Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Image Analysis by L1-Norm Semi-supervised Learning

This paper presents a novel L1-norm semi-supervised learning algorithm for robust image analysis by giving new L1-norm formulation of Laplacian regularization which is the key step of graph-based semi-supervised learning. Since our L1-norm Laplacian regularization is defined directly over the eigenvectors of the normalized Laplacian matrix, we successfully formulate semi-supervised learning as an L1-norm linear reconstruction problem which can be effectively solved with sparse coding. By working with only a small subset of eigenvectors, we further develop a fast sparse coding algorithm for our L1-norm semi-supervised learning. Due to the sparsity induced by sparse coding, the proposed algorithm can deal with the noise in the data to some extent and thus has important applications to robust image analysis, such as noise-robust image classification and noise reduction for visual and textual bag-of-words (BOW) models. In particular, this paper is the first attempt to obtain robust image representation by sparse co-refinement of visual and textual BOW models. The experimental results have shown the promising performance of the proposed algorithm.
Underwater Communication and Position Reference System

This report presents results obtained in the CageReporter project regarding the development of a low cost hydroacoustic subsea communication system adapted for use in fish cages. The report mainly addresses tasks regarding the optimization of sender and receiver technology, as well as algorithms for advanced signal processing to optimize bandwidth while ensuring stable real-time communication during operations in fish cages. The solutions have been tested and validated in full scale field trials in two fish farms. In addition, this activity presents a solution developed to obtain a relative position reference system where the main challenge was to develop a realistic real-time map of the fish cage. The report describes the analytical study that conducted to place the acoustic transmitters. The proposed configuration has been tested in full scale. Afterwards, the obtained experimental data have been used to develop and validate numerical methods that estimate a high-resolution real-time map of the fish cage. The work furthermore includes the development of algorithms for state estimation to increase accuracy and reduce target noise. The accuracy of the position reference system has been validated through multiple filed trials. PREPARED BY Biao Su CHECKED BY Nina Bloecher APPROVED BY Leif Magne Sunde This document has been approved according to SINTEF's approval procedure, and is digitally secured
Adaptive-Gains Enforcing Constraints in Closed-Loop QP Control

In this letter, we revisit an open problem of constraints formulation in the context of task-space control frameworks formulated as quadratic programs. In most inverse dynamics implementations, the decision variables are: robot joints acceleration, interaction forces (mostly physical contacts), and robot torques. Nevertheless, many constraints, like distance and velocity bounds, are not written originally in terms of one of these decision variables. Previous work proposed solutions to formulate and enforce joint limits constraints. Yet, none of them worked properly in closed-loop, specifically when bounds are reached or when they are time-varying. First, we show that constraints like collision avoidance, bounds of center of mass, constraints on field-of-view, Cartesian and velocity bounds on a given link. are written in a generic class. Then, we formulate such a class of constraints with gain-parameterized ordinary differential inequality. An adaptive-gain method enforces systematically such class of constraints, and results on a stable behavior when their bounds (even when they vary with time) are reached in closed-loop. Experimental results performed on a humanoid robot validate our solution on a large panel of constraints.
An Optimized Frame-Driven Routing Algorithm for Reconfigurable SRAM-Based FPGAs

Reconfigurable SRAM-based Field Programmable Gate Arrays (FPGAs) are everyday more attractive due to their high integration, performance, flexibility, and upgradability. Run-time reconfiguration improves the reconfigurable computing paradigm allowing to rewrite just a portion of the FPGA configuration memory on-line. This enhances the flexibility and provides opportunities for new high-performing architectures able to adjust in-flight the hardware to the current payload. However, the performance of reconfigurable architectures is bounded by the efficiency of the reconfiguration procedure, which in turn is bounded by the amount of configuration frames to be rewritten in the memory. Furthermore, the lack of tools and design software to implement optimized reconfigurable architectures makes their performance less efficient than expectation. In this work, we propose an approach to enhance the performance of reconfigurable systems by reducing the reconfiguration time of reconfigurable resources. Our method is based on a frame-driven routing algorithm able to drastically reduce the number of configuration memory frames used in the design. We evaluate the optimization achieved with our algorithm on several benchmark circuits of different size and we investigate the performance and the routability for different placement solutions. Experimental results confirm that our approach reduces the reconfiguration time up to 40% with respect to traditional reconfiguration approaches for a wide range of circuits.
Network intrusion prevention in the evolved packet core utilising software defined networks and network function virtualisation

Mobile Networks (MNs) are fundamental infrastructures in modern life. As traffic volumes rise and subscriber needs are expanding, MNOs need to adapt in order to keep up with the demand. This has led to Mobile Network Operators (MNOs) virtualising the Core Network (CN) by utilising Software Defined Networking (SDN) and Network Functions Virtualisation (NFV). The security and reliability of the MN are under higher levels of scrutiny as more traffic and subscribers make use of the MN. As MNs become more popular so do they become more enticing for malicious actors as targets for attacks. The virtualisation of the CN has led to new security issues being introduced such as unused network paths being created for attackers to exploit. This research aims to utilise SDN and NFV to mitigate this issue by only allowing for critical network paths to be traversable in a virtualised CN without triggering alerts and node quarantines. The CN of a MN controls/manages all network traffic flows through the mobile network from User Equipment (UE) to a backhaul network (e.g., the Internet). Flows are streams of data that make use of a network path between two or more nodes within a network. Security has mostly been focussed on defending the perimeter of the CN to prevent unwanted access to the internals of the CN, as well as preventing the UE of subscribers from getting compromised. This perimeter only focus has led to the High Value Assets (HVAs) of the CN being vulnerable to attacks from malicious actors that have gained access to the internal nodes of a CN. Vulnerabilities still exist in the system
Pembangunan Daftar Kata Terkait pada Kosa Kata Al-Qur'an Berdasarkan Kesamaan Distribusiaonal

The Quran is the Muslim holy book that contains so many words in it. This makes it difficult for ordinary people to find connections between words in the Quran. For example like the word معروف which has a connection with the word عفا because in the Quran the two words are often in one verse and also the two words have a connection in the meaning of forgiving is one of the good deeds. At present, there is no dictionary, encyclopedia or thesaurus in the vocabulary of the Quran that explains the interrelationships of words in the Quran. Therefore this study will discuss a connection with the words in the Qur'an and to further assist in finding the interrelations between verses. The method used in this study is a method with a distribution-based equality approach based on Continuous Bag of Word (CBOW). The use of the CBOW method produces a precision value of 98% based on the results of the system output with the correction from linguists
Four‐port shared rectangular radiator with defected ground for wireless application

In this research work, a modified shared rectangular radiator is proposed for 5.7‐GHz wireless application using multiple cuts and partially stepped ground. The shared design requires no isolating structure to decouple the ports. The proposed shared geometry produces >13 dB isolation in the 4.4‐ to 6.4‐GHz operating band. The gain varies from 3.0 to 6.2 dBi, and radiation efficiency is >70 % in the band. The mutual coupling among the port is reduced by orthogonal arrangement of ports, and envelope correlation coefficient (ECC) is <0.04 in the presented band. Apart from this, isolation in the proposed shared radiator is enhanced by additional multiple cuts with stepped ground. The indoor‐outdoor suitability of multiple input multiple output (MIMO) antenna for isotropic and Gaussian medium is checked by mean effective gain (MEG) and specific absorption rate (SAR). The presented results of MEG and SAR fulfill the required safety norms as per the ITU for different environments.
Millimeter Wave Beam Recommendation via Tensor Completion

Accurate and fast beam-alignment is essential to cope with the fast-varying environment in millimeter-wave communications. A data-driven approach is a promising solution to reduce the training overhead by leveraging side information and on-the-field measurements. In this work, a two-stage tensor completion algorithm is proposed to predict the received power on a set of possible users’ positions, given received power measurements on a small subset of positions. Based on these predictions and on positional side information, a small subset of beams is recommended to reduce the training overhead of beam-alignment. Numerical results evaluated with the Quadriga channel simulator demonstrate that the proposed algorithm achieves correct alignment with high probability using small training overhead: given power measurement on only 20% of the possible positions when using a discrete coverage area, our algorithm attains a probability of correct alignment of 80%, with only 2% of trained beams, as opposed to a state-of-the-art scheme which achieves 50% correct alignment in the same configuration. To the best of our knowledge, this is the first work to consider the beam recommendation problem based on measurements collected on a small subset of positions.
Lessons learned and new directions regarding Discharge Direct from Adult Intensive Care Units Sent Home (DISH): A narrative review

Purpose To perform a narrative review of the literature regarding the discharge of patients directly to home (DDH) from the intensive care unit, and to identify patient characteristics and clinical outcomes associated with this practice. Methods We searched MEDLINE and EMBASE from 1946 to present. We also manually searched the references of relevant articles. A two-step review process with three independent reviewers was used to identify relevant articles based on predetermined inclusion/exclusion criteria. Results Four studies were included in the final review. Two studies were retrospective and two studies were prospective that shared data from the same patient cohort. All were single center studies. Two of the four studies outlined clinical outcomes associated with DDH. Conclusions This study highlights the relative dearth in the literature regarding the increasingly common practice of DDH, underscores the importance of further studies in this area, and identifies future important foci of research.
A new species of Bachia (Squamata: Gymnophthalmidae) with pleisomorphic limb morphology

We describe a new species of Bachia from the upper Rio Negro drainage of southeastern Colombia and southern Venezuela. The new taxon is diagnosed by a complement of phalanges that is unique among gymnophthalmid lizards and intermediate relative to other Bachia and closely related genera. Variation in limb osteology among the species of Bachia and close relatives is reported. We discuss the distribution of B. panoplia and the taxonomic status of B. flavescens.
LOW-POWER AND LOSSY NETWORKS OF WIRELESS SENSOR NETWORKS WITH PROTOCOLS ALGORITHM COMPARISON

This research focuses on the energy efficiency needed by sensor nodes in the network architecture i.e, LEACH, HEED, LEACH-TLCH and PEGASIS according to the Routing Protocol Low Power and Lossy Network (RPL) pattern. In this research compared all algorithms based on the number of different sensor nodes. In this research, there is no focus on the type of sensor used but rests on the conclusions of the total results of the Overall energy (Avg_Energy) on clustering with the most appropriate networking strategy. In this research, 20, 50 and 100 nodes were tested in the HEED, LEACH, LEACH-TLCH and PEGASIS Algorithms respectively. furthermore, from the results of testing the total Power (mW) sensor obtained from the sum of 4 components of the Power Sensor node, i.e, CPU Power, Power LPM, Power TX (Transmitter) and Power Rx (Receiver), concludes the Power Consumption sensor node is affected by the number of nodes found on the Sensor Node Network Architecture, the lowest Power Consumption is the LEACH Algorithm Protocol with a total of 2,038 mW at 100 nodes, while the highest Power Consumption is LEACH-TLCH Protocol Algorithm 4,851 mW with 100 nodes followed by PEGASIS with total Consumption 4.31 mW. accordingly the simulation, At the lowest sensor node in this experiment, which is 20 nodes, LEACH produces the lowest total Power of 1.21 mW, while the highest is PEGASIS 1,361 mW. At the number of 50 nodes, the lowest is HEED with a power consumption of 1,558 mW, this result is only 0.046 mW with the LEACH Algorithm Protocol which is 1,604 mW. Furthermore, by building the right sensor network (WSN) it will support IoT networks with long sensor lifetime.
Random early detection gateways for congestion avoidance

This paper presents Random Early Detection (RED) gateways for congestion avoidance in packet-switched networks. The gateway detects incipient congestion by computing the average queue size. The gateway could notify connections of congestion either by dropping packets arriving at the gateway or by setting a bit in packet headers. When the average queue size exceeds a preset threshold, the gateway drops or marks each arriving packet with a certain probability, where the exact probability is a function of the average queue size. RED gateways keep the average queue size low while allowing occasional bursts of packets in the queue. During congestion, the probability that the gateway notifies a particular connection to reduce its window is roughly proportional to that connection’s share of the bandwidth through the gateway. RED gateways are designed to accompany a transport-layer congestion control protocol such as TCP. The RED gateway has no bias against bursty traffic and avoids the global synchronization of many connections decreasing their window at the same time. Simulations of a TCP/IP network are used to illustrate the performance of RED gateways.
Prevalence of clinical and subclinical caprine mastitis of northern region in Bangladesh

Mastitis is a multifactorial and infectious disease of the udder of dairy animals. Infected animals suffer from pain and fever and causing damage in udder and changes in milk. The aim of this work was to determine the prevalence of clinical and sub-clinical caprine mastitis associated with bacterial pathogens during the period from January to June, 2017. A total of 120 lactating goats were randomly selected from different villages of Dinajpur district and 240 milk samples from each udder half were aseptically collected. The udders were examined and screened for the evidence of clinical and subclinical mastitis by clinical examination of udders and examining milk samples. Subclinical mastitis was detected by California Mastitis Test (CMT). Milk samples of all clinical and subclinical mastitis goats were cultured in/on bacteriological media for isolation and characterization of responsible bacterial pathogens. The overall prevalence of clinical and subclinical mastitis in goats was found to be 11.67% and 38.75%, respectively. Bacteriological examination of mastitic milk samples of caprine revealed Staphylococcus spp. (32.5%) as the major pathogen followed by Escherichia coli (22.5%), Pseudomonas spp. (12.5%), Klebsiella spp. (12.5%) and Bacillus spp. (5%). Results of this study indicated that about 50% of caprine udder were infected with mastitis (38.75%), which may render lowering milk production and mortality of goats and kids. It needs to screen milk sample of lactating goats by CMT, identify mastitic goats with infected bacteria, evaluate antibiotic sensitivity and design control strategy accordingly.Progressive Agriculture 29 (2): 127-138, 2018
Real-time scalable algorithms for alpha-fair resource allocation in software defined networks. (Algorithmes distribués dédiés au calcul de l'allocation alpha-équitable en temps réel dans les réseaux SDN)

In this dissertation, we deal with the design of algorithms to tackle the α-fair resource allocation problem in real-time and distributed Software-Defined Networks (SDN). First, we define three major requirements that picture the challenges of real-time algorithms implementable in modern distributed SDN controllers. Those challenges are the ability to provide feasible resource allocations at all times, good transient solutions in terms of optimality gap that converge in an acceptable number of inter-controller communication rounds, and their ability of being massively parallelized independently of the network architecture. We use the Alternating Directions Method of Multipliers to design an algorithm that simultaneously, and unprecedentedly, tackles the three challenges. Motivated by a first study of the structural properties of the α-fair model, where we derive a lower bound on the optimal solution, we tune the penalty parameter of the augmented Lagrangian of the problem in order to optimize the algorithm’s performance. We show that the algorithm can function in real-time when the traffic requirements can vary more or less abruptly. The variation of the traffic requirements are modeled by real-time varying coefficients of the optimization model that is solved on-the-fly and may represent various prioritization policies of the traffic (payment, traffic type, number of connections within a tunnel, etc). Then, we describe how to extend the algorithm to real world use cases with limited modifications to cope with multi-path load balancing and online adjustments. Furthermore, we address the problem of α-fairness when the environment is uncertain and the available amount of resources over the network links is known only through general density functions. The main focus there is, instead of feasibility, the notion of safety. We design a heuristic that polishes an outer relaxation of the problem, based on the sensitivity analysis of the static problem. In general, we are able to provide a safe and acceptably efficient solution by solving several static problems.
Mapping industrial patterns and structural change in exports

This paper proposes a new methodology for identifying patterns in the organisation of industries and their evolution over time, based on the temporal network structure of the product space. To do this, I apply a community detection algorithm on 5-year snapshots of the product space from 1975 to 2000. This exercise enables us to identify different clusters of related products and to follow their evolution over time. I find that the product space is highly modular, that is it contains well delimited clusters of products. The community structure and its evolution show that the factors explaining industrial patterns and structural change are more complex than the traditional divide between low, medium and high-tech industries. Several common drivers can be identified to explain the emergence and evolution of different communities including the experience in a technological domain, factor abundance, scale economies as well as global value chains and vertical integration. Moreover, I find that technological domains and boundaries between industries are not always clear-cut and can evolve over time.
Detecting Stuxnet‐like data integrity attacks

This work suggests and tests a solution for detecting loss of integrity with means of prediction in distributed, low‐power computing environment or when collecting relational data disconnected from original data source to data ecosystems. With graph convolution network and variational auto‐encoder, the proposed solution is able to detect data integrity attacks based on link prediction for datasets from various data sources without common data model. The solution has in‐built mechanism to prevent poisoning the detection capability, and it is resource efficient since it does not require extensive storage capability. Due to its programmatic nature and high ability to integrate, unsupervised learning‐based solution fits well in embedded system ecosystems' control functions where automated decisions are taken.
Análisis de datos financieros con técnicas de Machine Learning

The phenomenon of Machine Learning was born in the mid-20th century, but it ́s booming did not take place until this last decade. The rapid consolidation of this technology has been underpinned by a radical increase in data generation and computational capacity, as well as a decrease in the cost of data storage. The relevance of this technology in the actual social paradigm will be explained through two Supervised Learning models and two Unsupervised Learning models.
Combining a psychological intervention with physiotherapy: A systematic review to determine the effect on physical function and quality of life for adults with chronic pain

Abstract Background: Chronic pain affects approximately 28 million people in the UK. It does not always respond to conventional curative treatments and can have a devastating impact on an individual’s ability to function and perform life roles in the way they would like. Psychological approaches to pain management have been advocated for over 40 years, but it is unclear whether the addition of such approaches to physiotherapy increases treatment effectiveness. Objectives: To systematically review the literature to determine whether the addition of psychological approaches to physiotherapy is more effective in improving physical functioning and quality of life than physiotherapy alone. Methods: An electronic database search focused on randomised controlled trials comparing a physiotherapy intervention for chronic pain with a matched intervention with an additional psychological therapy component. All eligible studies were independently reviewed by two researchers and the strength of evidence and results evaluated. A meta-analysis was conducted on post-test mean data for physical functioning. Results: Eight studies were identified for inclusion. Meta-analysis of the data showed combined treatments to be superior in modifying functional outcomes (standardised mean difference −1.12 95% confidence intervals −1.94 to −0.31). This was echoed in the narrative review of the quality of life evidence. Study quality was variable and thus the findings should be interpreted with caution. Conclusions: There is evidence that combining physiotherapy and psychological approaches improves physical function in chronic pain in comparison with physiotherapy alone. Further examination of this field is required to inform changes in practice and to develop treatment methods.