Miracle-dz/UniFAR-FTUs huggingface dataset:
Cranial decompression and expansion surgery for the treatment of refractory idiopathic intracranial hypertension: case report and systematic review

Abstract Objective The purpose of this study is to systematically review the literature on the clinical outcomes following different surgical techniques in patients with refractory idiopathic intracranial hypertension (IIH). Background IIH is a condition characterised by increased cranial pressure (ICP) in the absence of an intracranial lesion that does not adequately respond to different medical and surgical therapies. Cranial decompression or expansion surgeries are a last resort therapy for patients with refractory IIH. Methods A systematic literature search of the databases of PubMed, Embase and Medline from inception to 2019 was performed. Searches were limited to the English language and to clinical studies. Studies were included if clinical outcomes following different cranial decompression or expansion techniques were reported. We also add one case of our own experience with performing a bilateral frontoparietal expansion craniotomy and subtemporal craniectomy. Results Five manuscripts, describing 38 procedures, met the inclusion criteria. Thirty-one patients were female (82%). The mean age was 26.2 years. The techniques studied included subtemporal craniectomy (27/38, 71%), internal cranial expansion (10/38, 26%), and cranial morcellation decompression (1/38, 3%). Thirty-five patients presented with headaches of which 17 noted postoperative improvement or resolution (49%). Visual deficits were documented in 30 patients and 25 reported postoperative improvement (83%). Papilledema disappeared in 23 of 32 patients with this sign at presentation (72%). In our patient, symptoms completely resolved postoperatively and a 6% increase in intracranial volume was measured. Conclusions Cranial vault decompression or expansion surgeries may be an effective last resort therapy for patients with refractory IIH. These surgeries expand the intracranial volume, and thus may normalise ICP, leading to clinical improvement.
The Commonwealth Magistrates’ and Judges’ Association (CMJA) Brisbane Declaration: a brief assessment of magisterial independence in Malaysia

ABSTRACT Judicial independence for the lower courts, especially at the magisterial level, is often disregarded, be it administratively or in academic studies. Shortcomings in the independence of the Malaysian magistracy, though apparent, were never systematically evaluated against the judicial independence benchmarks. By using the Brisbane Declaration 2018, a Commonwealth Magistrates’ and Judges’ Association (CMJA) established instrument that prescribed standards of independence for the magistracies, this article briefly assesses the state of magisterial independence in Malaysia. The analysis confirms that institutional independence, adjudicatory independence and administrative independence of the Malaysian magistracy have been compromised. Certain essential formal safeguards of judicial independence are also absent. This lack of magisterial independence is mainly attributable to the complexity of the courts’ administrative structure that exists as a hybrid framework between the Executive and the Judicature. Hence, further in-depth study of this matter is required to preserve the dignity, integrity, impartiality and independence of the Malaysian magistracy.
Estimating tropical forest stand volume using Sentinel-2A imagery

Forest stand volume is one of the important variables to assess forest management. In this study, the integration of Sentinel-2A images and forest field data was used to estimate the forest volume for separate stratum and for the entire area of the study area. Accordingly, the forest area was divided into relative homogeneous strata as dense forests and open forests. A total of 202 sample plots were placed throughout the forest strata to measure relevant variables such as diameter at breast height and tree height. Spectral bands and vegetation indices within the plot were also extracted to correlate with forest stand volume. The predictions were separately tested for the entire area and for each forest stratum. The result indicated 8 out of 21 predictor variables (spectral and vegetation indices) were selected as optimal variables to build the estimate models. The results show that when separable stratum prediction is used instead of entire area estimation, the accuracy of estimates is more likely to be improved, especially with the forest stand under low disturbance. The accuracy of open forest was also found higher dense forest.
A New Occurrence of Terrestrial Native Iron in the Earth’s Surface: The Ilia Thermogenic Travertine Case, Northwestern Euboea, Greece

Native iron has been identified in an active thermogenic travertine deposit, located at Ilia area (Euboea Island, Greece). The deposit is forming around a hot spring, which is part of a large active metallogenetic hydrothermal system depositing ore-bearing travertines. The native iron occurs in two shapes: nodules with diameter 0.4 and 0.45 cm, and angular grains with length up to tens of μm. The travertine laminae around the spherical/ovoid nodules grow smoothly, and the angular grains are trapped inside the pores of the travertine. Their mineral-chemistry is ultra-pure, containing, other than Fe, only Mn (0.34–0.38 wt.%) and Ni (≤0.05 wt.%). After evaluating all the possible environments where native iron has been reported up until today and taking under consideration all the available data concerning the study area, we propose two possible scenarios: (i) Ilia’s native iron has a magmatic/hydrothermal origin i.e., it is a deep product near the magmatic chamber or a peripheral cooling igneous body that was transferred during the early stages of the geothermal field evolution, from high temperature, reduced gas-rich fluids and deposited along with other metals in permeable structural zones, at shallow levels. Later on, it was remobilized and mechanically transferred and precipitated at the Ilia’s thermogenic travertine by the active lower temperatures geothermal fluids; (ii) the native iron at Ilia is remobilized from deep seated ophiolitic rocks, originated initially from reduced fluids during serpentinization processes; however, its mechanical transport seems less probable. The native iron mineral-chemistry, morphology and the presence of the other mineral phases in the same thermogenic travertine support both hypotheses.
Robust Solutions to Constrained Optimization Problems by LSTM Networks

Many technical issues for communications and computer infrastructures, including resource sharing, network management and distributed analytics, can be formulated as optimization problems. Gradient-based iterative algorithms have been widely utilized to solve these problems. Much research focuses on improving the iteration convergence. However, when system parameters change, it requires a new solution from the iterative methods. Therefore, it is helpful to develop machine-learning solution frameworks that can quickly produce solutions over a range of system parameters. We propose here a learning approach to solve non-convex, constrained optimization problems. Two coupled Long Short Term Memory (LSTM) networks are used to find the optimal solution. The advantages of this new framework include: (1) near optimal solution for a given problem instance can be obtained in very few iterations (time steps) during the inference process, (2) the learning approach allows selections of various hyper-parameters to achieve desirable tradeoffs between the training time and the solution quality, and (3) the coupled-LSTM networks can be trained using system parameters with distributions different from those used during inference to generate solutions, thus enhancing the robustness of the learning technique. Numerical experiments using a dataset from Alibaba reveal that the relative discrepancy between the generated solution and the optimum is less than 1% and 0.1% after 2 and 12 iterations, respectively.
Research on Gait Evaluation Method Based on Machine Vision

Gait is a movement posture that appears during walking, which can reflect the health of the human body. Aiming at people with lower limb dyskinesia, a gait evaluation method based on machine vision is proposed. Through the posture estimation network combined with RGB-D data, complete the patient's three-dimensional joint point recognition. On this basis, the gait parameters are calculated through the motion trajectory of the joint points, and the Spearman algorithm is used to analyze the gait parameters in the normal population under different age and gender groups. As well as the performance between the normal population and the disabled population, select parameters that are not significantly related to age and gender, and show significant differences between the healthy population and the lower extremity dyskinesia population, and assign different weight coefficients to each parameter to establish information reflecting each parameter Comprehensive gait evaluation system.
Complementary Sequence Construction for Constant-Envelope OFDM Transmission Enabling Nonlinear Amplification and Clipping

The high PAPR of OFDM transmission leads to power/cost inefficiencies in amplifier use and/or spectral noise from clipping effects. To avoid these issues, we propose transmitting a complementary sequence pair whose aperiodic autocorrelations sum to a delta function in such a way that the amplitude of the signal is constant. This enables the use of low-cost nonlinear amplifiers operating at full power. The sequence pair is constructed iteratively, by sequential encoding of information symbols onto the pair such that the sequences remain complementary. The structure of these sequences and the resulting constant-envelope signal are analyzed, leading to methods of symbol extraction and the results of a decoding error. Finally, we extend the discussion to two dimensional sequence pairs, for use in mmWave/MIMO systems where the inefficiencies of a high PAPR are even more acute.
Masquerade detection: A topic model based approach

The goal of masquerade detection is to "detect" when an intruder has infiltrated a computer system by looking for evidence of malicious behaviour. In this project, I use a topic model based intrusion detection system to search for intruders within the SEA and Greenberg datasets of Unix computer commands. Using LDA topic modeling I was able to find a probability distribution for each user for both the topics over a block of commands and over each command. Using these two probability distributions and building on previous detection techniques I was able to create five different detection techniques. I describe how I created the five LDA based models and combine them to find a sixth model. All of these techniques performed on par with their non-LDA counter-parts. Therefore, combined with the reduction in dimensionality afforded by the LDA topic model, I conclude that my methods perform better than the current models.
Feature Detection Method of Assembly Position Based on Machine Vision

The detection of assembly station scene features is a hot research issue in the field of computer vision. Its purpose is to superimpose assembly-related information on the corresponding assembly station scene and provide intuitive online guidance to assembly operators. To this end, this paper proposes a method based on machine vision to detect assembly station scene features, and uses dimensionality reduction processing to simplify the detected high-dimensional features, and uses a bag of words model to construct a Code Book representation method of assembly station scene feature vectors. The dimensionality reduction method is used to solve the problem of the large amount of calculations directly using natural features to match the scene.
CloudImmu: Transparent Protection of Binary Applications in the Cloud

As more organizations are moving their IT infrastructures from on-premises to the cloud, cloud security breaches have just surpassed on-premises breaches. There is a pressing need to develop practical and deployable cyber defense capabilities to protect the enormous amount of potentially vulnerable binary applications in the cloud from previously unseen cyberattacks. In this paper, we present CloudImmu, a practical cloud cyber defense system that is built upon a novel combination of binary rewriting and instrumentation techniques, virtual machine introspection and hypervisor level anomaly detection techniques. Our immunization tool has successfully “immunized” large real world binary applications such as bash, Snort, and our experiments with real world exploits have shown that CloudImmu can detect and block cyberattacks on properly immunized, otherwise vulnerable binary applications in virtual machines in real-time without using any prior knowledge of the attacks. Our benchmark experiments show that CloudImmu incurs less than 1.06% overall run-time performance overhead on typical applications with typical workloads.
Mitigating TCP Congestion: A Coordinated Cyber and Physical Approach

The operation of the modern power grid is becoming increasingly reliant on its underlying communication network, especially within the context of the rapidly growing integration of Distributed Energy Resources (DERs). This tight cyber-physical coupling brings uncertainties and challenges for the power grid operation and control. To help operators manage the complex cyber-physical environment, ensure the integrity, and continuity of reliable grid operation, a two-stage approach is proposed that is compatible with current ICS protocols to improve the deliverability of time critical operations. With the proposed framework, the impact Denial of Service (DoS) attack can have on a Transmission Control Protocol (TCP) session could be effectively prevented and mitigated. This coordinated approach combines the efficiency of congestion window reconfiguration and the applicability of physical-only mitigation approaches. By expanding the state and action space to encompass both the cyber and physical domains. This approach has been proven to outperform the traditional, physical-only method, in multiple network congested scenarios that were emulated in a real-time cyber-physical testbed.
Non-Parametric and Geometric Multi-Target Data Association for Distributed MIMO Radars

Distributed MIMO radar systems offer tremendous advantage in the detection of airborne platforms employing stealth and are resilient to single point failure. However, when multiple targets are present over the surveillance region, the reflected signals at various receivers from these targets cannot be uniquely associated to the targets easily. Incorrect associations of the received data lead to the creation of ghost targets, and hence, de-ghosting is an inherent problem in distributed radar systems. Exploiting the geometry of the measurement model into the association process, we devise algorithms that are practically implementable and computationally feasible. In this work, a novel, efficient and fast data association scheme followed by a localization algorithm is proposed that utilizes Time-of-Arrival and Doppler frequency measurements of the targets with respect to the transmitter-receiver pairs to accurately determine 3D position and velocities of the targets. The proposed approach is non-parametric as it does not need the assumption of initial states, number of targets and their motion models. It simultaneously associates up to four targets present within a minimum horizontal separation of $100m\times 100m$ for signals of bandwidth 20MHz and any number of targets that are flying far away from this minimum separation in the observation region. It can also associate and track up to nine targets that have sequential birth and random death, flying with random realizable velocities.
Transmission grid outage statistics extracted from a web page logging outages in Northeast America

Detailed outage data is foundational for the study of power transmission grid reliability and resilience, and particularly for dependent outages and rarer events, but there are very few such data sets that are published and freely accessible to all engineers and researchers. There are voluminous logs of scheduled and actual outages in a region of Northeast America available on the web. We show how to compress and process these logged data to obtain bulk statistics describing the outages, such as event size, propagation, and spread on the network. These statistics are very useful for calibrating and validating models of resilience to ensure realism, and in developing data-driven approaches.
Migration of DOD SATCOM Earth Segment Architectures to Digital IF

DOD SATCOM Earth segment architectures are poised to migrate to Digital IF as the medium of choice. This migration is already underway within commercial SATCOM. Motivators in the case of DOD SATCOM include mission demand for resilience, carrier count congestion, RF bandwidth growth, operational demand for virtualization, modem value proposition, and environmental hardening. Migration at DOD SATCOM gateways is anticipated in three phases, commencing now with foundational groundwork, leading soon to baseline infrastructure, then followed by advanced initiatives such as virtualization and management automation. This migration is being supported by updates to necessary MIL standards and WGS Performance Certification processes to enable WGS access. Subsequent enterprise gateway deployment and robust terrestrial mesh networking achieves SATCOM resilience and mission planning flexibility while enabling the aggregation of baseband operations to regional facilities. Digital IF is fast maturing. Digital IF technology investment is being made and the IEEE-ISTO 4900–2021 “DIFI Standard” for interoperability has just been published. These developments will enable growth and reduce overall lifecycle costs for all manner of communication systems. On all counts, a comprehensive migration to Digital IF, well under way commercially, is also now beginning for DOD SATCOM Earth segment architectures.
State Estimation in Smart Grids Using Temporal Graph Convolution Networks

State estimation (SE) is one of the key functions of smart grids. The availability of large volumes of measurement data introduces new opportunities for improving and complementing the conventional model-based SE in power systems. In this work, a data-driven approach based on Graph Convolution Neural Networks (G-CNNs) is presented for SE in smart grids. The G-CNN can learn the features in the non-Euclidean domain of graphs, which can capture the structures and interactions among the components of power grids. By integrating the temporal dependencies in the time-series data, a temporal G-CNN (T-GCN) is adopted for the SE problem. Specifically, a message-passing G-CNN is used to capture the topological structure of the smart grid and the gated recurrent units are used to capture the dynamic variation of state information for temporal dependencies. The performance evaluation of the presented method for two cases of full measurement availability and availability of a subset of measurements in comparison with some of the existing SE techniques shows promising performance.
A Simple Low-Profile Broadband Antenna Design for 5G Millimeter-Wave Applications Over 38 GHz Spectrum

A broadband antenna operating over 36.8 - 43 GHz frequency range is proposed in this paper for 5G wireless communications systems. The antenna design is extracted from circular patch antenna by loading three patches between circular radius and feed-line. Further improvement in antenna's performances is achieved using a perpendicular pair of elliptical slots inside the circular patch. The proposed antenna offers compact size of 12 mm x 15 mm x 0.79 mm and covers the globally allocated band spectrum for 38 GHz communication systems with more than 7 dBi gain and 90% radiation efficiency throughout the working frequency area. The antenna has designed and analyzed using Higher Frequency Structure Simulator (HFSS). Furthermore, a comparison with state-of-the-art-works is performed to show the potential of proposed design for targeted frequency band. The results confirm that, the proposed antenna with advantages of small size, wide bandwidth, resonance frequency with high impedance matching at 38 GHz, high radiation gain and efficiency, low cost, and simple geometry is a proper candidate for 5G millimeter-waves applications.
Indigenous Knowledge of Wild-food Resources among Children in Tanzania

Abstract This is a case study of children’s knowledge of wild-food resources in three rural communities in Tanzania. The study argues that research on African Indigenous Knowledge Systems (AIKS) on wild food resources marginalizes the theoretical and practical knowledge that children have on these resources. It was found that children, both boys and girls, had a wide knowledge of the wild food resources. This was acquired through social practice. They saw, heard, gathered, processed and ate these food resources. Girls had more theoretical to practical knowledge of wild-food plants, while boys demonstrated greater knowledge of wild animals and birds due their respective involvement in related activities. The study recommends that more research should be conducted to establish the local knowledge of children in different age groups and genders for AIKS curriculum development purposes.
Identification of a gas composition based on an optical spectrum of plasma generated in MEMS ion spectrometer

This article describes a method of identification of a gas composition based on an optical emission spectroscopy performed in a miniature MEMS plasma generator. Individual components are detected on the bases of characteristic peaks in the optical spectrum. However, determination of exact proportions of components in a gas mixture is much more problematic. Here, we present how to deal with this issue. We show which initial reference tests needs to be done and what signal processing operations are required to derive relative concentration of each gas specie. The developed method was applied to investigate two- and three-component mixtures of carbon dioxide, methane and nitrogen.
Resource Management in Software Defined Coalitions (SDC) through Slicing

Future defence infrastructure systems will require increased flexibility and agility to respond to changing application goals, external threats and complex environments. A key enabler for such agility is Software Defined Coalitions (SDC), where the network comprise multiple domains of resources owned by different defence units (partners) but dynamically joined together to form an infrastructure for communications and computation. Software Defined (SD) Slicing aims to enable agile and near-real-time provision and configuration of “slices” of the infrastructure resources for supporting future communications and computing applications. An SD slice, makes use of the allocated resources distributed across several domains to support a set of applications including distributed analytic services. We present a 3-level control architecture with a global SD-slice controller at the top, domain controllers (DC) in the middle, and dynamic, end-to-end flow controllers at the bottom. Associated with each DC is a domain inference engine whose function is to estimate availability of various resources in that domain. Based on the inferred resource availability in domains, the global controller determines the feasibility of supporting a new SD slice and if so, allocates resources to achieve/maintain the required performance of all slices. Based on the resources allocated by the global controller, the slice controller is responsible for sharing the resources across domains among data/processing flows to optimize resource utility. The end-to-end flow controllers then allocate resources to data flows or processing tasks according to dynamic conditions of resources for efficiency and robustness.