herbivore species, whereas effects on body mass depended on herbivore species and downpour frequency. Caterpillar disturbance by rain and recorded microclimatic cooling by 5°C may explain extended immature development. Indirect, plant‐mediated effects of downpours on the herbivores were generally small, despite the fact that sugar concentrations were reduced and herbivore induction of secondary metabolites (glucosinolates) was enhanced in plants exposed to rain. Changes in the frequency of precipitation events due to climate change may impact the survival and development of insect herbivores differentially. Broader effects of downpours on insects and other arthropods up the food chain could seriously impair and disrupt trophic interactions, ultimately destabilizing communities.