Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving stream flow estimation by incorporating time delay approach in soft computing models

ABSTRACT In the present study, Artificial Neural Networks (ANNs) and Genetic Programming (GP) soft-computing models were established to estimate streamflow in Tel River Basin, Odisha, India. In most of the previous time-series soft computing models/studies, the time delay or basin lag has been considered without any physical basis for streamflow estimation/forecasting. Considering these limitations, the 02 isochrones (zones) were delineated up to Kesinga Station based on its topography for incorporating the time delay phenomenon. The monsoon rainfall of the period of 1998–2011 (14 years) in each zone was estimated and delayed as per their respective time-delay zone and fed into both the models for discharge estimation. It was observed that after incorporating the time delay factor, the accuracy of streamflow estimation increased for both the models. However, the ANNs could not map the peak discharge and the model efficiency was less as compared to the GP model. The best GP model achieved an efficiency of 74%, where ANNs could reach up to only 59%. After establishing the soft computing based discharge estimation model for the basin, the flood inundation between Kesinga and Kantamal stations was studied using the hydrodynamic model for the estimated peak discharge.
Blockchain based transactive energy systems for voltage regulation in active distribution networks

Transactive Energy Systems (TES) are modern mechanisms in electric power systems that allow disparate control agents to utilize distributed generation units (DGs) to engage in energy transactions and provide ancillary services to the grid. Although voltage regulation is a crucial ancillary service within active distribution networks (ADNs), previous work has not adequately explored how this service can be offered in terms of its incentivization, contract auditability and enforcement. Blockchain technology shows promise in being a key enabler of TES, allowing agents to engage in trustless, persistent transactions that are both enforceable and auditable. To that end, this paper proposes a blockchain based TES that enables agents to receive incentives for providing voltage regulation services by i) maintaining an auditable reputation rating for each agent that is increased proportionately with each mitigation of a voltage violation, ii) utilizing smart contracts to enforce the validity of each transaction and penalize reputation ratings in case of a mitigation failure and iii) automating the negotiation and bidding of agent services by implementing the contract net protocol (CNP) as a smart contract. Experimental results on both simulated and real-world ADNs are executed to demonstrate the efficacy of the proposed system.
Decorrelated Loss Recovery (DCLOR) Using SACK Option for Spurious Timeouts

A spurious timeout in TCP forces the sender to unnecessarily retransmit one complete congestion window of data into the network. In addition, the congestion state of the network could change substantially after a spurious timeout. In this draft we propose a conservative congestion response algorithm afert spurious timeout that takes network state into account.
Application et exploration de l’approche de découpage des systèmes de recommandations pré-contextuelle.

Conventional Recommendation Systems (SR) offer a solution to the increasing amount of content produced each day. They provide users with content that is most relevant to them, but neglecting the current user context. Hence the emergence of context-aware recommendation systems "CARS" that integrates contextual information into a recommendation. In our end-of-study project, we aim to implement one of the most effective approaches in the CARS namely: Splitting approach. Thus, we explore the CARSKits library implementing the majority of state-of-the-art CARS algorithms. CARSKits is very useful for the evaluation of future recommendation algorithms proposed and more particularly those sensitive to the context.
Analysis of Constrained Random WayPoint Mobility Model on Graph

Graph traversal is one of fundamental problems in discrete mathematics since it has a broad range of applications in diverse research areas such as computer science and information networking. In the last decade, the characteristics of random-walk-based mobility models have been extensively studied by many researchers through mathematical analyses as well as computer simulations. One of the popular mobility models on a graph is the Constrained Random WayPoint (CRWP) mobility model, which is a natural extension of the Random WayPoint (RWP) mobility model. It has been known that the RWP mobility model on a continuous space has many favorable properties (e.g., fast mixing time and wide range of positional distribution) over random-walk-based mobility model on a continuous space. The RWP mobility model has always a specific goal so that the agent is likely to deviate from the current position. Such deviating property of the RWP mobility model has positive impact on many applications. However, to the best of our knowledge, the superiority of such mobility model on a graph has not been well understood. In this paper, we derive the two major indices of the CRWP mobility model on an arbitrary graph G (i.e., the recurrence time and the first hitting time). Furthermore, through several numerical examples, we investigate the effect of the structure of a graph on the characteristics of the CRWP mobility model. Our findings include that the characteristics of the CRWP mobility model are determined by betweenness centralities of nodes.
Zur Numerik großdimensionaler Eigenwertprobleme

Eigenwerten großdimensionaler reeller Matrizen vor. Neben der Vektoriteration und der inversen Iteration f¨ur das partielle Eigenwertproblem wird der QR-Algorithmus zur L¨osung des vollst¨andigen Eigenwertproblems behandelt. F¨ur sehr große Matrizen ist man an einer Lokalisierung des Spektrums interessiert, wof¨ur Ritz-Werte und Petrov-Werte ermittelt und an Beispielen demonstriert werden. 65F50, 65F10
Dynamic Wireless Charging System for Electrical Vehicle

: Dynamic remote charging (DWC) is a rising innovation that empowers the batteries of electric vehicles (EVs) to charge naturally while the vehicles are in movement. The DWC-EV framework tends to the difficulties innate in battery innovation, for example, the short driving extent, long reviving time, and high cost. Contrasted and ordinary module EVs, the DWC-EV can charge a battery all the more every now and again on the grounds that it tends to be done while the EV is in movement from the charging foundation introduced out and about. A double circle essential controller is proposed to direct essential side power and current. The controller permits consecutive and auspicious enactment of portioned essential loops; it controls the essential curl current at the reference an incentive under no-heap and stacked conditions, makes up for power move decrease brought about by the vehicle sidelong misalignment, and anticipates essential over-burdening.
A semi‐oblivious approach for robust segment routing in software‐defined wide area networks

In this paper, we have proposed a robust optimization for offline segment routing in software‐defined wide area networks combining traffic‐aware and traffic‐oblivious methods. Most prior works on segment routing assume accurate estimation of the traffic matrix, which is difficult to obtain due to the measurement costs and wide variations in traffic matrix over time. To deal with traffic uncertainties, an oblivious segment routing technique was previously developed. However, since all possible traffic fluctuations are considered, this leads to a considerable loss of routing performance in normal conditions. Since the traffic usually fluctuates around a normal pattern within a certain lower/upper bound, in this paper, we propose a semi‐oblivious segment routing method that optimizes routing for a predicted probable traffic matrix, while ensuring its robustness by guarantying the worst‐case performance under the fluctuations. Prior works also considered 2‐segment routing that is demonstrated to lead to routing deficiencies in some cases. We have extended the formulation to include 3‐Segment Routing (3‐SR) and using the strong duality theorem, the proposed semi‐oblivious 3‐SR method is formulated as a linear programming problem with an objective to maximize network throughput. We envision to apply this scheme in software‐defined networks where the controller is responsible to compute robust segment routing paths and to configure the edge routers accordingly. Simulation results show that the proposed method, while being robust to traffic fluctuations, significantly outperforms the traffic oblivious methods in normal network conditions and also achieves comparable performance to the models with known traffic matrix.
Frequency and clinical pattern of autonomic neuropathy in adult diabetic Sudanese patients

Diabetic autonomic neuropathy can cause cardiac, gastrointestinal, genitourinary and metabolic disorders. Strict glycemic control can slow the onset of diabetic autonomic neuropathy and sometimes reverse it. The study was aimed at investigating the presence of diabetic autonomic neuropathy in a group of Sudanese diabetic patients. This is a descriptive prospective cross-sectional study. It was conducted at Herra medical center and Alnoor specialized center in Omdurman. One hundred diabetic patients were studied using standardized questionnaire, including history, clinical examination and investigations. Female to male ratio was 1.7:1. Most of the patients had Type 1 Diabetes mellitus (DM). Common age group affected was >51 years and common duration of DM was < 10 years. Diabetic autonomic neuropathy was diagnosed in 71% of the patients. The majority of the patients had early stage diabetic autonomic neuropathy. Diabetic autonomic neuropathy is a very common problem among our studied group (all the patients were adult diabetic Sudanese). Diabetic autonomic neuropathy can occur at any age and at any time, even at early diagnosis of diabetes but poor glycemic control is the main implicating factor. All diabetic patients have to do annual expiration/inspiration ratio to detect early stages of diabetic autonomic neuropathy.
Healing Residential School Trauma. The Case for Evidence-Based Policy and Community-Led Programs

The paper outlines the emerging theory of historic trauma and its relevance to residential school experience. Recent research on suicide and economic development is cited to demonstrate the importance of restoring the bonds of community to achieve change in various sectors. Key findings of research and evaluation conducted by the Aboriginal Healing Foundation (AHF) are presented to identify healing strategies that are having an effect. The congruence between the AHF findings and recommendations of reports on mental health spanning the past decade is underlined. The article concludes with the argument that the evidence base for policy to support culturally adapted, community-led programming is well established. The time has come to translate knowledge into action.
Automated Road Curb Break Lines Extraction from Mobile LiDAR Point Clouds

In last decades, Mobile Light Detection And Ranging (LiDAR) systems were revealed to be an efficient and reliable method to collect dense and precise point clouds. The challenge now faced by researchers is the automatic object extraction from those point clouds, such as the curb break lines, which are essential to road rehabilitation projects and autonomous driving. Throughout this work, an efficient method to extract road curb break lines from mobile LiDAR point clouds is presented. The proposed method was based on the system working principles instead of an algorithmic application over the cloud as a mass of points. The point cloud was decomposed in the original sensor scan profiles. Then, a GPS epoch versus trajectory distance was used to eliminate most non-ground points. Finally, through a vertical monotone chain decomposition, candidate point arrays were created and the curb break lines are formed. The proposed method was shown to be able to avoid the occlusion effect caused by undergrowth. The method allows for distinguishing between right and left curbs and works on curved curbs. Both top and bottom tridimensional break lines were extracted. When compared with a reference manual method, in the tested dataset, the proposed method allowed for a decrease in the curb break lines extraction time from 25 min to less than 30 s. The extraction method provided completeness and correctness rates above 95% and 97%, respectively, and a quality value higher than 93%.
CLUSTERS AS THE FORM OF ENTERPRISE FUNCTIONING IN POLISH METALLURGICAL INDUSTRY

In recent years, we have observed an increasing interest of the European Union and its selected member states in the new type of economic policy that is based on the concept of the cluste Clusters are specialised groups of mutually competitive, but also cooperating, enterprises they are the key to generating and maintaining competitive advantage. The crucial cluster element is the geographically concentrated group of companies engaged in identical business. The concentration of enterprises increases their mutual competitiveness on the market. Simultaneously, the fact that they act in a group causes some external factors to arise that are beneficial to all; for instance, a greater flow of innovative human resources, so necessary for the efficiency of the manufacturing process. Clusters form a new approach to the competition of competing companies that is becoming increasingly popular. Lately, we have been witnessing intensive expansion of this form of companies' functioning in the metallurgical industry. Literature and government statistics have made it possible to ascertain that Polish metallurgy makes up a significant element of national economy and there are premises for the emergence of relevant cluster structures. Metallurgical clusters tend to function as industrial (network) districts and are aware of the benefits of participating in the said cluster structure.
IoT-Based Aquatic Pollution Monitoring using Convolutional Neural Networks

: The continuously decreasing quality of drinking water is of great concern across the globe. Due to the incessant mixing of unprocessed industrial and domestic wastes, the contamination rate of water is rapidly increasing.The contaminations are of physical, organic, and chemical nature. Owing to this rise in pollution, the idea proposed is to build a model equipped with a visual device such as a camera, and other sensors, that by means of floating on water, can constantly monitorthe water body. Using techniques of computer vision, floating or other debris is detected on the surface of water. The use of sensors can help to keep in check the physical as well as the chemical parameters of water. Conditions like temperature, pH, turbidity, conductivity etcetera can be used either directly or indirectly to judge the overall quality of water.The optical device captures real-time images and a debris detection algorithm using Convolutional Neural Network (CNN) sends vital measurements to the user. The processed dataafter converting intoa user-understandable format can be used to get a general idea of whether the water is safe for consumption, that is, to determine whether it is potable or could be made potable by some treatment. Secondly, it could be used to monitor the balance in marine ecosystems.
Pedestrian Image Generation for Self-driving Cars

Pedestrian image generation in the desired pose can be used in a wide range of applications e.g., person re-identification and tracking which are among the fundamental challenges in self-driving cars. This is a hard task because it should be invariant to a set of nuisances such as body poses, illuminations, or changes in camera viewpoint. In this work, we want to study the task of synthesizing a latent canonical view of a pedestrian that will potentially be robust to the mentioned factors of nuisances. Our goal is to generate the unique frontalized view of a pedestrian observed in the wild. The generated image should visually be the same regardless of the body pose. We propose a new generative framework that goes beyond the 1 to 1 supervision commonly used. We propose to jointly reason on multiple inputs and outputs thanks to a carefully chosen loss function acting as a regularizer. Our experiments show the benefits of our framework on challenging low-resolution datasets.
Evaluating the Effectiveness of BEYOND THE BASICS Parenting Groups for Parents/Caregivers of Young Children Involved with Child Welfare

Kerrin Churchill & Heather MacDonald Aisling Discoveries Child and Family Centre Patti Rose-Vellucci Catholic Children’s Aid Society Cindy Himelstein & Ronalee Kunz, Children’s Aid Society of Toronto Nesta Blake Jane Finch Community and Family Centre Heather Shimkovitz Jewish Family and Child Service Debbie Bridge & Brian Russell Lakeshore Area Multiservice Project Shirley Gillis-Kendall & Isaac Mandamin Native Child and Family Services of Toronto Katherine Hodgson-McMahon Toronto First DutyACTT-Secord/Dawes Project
Making digital twins using the Deep Learning Kit (DLK)

Deep learning (DL) is one of the fastest-growing fields in artificial intelligence (AI). While still in its early stages of adoption, DL has already shown it has the potential to make significant changes to the lithography and photomask industries through the automation or optimization of equipment and processes. The key element required for application of DL techniques to any process is a large volume of data to adequately train the DL neural networks. The accuracy of the classification or prediction of any DL system is dependent on the depth and breadth of the training data to which it is exposed. For semiconductor manufacturing, finding adequate data – especially for corner cases – can be difficult and/or expensive. In this paper, we will present two digital twins that are themselves built from DL as a part of a DL Starter Kit. We will demonstrate the creation of DL-based digital twins for a mask scanning electron microscope (SEM) and for curvilinear inverse lithography technology (ILT).
Equilibrium asset prices and the wealth distribution with inattentive consumers

This paper studies the e¤ects of heterogeneity in planning propensity on wealth inequality and asset prices. I consider an economy populated by "attentive" and "inattentive" agents. Attentive agents plan their consumption period by period, while inattentive agents plan every other period. Infrequent planning increases uncertainty concerning future income or future asset returns. In general equilibrium, inattentive consumers trade at unfavorable prices. If the only source of uncertainty is future income, inattentive consumers will still accumulate more wealth. In contrast, in a version of the model driven by uncertain asset returns, infrequent planning produces the opposite result: inattentive investors accumulate less wealth, in line with empirical evidence. Moreover, asset prices are much more volatile than in a representative agent model with full attention, because changes in asset prices must induce attentive consumers to voluntarily bear the burden of adjusting to aggregate shocks. Keywords: Inattentiveness, Infrequent Planning, Heterogeneous Agents JEL codes: E21, D52, D80, D91 Sveriges Riksbank, Research Division SE103-37 Stockholm, Sweden Tel. +4687870432. For invaluable comments and continuous support, I am deeply indebted to John Hassler, Per Krusell and Torsten Persson. I also bene ted from comments fromMikael Carlsson, Wouter den Haan, Ricardo Reis, Eric Young and seminar participants at the IIES and the Riksbank. The views expressed in this paper are solely the responsibility of the author and should not be interpreted as reecting the views of the Executive Board of Sveriges Riksbank. Any errors or omissions are the responsibility of the author.
Handling Stuck-at-Fault Defects Using Matrix Transformation for Robust Inference of DNNs

Matrix-vector multiplication is the dominating computational workload in the inference phase of deep neural networks (DNNs). Memristor crossbar arrays (MCAs) can efficiently perform matrix-vector multiplication in the analog domain. A key challenge is that memristor devices may suffer stuck-at-fault defects, which can severely degrade the classification accuracy. Earlier studies have shown that the accuracy loss can be recovered by utilizing additional hardware or hardware aware training. In this article, we propose a framework that handles stuck-at-faults using matrix transformations, which is called the MT framework. The framework is based on introducing a cost metric that captures the negative impact of the stuck-at-fault defects. Next, the cost metric is minimized by applying matrix transformations <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>. A transformation <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> changes a weight matrix <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula> into a new weight matrix <inline-formula> <tex-math notation="LaTeX">$\widetilde {W}= T(W)$ </tex-math></inline-formula>. In particular, a row flipping transformation, a permutation transformation, and a value range transformation are proposed. The row flipping transformation results in that stuck-off (stuck-on) faults are translated into stuck-on (stuck-off) faults. The permutation transformation maps small (large) weights to memristors stuck-off (stuck-on). The value range transformation is based on reducing the magnitude of the smallest and largest elements in the weight matrices, which results in that the stuck-at-faults introduce smaller errors. The experimental results demonstrate that the MT framework is capable of recovering 99% of the accuracy loss on both the MNIST and CIFAR-10 datasets without utilizing hardware aware training. The accuracy improvements come at the expense of an <inline-formula> <tex-math notation="LaTeX">$8.19\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$9.23\times $ </tex-math></inline-formula> overhead in power and area, respectively. Nevertheless, the overhead can be reduced with up to 50% by leveraging hardware aware training.
An Adaptive Sensor Fusion Framework for Pedestrian Indoor Navigation in Dynamic Environments

Indoor navigation is a representative application of an indoor positioning system that uses a variety of equipment, including smartphones with various sensors. Many indoor navigation systems utilize Wi-Fi signals, as well as a variety of inertial sensors, such as a 3D accelerometer, digital compass, gyroscope, and barometer, to improve the accuracy of user location tracking. The inertial sensors are vulnerable to changes in the surrounding environments and sensitive to users behavior, but little research has been conducted on sensor fusion under these conditions. In this paper, we propose a dynamic sensor fusion framework (DSFF) that provides accurate user tracking results by dynamically calibrating inertial sensor readings in a sensor fusion process. The proposed method continually learns the errors and biases of each sensor due to the changes in user behavior patterns and surrounding environments. The learned patterns are then dynamically applied to the user tracking process to yield accurate results. The results of experiments conducted in both a single-story and a multi-story building confirm that DSFF provides accurate tracking results. The scalability of the DSFF will enable it to provide more accurate tracking results with various sensors, both existing and under development.