Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement on LEACH Protocol in Wireless Sensor Networks

Wireless sensor network (WSN) is a network consists of large number of low power sensor nodes. Leach is a less energy adaptive clustering hierarchy protocol. The main goal of cluster based sensor networks is to decrease system delay and reduce energy consumption. Leach is a cluster based protocol for micro sensor networks which achieves energy efficient, scalable routing and fair media access for sensor nodes. Many improvements are done in wireless sensor network. Security is very essential in wireless sensor network. This paper describes LEACH protocol, their advantages, disadvantages etc. The paper is organized as follows: In section I, contains introduction, section II contains description of LEACH protocol, and section III contains literature review
Tool for the Analysis of Human Interaction with Two-Dimensional Printed Imagery

The study of human vision can include our interaction with objects. These studies include behavior modeling, understanding visual attention and motor guidance, and enhancing user experiences. But all these studies have one thing in common. To analyze the data in detail, researchers typically have to analyze video data frame by frame. Real world interaction data often comprises of data from both eye and hand. Analyzing such data frame by frame can get very tedious and time-consuming. A calibrated scene video from an eye-tracker captured at 120 Hz for 3 minutes has over 21,000 frames to be analyzed. Automating the process is crucial to allow interaction research to proceed. Research in object recognition over the last decade now allows eye-movement data to be analyzed automatically to determine what a subject is looking at and for how long. I will describe my research in which I developed a pipeline to help researchers analyze interaction data including eye and hand. Inspired by a semi-automated pipeline for analyzing eye tracking data, I have created a pipeline for analyzing hand grasp along with gaze. Putting both pipelines together can help researchers analyze interaction data. The hand-grasp pipeline detects skin to locate the hands, then determines what object (if any) the hand is over, and where the thumbs/fingers occluded that object. I also compare identification with recognition throughout the pipeline. The current pipeline operates on independent frames; future work will extend the pipeline to take advantage of the dynamics of natural interactions.
Multi-step recovery strategy for humanoid robots using model predictive control

Humanoid robots in any environment are likely to experience collisions with obstacles or imbalance while attempting to navigate or perform a task. While humans are quite capable of balancing when encountering moderate collisions and imbalance, humanoid robots have yet to master the same skill. Push recovery is a strategy to maintain an upright posture in a robot while subjected to disturbances or imbalance. Performing push recovery on a humanoid robot consists of planning the actuation of the joints, ground reaction forces, and footsteps. Controlling a robot’s trajectory has many challenges such as incorporating nonlinear dynamic equations, achieving resilience to disturbances, and maintaining computational eﬃciency. This paper introduces a control framework that determines the necessary actuated joint forces and footsteps in order to balance. The proposed control framework is distinct from existing Capture Point/Capture Region solutions in that the controller simultaneously plans multiple future footsteps and all actuated joint forces. Model Predictive Control provides the necessary planning and optimization to anticipate the robot’s future trajectory and accommodate imbalance. Combining the eﬀort of all actuators and multiple footsteps into one recovery strategy has yet to be implemented in push recovery of humanoid robots. This control framework is demonstrated with a bipedal walking robot model and the performance is validated with simulation results.
Optimizing the copy-on-write mechanism of docker by dynamic prefetching

Docker, as a mainstream container solution, adopts the Copy-on-Write (CoW) mechanism in its storage drivers. This mechanism satisfies the need of different containers to share the same image. However, when a single container performs operations such as modification of an image file, a duplicate is created in the upper readwrite layer, which contributes to the runtime overhead. When the accessed image file is fairly large, this additional overhead becomes non-negligible. Here we present the concept of Dynamic Prefetching Strategy Optimization (DPSO), which optimizes the CoW mechanism for a Docker container on the basis of the dynamic prefetching strategy. At the beginning of the container life cycle, DPSO pre-copies up the image files that are most likely to be copied up later to eliminate the overhead caused by performing this operation during application runtime. The experimental results show that DPSO has an average prefetch accuracy of greater than 78% in complex scenarios and could effectively eliminate the overhead caused by the CoW mechanism.
Multi-Objective Optimization of Turning Process during Machining of AISI 1025 on CNC machine Using Multi-objective particle swarm optimization

Parametric optimization of the transformation process is a task of improving multiple objectives. In general, no single set of input parameters can provide the best hardness of the metal and the best finish of the surface at the same time. While when using the genetic algorithm proved that it is possible to obtain more than one goal of the function at the same time. In the current research the use of Multi-objective particle swarm optimization (MOPSO) to determine the best hardness of the metal and less roughness of the surface operator at the same time. A mathematical model was also developed to predict the hardness of the metal and surface roughness using Taguchi technology depending on the input variables (cutting speed, cutting depth and feeding rate). Experiments were conducted on a CNC machine in turning carbon steel AISI 1025 using carbide cutting tool. It was found that the most influential factor on the surface roughness is the feeding rate, while the most important effect on the hardness is the cutting speed.
MM-DSL, support for implementing modelling tools for manufacturing processes

Today’s competitive conditions call for detailed comparative analyzes of manufacturing processes in order to get competitive products. This analysis involves the development of faithful and robust models for the supervision and management of all organizational and operational activities of companies. Efficient modelling involves the selection and use of appropriate tools for modelling, simulation and analysis of manufacturing processes. The diversity of manufacturing processes often makes it necessary to implement specific modelling tools. MM-DSL is a platform independent language for specifying and implementing specific modelling tools. The core objective of the MM-DSL language is the implementation of the modelling method concept. The paper presents the mechanisms underlying the MM-DSL language as well as its use for building the modelling tools specific to the manufacturing systems.
Work Orders - Value from Structureless Text in the Era of Digitisation

Free text and hand-written reports are losing ground to digitization fast, however many hours of effort are still lost across the industry to the manual creation and analysis of these data types. Work orders in particular contain valuable information from failure rates to asset health, but at the same time present operators with such analytical difficulties and lack of structure that many are missing out on the value completely. This research challenges the current mainstream practice of manual work order analysis by presenting a methodology fit for today’s context of efficiency and digitization. A prototype text mining software for work order analysis was developed and tested in a user-oriented approach in cooperation with industrial partners. The final prototype combines classical machine learning methods, such as hierarchical clustering, with the operator’s expert knowledge obtained via an active learning approach. A novel distance metric in this context was adapted from information-theoretical research to improve clustering performance. Using the prototype tool in a case study with real work order data, analytical effort for certain datasets was reduced by 90% - from two working weeks to a day. In addition, the active learning framework resulted in an approach that end users described as "practical" and "intuitive" during testing. An in-depth review was also conducted regarding the uncertainty of the results – a key factor for implementation in a decision-making context. The outcomes of this work showcase the potential of machine learning to drive the digitization of not only new installations, but also older assets, where as a result the large amount of unstructured historical data becomes an advantage rather than a hindrance. User testing results encourage a wider uptake of machine learning solutions in the industry, and particularly a shift towards more accessible in-house analytical capabilities.
A Game Theoretic Approach for Mobility Prediction Clustering in Unmanned Aerial Vehicle Networks

Unmanned aerial vehicle (UAV) swarms have attracted great interests in numerous civil and military scenarios due to their flexible deployment and mobility. The increased network size poses a challenge for the network scalability. Clustering as an effective scheme, is able to provide efficient communication and network performance. In order to adapt to the change of network topology, the link subsistence probability is used to evaluate the mobility of UAVs. However, the UAVs usually perform tasks collaboratively, which leads to their movements are typically group-based. To this end, a coalition game theoretic framework is proposed to cluster UAVs into coalitions in a distributed autonomous manner, based on the group and the mobility information. The proposed game allows the UAVs with the same group, as much as possible, into one coalition under some constraints, such as cluster size and cluster diameter. To solve the game, each UAV makes its decision whether to switch based on the coalition value. Finally, some comparisons are provided to illustrate the efficiency of the proposed algorithm.
Automated Open Circuit Scuba Diver Detection with Low Cost Passive Sonar and Machine Learning

This thesis evaluates automated open-circuit scuba diver detection using low-cost passive sonar and machine learning. Previous automated passive sonar scuba diver detection systems required matching the frequency of diver breathing transients to that of an assumed diver breathing frequency. Earlier work required prior knowledge of both the number of divers and their breathing rate. Here an image processing approach is used for automated diver detection by implementing a deep convolutional neural network. Image processing was chosen because it is a proven method for sonar classification by trained human operators. The system described here is able to detect a scuba diver from a single acoustic emission from the diver. Twenty dives were conducted in support of this work at the WHOI pier from October 2018 to February 2019. The system, when compared to a trained human operator, correctly classified approximately 93% of the data. When sequential processing techniques were applied, system accuracy rose to 97%. This demonstrated that a combination of lowcost, passive sonar and a properly tuned convolutional neural network can detect divers in a noisy environment to a range of at least 12.49 m (50 feet). Thesis Supervisor: Carl Kaiser Title: Program Manager Woods Hole Oceanographic Institution Thesis Supervisor: Andone Lavery Title: Senior Scientist Woods Hole Oceanographic Institution
Eye&Head: Synergetic Eye and Head Movement for Gaze Pointing and Selection

Eye gaze involves the coordination of eye and head movement to acquire gaze targets, but existing approaches to gaze pointing are based on eye-tracking in abstraction from head motion. We propose to leverage the synergetic movement of eye and head, and identify design principles for Eye&Head gaze interaction. We introduce three novel techniques that build on the distinction of head-supported versus eyes-only gaze, to enable dynamic coupling of gaze and pointer, hover interaction, visual exploration around pre-selections, and iterative and fast confirmation of targets. We demonstrate Eye&Head interaction on applications in virtual reality, and evaluate our techniques against baselines in pointing and confirmation studies. Our results show that Eye&Head techniques enable novel gaze behaviours that provide users with more control and flexibility in fast gaze pointing and selection.
Management Organizational Complexity in the Background Multi - Projects

To be effective in running multiple concurrent projects and achieving their strategic business objectives, project-oriented companies need to have adequate information about the challenges they face in managing them. The study conducted as part of this research work borrows a descriptive path and contributes to the improvement of the understanding of the different users of what is the multi-project management. These challenges include project selection; the allocation of resources; the prioritization of projects and the management of project teams. It is thus necessary to educate the project management community regarding the specifics of multi-project management and the organizational complexities while developing new tools which will permit organizations faced with the above identified challenges to adequately adapt in an increasingly complex environment. In addition to these comments, this study extends its contribution by presenting proposals for future research and resolution of the problem of project selection, project prioritization and the composition of a project portfolio.
Assessing the Readiness of Academia in the Topic of False and Unverified Information

The spread of false and unverified information has the potential to inflict damage by harming the reputation of individuals or organisations, shaking financial markets, and influencing crowd decisions in important events. This phenomenon needs to be properly curbed, otherwise it can contaminate other aspects of our social life. In this regard, academia as a key institution against false and unverified information is expected to play a pivotal role. Despite a great deal of research in this arena, the amount of progress by academia is not clear yet. This can lead to misjudgements about the performance of the topic of interest that can ultimately result in wrong science policies regarding academic efforts for quelling false and unverified information. In this research, we address this issue by assessing the readiness of academia in the topic of false and unverified information. To this end, we adopt the emergence framework and measure its dimensions (novelty, growth, coherence, and impact) over more than 21,000 articles published by academia about false and unverified information. Our results show the current body of research has had organic growth so far, which is not promising enough for confronting the problem of false and unverified information. To tackle this problem, we suggest an external push strategy that, compared to the early stages of the topic of interest, reinforces the emergence dimensions and leads to a higher level in every dimension.
System load trend prediction method based on IF-EMD-LSTM

Accurately predicting the load change of the information system during operation has important guiding significance for ensuring that the system operation is not interrupted and resource scheduling is carried out in advance. For the information system monitoring time series data, this article proposes a load trend prediction method based on isolated forests-empirical modal decomposition-long-term (IF-EMD-LSTM). First, considering the problem of noise and abnormal points in the original data, the isolated forest algorithm is used to eliminate the abnormal points in the data. Second, in order to further improve the prediction accuracy, the empirical modal decomposition algorithm is used to decompose the input data into intrinsic mode function (IMF) components of different frequencies. Each intrinsic mode function (IMF) and residual is predicted using a separate long-term and short-term memory neural network, and the predicted values are reconstructed from each long-term and short-term memory model. Finally, experimental verification was carried out on Amazon’s public data set and compared with autoregressive integrated moving average and Prophet models. The experimental results show the superior performance of the proposed IF-EMD-LSTM prediction model in information system load trend prediction.
Benefits of triple-layer remote phosphor structure in improving color quality and luminous flux of white LED

Remote phosphor structure has higher luminous efficiency comparing to that of both conformal phosphor and in-cup phosphor structures. However, it is hard to control the color quality of remote phosphor structure, and this issue has become one of the most researchable objectives to many researchers in recent years. Up to now, there are two remote phosphor structures applied to improve the color quality, including dual-layer phosphor configuration and triple-layer phosphor configuration. The purpose of this research is to select one of those configurations to have multi-chip white LEDs (WLEDs) achieved the highest color rendering index (CRI), color quality scale (CQS), luminous efficacy (LE), and color uniformity. In this research, WLEDs with two correlated color temperatures (CCT) of 6600K and 7700K were applied. The obtained results showed that triple-layer phosphor configuration is more outstanding in CRI, CQS, and LE. Moreover, the color deviation has been significantly reduced, which means the color uniformity has been enhanced with the application of triple-layer phosphor configuration. These results can be proven by scattering properties of phosphor layers based on Mie theory. Thus, the researched results have become a reliable and valuable reference for manufacturing higher-quality WLEDs.
How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?

We investigate the moderating effect of product attributes and review ratings on views, conversion|views (conversion conditional on views), and final conversion of a purchase-based collaborative filtering recommender system on an e-commerce site. We run a randomized field experiment on a top retailer with 184,375 users split into a recommender-treated group and a control group. We tag theory-driven attributes of 37,125 unique products via Amazon Mechanical Turk to augment the usual product data (e.g., review ratings, descriptions). By examining the recommender’s impact through different stages—awareness (views), salience (conversion|views), and final conversion—and across product types, we provide nuanced insights. The study confirms that the recommender increases views, conversion|views, and final conversion rates by 15.3%, 21.6%, and 7.5%, respectively, but this lift is moderated by product attributes and review ratings. We find that the lift on product views is greater for utilitarian products compared with hedonic products as well as for experience products compared with search products. In contrast, the lift on conversion|views rate is greater for hedonic products compared with utilitarian products. Furthermore, the lift on views rate is greater for products with higher average review ratings, which suggests that a recommender acts as a complement to review ratings, whereas the opposite is true for conversion|views, where recommender and review ratings are substitutes. Additionally, a recommender’s awareness lift is greater than its saliency impact. We discuss the potential mechanisms behind our results as well as their managerial implications. This paper was accepted by David Simchi-Levi, information systems.
Minimum Cost Design of Cellular Networks in Rural Areas With UAVs, Optical Rings, Solar Panels, and Batteries

Bringing the cellular connectivity in rural zones is a big challenge, due to the large installation costs that are incurred when a legacy cellular network based on fixed Base Stations (BSs) is deployed. To tackle this aspect, we consider an alternative architecture composed of UAV-based BSs to provide cellular coverage, ground sites to connect the UAVs with the rest of the network, Solar Panels (SPs) and batteries to recharge the UAVs and to power the ground sites, and a ring of optical fiber links to connect the installed sites. We then target the minimization of the installation costs for the considered UAV-based cellular architecture, by taking into account the constraints of UAVs coverage, SPs energy consumption, levels of the batteries and the deployment of the optical ring. After providing the problem formulation, we derive an innovative methodology to ensure that a single ring of installed optical fibers is deployed. Moreover, we propose a new algorithm, called DIARIZE, to practically tackle the problem. Our results, obtained over a set of representative rural scenarios, show that DIARIZE performs very close to the optimal solution, and in general outperforms a reference design based on fixed BSs.
Computing strategy for PID calibration samples for LHCb Run 2

The samples for the calibration of the Particle Identification (PID) are sets of data collected by LHCb where decay candidates have a kinematic structure which allows unambiguous identification of one the daughters with a selection strategy not relying on its PID-related variables. PID calibration samples are used in a data-driven technique to measure the efficiency of selection requirements on PID variables in many LHCb analyses. Since the second run of the LHCb experiment, PID variable are computed as part of the software trigger and then refined in the offline reconstruction (this includes two different applications, named Brunel and DaVinci). Physics analyses rely on selections combining PID requirements on the online and offline versions of the PID variables. Because of the large, but not full, correlation between online and offline PID variables, the PID samples must be built such that it is possible to access, particle by particle, both versions of the full PID information, such that combined requirements on the offline and online versions can be defined. The only viable solution is to write the PID Calibration samples to a dedicated stream where the information evaluated in the trigger (the online version) is stored together with the full raw event, which is then reconstructed and processed offline. A dedicated algorithm allows to match online and offline candidates and to produce datasets to be used as input for the package that allows analysts to access the calibration samples. This note is focused on PID Calibration samples, but the general layout should be applied to any other calibration sample in Run 2.