Miracle-dz/UniFAR-FTUs huggingface dataset:
An Association Rule Based PRoPHET (ARBP) Routing Protocol in An Opportunistic Network

An opportunistic network (OppNet) is a type of mobile ad-hoc networks (MANETs), which demand end-to-end communication routes for message exchange. In contrast to MANETs, OppNet is an interconnected wireless network where nodes can be connected without having a predetermined or fixed path, rather this network uses the store-carry-and-forward approach to transmit a message from one node to the destined one through intermediate nodes. Several numbers of routing protocols are proposed to increase the performance and minimize the resource consumptions. In this paper, we have mainly focused on the improvement of traditional Probability Routing Protocol using History of Encounters and Transitivity (PRoPHET) which uses basically the history of transitivity and encounter. Here, we implement a new PRoPHET routing protocol based on the association rule mining technique of machine learning for better delivery, lower overhead, less number of dropped messages,and lower latency, which are desired for any successful communication between nodes. Our proposed approach uses the historical information of the prophet routing protocol and applies to them the association rules to find the best encountering node for good delivery of messages. Overall, our proposed ARBP routing protocol outperforms the existing PRoPHET routing with the consideration of the above mentioned performance metrics.
An Uncertainty Based Predictive Analysis of Smart Water Distribution System Using Bayesian LSTM Approach

A well-designed water distribution system is crucial for maintaining high service standards in any modern smart city. Moreover, as the population is sky-rocketing, the demand for energy and water is increasing more rapidly than a decade before. Therefore, ensuring a steady clean water supply with optimized energy and water consumption has become necessary. To accurately monitor water distribution systems, the accuracy of input data plays a vital role in determining how accurate the system’s status estimations are. There must be a way for system operators to know what is going on at any given time to make practical decisions about how reliable the data they are receiving is. The input data uncertainty can induce flow and pressure calculation inaccuracies, which can be fatal while planning for future demands and needs to be quantified.Knowing the degree of uncertainty in predicting the water distribution system’s capacity or load can help people better prepare for future capacity or load predictions. Accurate uncertainty calculations are critical to time series forecasting. Probabilistic formulae are widely employed with classical time series models to estimate uncertainty. But incorporating new data and fine-tuning these models is a challenging task. This research paper presents a Bayesian LSTM network that computes both time series prediction and uncertainty assessment at the same time. In this paper, a real-time data set from VCU’s OpenCity test bed is employed to evaluate the efficacy of the suggested strategy.
Towards Efficient Shortest Path Counting on Billion-Scale Graphs

Shortest path counting computes the number of shortest paths between two vertices on a graph, which can be used in the applications such as social network search and POI (Point of Interest) recommendation. The state-of-the-art approach leverages index to speed up the query processing. However, this approach incurs not only significant space overheads but also prohibitive indexing time, which makes it inapplicable to handle such queries on large graphs. Motivated by this, in this paper, we aim to propose a new solution to scale up the shortest path counting. To achieve this goal, we first propose a novel size-tunable indexing framework, which allows users to tune the index space consumption based on their requirements for query processing efficiency and available memory. Based on the size-tunable indexing framework, we devise a new parallel paradigm to accelerate index construction. We conduct experiments on 15 real graphs and the experimental results demonstrate that our new approach significantly outperforms the state-of-the-art approach regarding the index space cost and index construction cost, and is able to handle billion-scale graphs that the state-of-the-art approach cannot process with less than 5 milliseconds query processing time on all test cases.
Online Shipping Container Pricing Strategy Achieving Vanishing Regret with Limited Inventory

With the growing demand for global trade transportation, the shipping container market has gained an increasingly important position. As a key issue of the market, container pricing is regarded as an important indicator to adjust the market supply and demand as well as the revenue of liner enterprises. Although various methods aimed at increasing enterprise revenue, such as expert pricing and dynamic pricing, have been proposed by industry and academia in recent years, these approaches rarely yield worst-case performance guarantee for the double-sided online scenarios of commodities and buyers.To cater to the double-sided online scenario and provide theoretical performance guarantee, we propose an online learning-based pricing framework named Balancing Inventory and Revenue with -chasing Decider (BIRD). BIRD determines container price by combining advantages of given multiple online pricing strategies. We utilize a strategy selector A to select a proper target strategy and use an ϵ-chasing decider ${{{\mathfrak{D}}}^{Cha\operatorname{s} ing}}$ to determine the price. BIRD is proven to combine the advantages of multiple online pricing strategies to achieve the performance close to the posterior optimal strategy for any sequence of online buyers on realistic sales platforms with inventory limitation. BIRD is proved to yield a vanishing regret for the online posted pricing problem with the features of limited inventory and multi-unit demand. Based on the historical data provided by COSCO, one of the largest liner enterprises in the world, we experimentally demonstrate the effectiveness of the proposed algorithm.
Correlation of Egg counts, Micro-nutrients, and NDVI Distribution for Accurate Tracking of SCN Population Density Detection

Soybean Cyst Nematode (SCN) is a serious pathogen in soybean production and contributes to annual economic losses of more than $1.5 billion (1996–2016) in the U.S. SCN is a microscopic thread-like nematode that burrows into the roots of soybean plants and typically cannot be identified above ground. The paper investigates multitude of variables such as NDVI from multi-spectral images, egg counts, and micro-nutrient composition (e.g., pH, nitrogen, phosphorus, potassium) across two SCN-prone field plots in Casselton/Prosper, North Dakota. The preliminary results indicate that NDVI is a good metric to track for SCN density population during planting, growing, and harvesting periods along with other historical ground truth data. Also, a contour plot using Empirical Bayesian Kriging (EBK) was designed by integrating NDVI and egg count data for co-tracking distribution changes. Such access to ground truth data (i.e., aerial and soil properties) will enable the development and training of robust machine learning models for predicting SCN hotspots.
Modelling and Simulation of a fully Electrified Urban Private Transport - a case Study for Berlin, Germany

In order to identify congestions in the electric grid due to the electrification of private internal combustion engine vehicles (ICEVs), a method is developed that allows forecasting the spatial and temporal distribution of charging energy demand resulting from the electrification of private ICEV s with high spatial and temporal resolution. The main database of the method is OpenStreetMap geodata which is freely available worldwide. The demonstration example of this work is Berlin, Germany, assuming that all private cars are replaced by battery electric vehicles (BEVs). The results show that more than 6000 MWh of charging energy is required on an average workday, with 62% of this energy being charged at residences. The results obtained serve as a basis for further investigations. It is found that through load shifting, vehicle charging times can be shifted to reduce peak power demand in the districts by up to 28%, without changes in the mobility behaviour. A full vehicle-to-grid (V2G) capability, where excess renewable energy is stored in BEV s and fed back into the grid as needed, is also analyzed. If V2G is deployed, more than 99% of household and BEV energy demand can be met with renewable energy if 30% of vehicles participate in V2G.
Use of Convolutional Neural Networks and Long Short-Term Memory for Accurate Residential Energy Prediction

With the deployment of smart meters on the residential level, consumers now possess more options for controlling the electrical consumption of their electrical appliances. So, consumers can better plan for and control how much electricity they use if they know how much electricity they use every day. Today's electrical systems must properly estimate consumer energy use, which can lead to a better understanding of the actual power consumption patterns that consumers experience. This paper addresses methodologies based on machine learning tools used to improve electrical system load forecasting by applying Long Short-Term Memory and Convolutional Neural Networks on a dataset containing 2 months, (i.e. from 1-1-2022 to 1-3-2022), of six-second regularly spaced measurement samples obtained from a lab designed smart meter placed in a residential house. This study also looks at how well the proposed LSTM-CNN model can predict home consumption based on data from two months.
MLOps meets Edge Computing: an Edge Platform with Embedded Intelligence towards 6G Systems

The evolution towards more human-centered 6G networks requires the extension of network functionalities with advanced, pervasive automation features. In this direction, cloud-native, softwarized network functions and integration of extreme/far edge devices shall be supported by even more distributed and decomposable systems, such as Edge Cloud environments, while building on AI/ML data-driven mechanisms to improve their performance and resilience for the stringent requirements of next-generation applications. In this work, we propose an intelligence-native Edge Management Platform coupled with MLOps functionalities-the $\pi$-Edge Platform-which encompasses automation features for cloud-native lifecycle management of Edge Services. Our introduced architecture incorporates MLOps services and processes, operating as integrated micro-services with the rest of the $\pi$-Edge architectural components, ensuring the reliable operation and QoS of Edge network and application services. We experimentally validate our approach with a prototypical implementation of key $\pi$-Edge features, including the incorporation of state-of-the-art ML models for performance prediction and anomaly detection, on a multi-media streaming use case based on scenarios from real production environment.
Energy and angular momentum control of robot running

A new approach for running by exploiting passive elastic dynamics is addressed in this paper. A control method based on energy and angular momentum regulation is proposed. The controller exploits invariance properties of energy and angular momentum to achieve stabilization of passive gaits with almost zero control effort after convergence. The passive gaits are derived based on a modified version of the well-known SLIP model, which takes into account the pitch dynamics during the flight phase, which is otherwise disregarded by the conventional SLIP model. Based on this model, the interesting phenomenon of running in presence of persistent somersaulting is investigated and persistently-somersaulting running gaits are identified and analyzed. Numerical simulations investigate the method and confirm the effectiveness in ideal conditions.
Preliminary Exploration and Evaluation of Smart Support for Homeless Community

Nowadays, the homeless situation is getting worse especially under the pandemic. The rates of homelessness in the United States have expanded by over 130 percent in just the past two years. Our society has been highly changed and our life quality has significantly improved thanks to technologies like Artificial Intelligence in nearly a decade. However, for people experiencing homelessness, and other such vulnerable groups are under social circumstances lacking humanistic consideration and facing moral issues. Over the past few years, many policies, technologies and approaches have been developed in directions like developing information-collecting platforms, building and classifying emergency shelters and creating robots dedicated to solving comprehensive problems to help people overcome the influences of poverty, illness and unaffordable housing that lead to homelessness, but the actions are never enough because of the partial absence of their focus on user experience, insufficient scope of application and the difficulty of promotion. Under this comprehensive circumstance of the crisis of homelessness, in this paper, we explore homeless status, difficulties and both internal and external challenges with their actual need and various technical solutions. More importantly, we are trying to analyze reasons for not being able to help homeless solve problems and evaluate breakthroughs in application and promotion, summarizing existing technologies and future innovations, proposing possible direction of improvement with a perspective of promoting sustainable development, universal design, and communicative action to help the homeless in various situations.
Optimal D2D Learning-Based Neighbor Selection in mmWave Networks using Gittins Indices

Device-to-Device (D2D) communication helps in increasing the coverage range and throughput in millimeter Wave (mmWave) networks. The performance of the mmWave D2D communication mainly depends on selecting the best neighbor device through a process of Beamforming Training (BT). The process of BT involves pointing beams of multiple resolutions at different angles to select the best neighbor device in terms of link quality. While the sender exhaustively performs BT with all the neighbors in naive BT, the Reinforcement Learning (RL) based techniques, on the other hand, employ exploration/exploitation strategies to intelligently search for the best neighbor.The state-of-the-art RL algorithms typically yield sub-optimal performance, by trading-off computational tractability over optimality. Tractable optimal solving methods are therefore critical in improving the performance of neighbor selection through an intelligent BT process. To this end, this work studies the problem structure of the mmWave BT neighbor selection process formulated as a Multi-Armed Bandit (MAB) problem and provides a simple and scalable optimal method to select mmWave D2D neighbors with the objective of maximizing throughput performance. With extensive simulations, the efficacy of the proposed framework is demonstrated.
The Role of Software Architecture in Business Model Transformability

The lifespan of business models is continuously shrinking, and consequently, businesses are facing the need to renovate their business models much more frequently than ever before. With a “stale” business model, a business becomes at risk of being unable to sustain itself; let alone reap the benefits new opportunities may bring. This paper presents qualitative exploratory research that aims to identify what constitutes business model transformability and explore the effect of software architecture in this regard. The research uses a grounded theory approach to construct a substantive theory, particularly a code system that IT -based organizations can reference. The study uses theoretical sampling and the constant comparative method to identify required participants and analyze interview data. The resulting code system identifies thirteen main categories, one of which is Transformative Software Architecture, and delves further into its subcategories.
Studying Secure Coding in the Laboratory: Why, What, Where, How, and Who?

Software security is an area of growing concern, with over 192,000 known vulnerabilities in public software at the time of writing. Many aids to secure coding exist. Assessing the effectiveness of such aids in a laboratory environment is difficult. There are a number of concerns to address, such as recruitment issues and the level of instrumentation needed to perform an accurate measurement. Based on an extensive literature review of software development aids, we describe recent approaches to running laboratory studies, their characteristics, and their benefits and drawbacks. This paper should be of use to anyone planning to undertake coding studies with software developers.
5G Radio Resource Allocation for Communication and Computation Offloading

Edge computing is envisioned as a key enabler in future cellular networks by bringing the computing, networking and storage resources closer to the end users and enabling offloading for computation-intensive or latency-critical tasks coming from the emerging 5G/6G applications. Such technology also introduces additional challenges when it comes to deciding when to offload or not since the dynamic wireless environment plays a significant role in the overall communication and computation costs when offloading workload to the nearby edge nodes. In this paper, we focus on the communication cost in computation offloading via wireless channels, by formulating an $\alpha$-fair utility-based radio resource allocation (RRA) problem tailored for offloading in a multi-user urban scenario where the uplink connection is the main focus. We begin by modeling the wireless channel with large- and small-scale fading at both lower and millimetre-wave frequencies, followed by data rate calculation based on 3GPP for a more realistic approach. Then, while assessing the fairness of the RRA, we simulate the resource allocation framework while taking into account both users who need to offload and users who are only interested in high downlink data rates. Simulation results show that the weighted proportional fairness method adapted for computation offloading can provide a good trade-off between fairness and performance compared to other benchmark schemes.
Slow Rolling of Multiple Pump Motors at a Large Petrochemical Complex

Slow rolling of rotating equipment has been used for decades to prevent rotating parts such as shafts of large electric turbines, motors, pumps, and compressors from sagging or bending when not in operation. In industrial facilities, such as large petroleum processing plants, slow rolling is often used with stand-by pumps to prevent pump casing distortion and shaft bending. This paper covers an industrial application of a slow rolling scheme for up to two motors driven simultaneously utilizing a single Adjustable Speed Drive (ASD). The control system has been fully automated using a Programmable Logic Controller (PLC) interfaced with an upstream Distributed Control Systems (DCS). The scheme provides a cost-effective solution for multi-motor applications by reducing the number of ASDs required, minimizing the required footprint in the substation, improving energy efficiency by eliminating electric power conversion losses, and reducing the overall installation costs. Attention will be given to the control systems architecture, automated modes of operation, electrical configuration, and mechanical performance.
Optimal Location of PMUs to Enhance State Estimation of Power Systems

The Wide Area Monitoring Systems (WAMs) has brought several advantages for the power system monitoring, operation, control. Recently, the introduction of Phasor Measurement Units (PMUs) to power grids played a significant role in transforming them into smart grids. However, PMUs are costly and installing PMU at each bus is feasible. Therefore, the formulation of optimal placement of PMUs (OPP) in power systems is very important to minimize the total number, while preserving the observability of the system. Newton Raphson load flow method is applied to obtain load flow results of the system. Weighted least squares WLS with PMUs approaches for the state estimation of IEEE 14 bus test case is presented. The results show more accurate state estimation process.
Adaptive Risk Sensitive Path Integral for Model Predictive Control via Reinforcement Learning

We propose a reinforcement learning framework where an agent uses an internal nominal model for stochastic model predictive control (MPC) while compensating for a disturbance. Our work builds on the existing risk-aware optimal control with stochastic differential equations (SDEs) that aims to deal with such disturbance. However, the risk sensitivity and the noise strength of the nominal SDE in the riskaware optimal control are often heuristically chosen. In the proposed framework, the risk-taking policy determines the behavior of the MPC to be risk-seeking (exploration) or risk-averse (exploitation). Specifically, we employ the risk-aware path integral control that can be implemented as a Monte-Carlo (MC) sampling with fast parallel simulations using a GPU. The MC sampling implementations of the MPC have been successful in robotic applications due to their real-time computation capability. The proposed framework that adapts the noise model and the risk sensitivity outperforms the standard model predictive path integral in simulation environments that have disturbances.
IFCA: Index-Free Community-Aware Reachability Processing Over Large Dynamic Graphs

Reachability is a fundamental graph operator. State-of-the-art index-based reachability processing frameworks can efficiently handle static graphs, but the recent advent of dynamic graph data poses new challenges. To address these challenges, we propose an index-free, community-aware (IFCA) reachability processing framework inspired by efficient Personalized PageRank approximation algorithms, which identifies community structures on-the-fly to accelerate query processing. On top of it, we devise a community contraction technique to bridge the gap between vertices in distinct communities, and a cost-based strategy selection procedure to efficiently handle the resulting reduced graph. We conduct experiments with realistic query workloads over large-scale real dynamic graphs, showing our approach’s superior efficiency compared with index-based and index-free state-of-the-art methods.
Personalized LSTM-based alarm systems for hypoglycemia prevention

Hypoglycemia prevention is one of the main challenges of an efficient Type 1 diabetes control. Alarm Systems (ASs) that alert the patients about upcoming hypoglycemia are useful instruments to act in advance and avoid it. Model-based ASs use patient models to predict future Blood Glucose (BG) levels and to activate alarms, so model prediction capabilities highly influence the AS performance. In recent studies, neural network techniques for BG forecasting obtained promising results, both as population and personalized models. In particular, Personalized Long Short-Term Memory models (P-LSTMs) for BG predictions obtained good results in literature on the 100 in silico patients of the UVA/Padova simulator. In this work, personalized ASs for hypoglycemia prevention based on PLSTMs are proposed. The ASs are able to predict correctly the 79% of the hypoglycemia events with a precision of 87%. These performances could be improved for some critical patients that present poor prediction performances. An enhanced version of the P-LSTM is currently under study.