Miracle-dz/UniFAR-FTUs huggingface dataset:
Bolstering the Mobile Cloud: Addressing Emerging Threats and Strengthening Multi-Layered Defenses for Robust Mobile Security

The proliferation of mobile devices as the primary computing platform for billions of users worldwide introduces escalating security threats, especially as mobile interactions with sensitive data and cloud services increase. This paper provides an analysis of emerging mobile security issues including malware stealing credentials and data from mobile cloud services, lower-level operating system and hardware vulnerabilities enabling exploits on mobile cloud confidentiality and integrity, weaknesses in mobile apps enabling attacks, and rising user privacy risks from excessive tracking. Current security limitations are also reviewed, including signature-based malware detection, coarse-grained access control, and fragmented visibility across the mobile ecosystem. An integrated cross-layer security strategy is proposed combining formal verification of critical mobile components, pervasive hardware-anchored security, intelligence-driven malware adaptation, robust devsecops processes, and coordinated defense visibility. As mobile continues dominating personal and enterprise computing, constructing resilient security foundations through research and awareness of mobile security issues and countermeasures is imperative for realizing the potential of mobile cloud computing.
Channel Estimation in Broadband MIMO Systems

The use of MILLIMETER wave (mmWave) communication has gained attention of advanced technology due to high bandwidth channels. , which could potentially enhance data transmission rates. However, utilizing higher bandwidths poses a challenge to the ADC’s. Finding ADCs with sufficient bandwidth and precision for mmWave systems may not be feasible, devices.The goal of this project is to create a broadband estimation of channel algorithm for MIMO systems in the mmWave frequency range that use few-bit ADCs. To solve the noisy compressed sensing problem, efficient Approximate Message Passing (AMP) algorithms will be applied. The expectation maximization algorithm will be used to achieve an approximately minimum mean square error. Additionally, training sequences with FFT will be used to minimize the peak-to-average ratio.Simulation results will be used to compare the performance of 1-bit and few (4-bit) ADCs with infinite bit ADCs at different SNRs, training lengths, ADC resolutions, MSE, and mutual information. The results show that 1-bit and few (4-bit) ADCs have minimal performance losses at low and medium SNR.
Segmentation of heart wall muscles and detection of hypertrophic cardiomyopathy from 2D echo images using U- Nets

In this paper, we focus on the development of an automatic technique to obtain the segmentation of the heart wall muscles from echo images using U-Nets. The detection of hypertrophic cardiomyopathy (HCM) and similar conditions is achieved by measuring the thickness of the posterior heart wall in the left ventricle. We measure the thickness of the segmented heart wall using repeated morphological operations in the form of erosion to give us an idea of its real thickness. Medical literature suggests that if the thickness of the heart wall is greater than 15mm, then we classify the image as a potential case of HCM. In our experiment, we have taken 139 images in our training, and 34 images in our test dataset in order to examine the accuracy of our technique. We find that the U-Net obtains an accuracy of 0.85 in terms of the Dice similarity coefficient.
Use of weather types to disaggregate general circulation model predictions

General circulation models (GCMs) simulate climatic conditions with a grid cell resolution on the order of 100,000 km2. This resolution is inadequate to assess the effects of climatic change on water resources at a regional scale. A method has been developed that uses weather-type analysis as a tool to spatially disaggregate GCM predictions to make them useful for water resource studies. The method has been applied to the Delaware River basin to predict the effects of doubling atmospheric carbon dioxide on precipitation patterns in the region. An application of the technique to the Delaware River basin indicates that future climatic conditions will show minimal changes in weather-type frequency, implying that air circulation patterns will remain unchanged. Results of this study indicate that changes in regional precipitation patterns under a doubling of atmospheric carbon dioxide will be a result of within-type changes in weather characteristics.
Design of stable 3.3V to 1.8 V,100 mA Low Drop out voltage Regulator Using 180nm Technology

This paper presents the design of a specific low dropout (LDO) voltage regulator, aiming to ensure a stable output voltage of 1.8 V. This circuit operates within an input voltage range of 3.3 V to 2.2 V and provides a maximum current output of 100 mA. LDOs play a crucial role in electronic devices by regulating voltage and delivering stable voltage to delicate components. The described LDO in this study emphasizes a high current output capability while minimizing output voltage fluctuations. Its versatility makes it applicable in diverse scenarios, including battery-powered devices, sensors, and portable electronics that demand strict adherence to the stable output voltage [11] – [20].
Botnet Attacks Detection using Image Processing Approach: Sequential Neural Network and Feature Selection

Botnets are networks of compromised computers that are controlled by an attacker for malicious purposes such as spamming attacks, DDoS, or data theft. Botnet attacks detection is a challenging task due to their distributed and covert nature. Botnet attacks are a major security concern for organizations and individuals alike. In this paper we proposed a botnet attacks detection using image processing approach. We propose an AI model architecture using Sequential Neural Network and feature selection. There are two methods for converting raw data into image data. First method using predefined categorize for choosing features. The second method uses Pearson correlation value between features in dataset and the label. Predefined categorized features have successfully increased the performance of proposed AI model for botnet attacks detection as well as Pearson correlation value.
Lessons from Usable ML Deployments and Application to Wind Turbine Monitoring

Through past experiences deploying what we call usable ML (one step beyond explainable ML, including both explanations and other augmenting information) to real-world domains, we have learned three key lessons. First, many organizations are beginning to hire people who we call ``bridges'' because they bridge the gap between ML developers and domain experts, and these people fill a valuable role in developing usable ML applications. Second, a configurable system that enables easily iterating on usable ML interfaces during collaborations with bridges is key. Finally, there is a need for continuous, in-deployment evaluations to quantify the real-world impact of usable ML. Throughout this paper, we apply these lessons to the task of wind turbine monitoring, an essential task in the renewable energy domain. Turbine engineers and data analysts must decide whether to perform costly in-person investigations on turbines to prevent potential cases of brakepad failure, and well-tuned usable ML interfaces can aid with this decision-making process. Through the applications of our lessons to this task, we hope to demonstrate the potential real-world impact of usable ML in the renewable energy domain.
UvA-DARE (Digital Academic Repository) Direct observation of a Fermi liquid-like normal state in an iron-pnictide superconductor

There are two prerequisites for understanding high-temperature (high-T c ) superconductivity: identifying the pairing interaction and obtaining a correct description of the normal state from which superconductivity emerges. The nature of the normal state of iron-pnictide superconductors, and the role played by correlations arising from partially screened interactions, are still under debate. Here we show that the normal state of carefully annealed electron-doped BaFe 2 − x Co x As 2 at low temperatures has all the hallmark properties of a local Fermi liquid, with a more incoherent state emerging at elevated temperatures, an identification made possible using bulk-sensitive optical spectroscopy with high frequency and temperature resolution. The frequency dependent scattering rate extracted from the optical conductivity deviates from the expected scaling M 2 ( ω , T ) ∝ (  ω ) 2 + ( p π k B T ) 2 with p ≈ 1.47 rather than p = 2, indicative of the presence of residual elastic resonant scattering. Excellent agreement between the experimental results and theoretical modeling allows us to extract the characteristic Fermi liquid scale T 0 ≈ 1700 K. Our results show that the electron-doped iron-pnictides should be regarded as weakly correlated Fermi liquids with a weak mass enhancement resulting from residual electron-electron scattering from thermally excited quasi-particles. Strong electronic correlations and Mott physics have played an important role in shaping our understanding of high-T c superconductivity (HTSC) 1 . With the discovery of the iron-pnictide family of HTSCs a new playground to study correlation effects has emerged 2 . Unlike the cuprate HTSC, the pnictides are properly classified as moderately correlated semi-metals 3 . By studying their normal state properties a new picture has started to emerge 4 where intra-atomic exchange processes (Hund’s coupling) govern the degree of correlation effects.
Montana's Forest Resources, 2003-2009

This report presents a summary of the most recent inventory information for Montana's forest lands. The report includes descriptive highlights and tables of area, number of trees, biomass, volume, growth, mortality, and removals. Most of the tables are organized by forest type group, species group, diameter class, or owner group. The report also describes inventory design, inventory terminology, and data reliability. Results show that Montana's forest land totals 25.6 million acres. Sixty percent (15.4 million acres) of this forest land is administered by the USDA Forest Service. Douglas-fir forests cover 7.5 million acres or roughly 29 percent of Montana's forested lands, making it the most abundant forest type in the State. The lodgepole pine type is the second-most common individual forest type comprising 17 percent of Montana's forest land. Lodgepole pine is the most abundant tree species in Montana by number of trees, and Douglas-fir is the most abundant species by volume or biomass. Net annual growth of all live trees 5.0 inches diameter and greater on Montana forest land totaled 289.8 million cubic feet. Average annual mortality totaled nearly 746.3 million cubic feet.
DS-IRSA: A Deep Reinforcement Learning and Sensing Based IRSA

One of the main difficulties to enable the future scaling of IoT networks is the issue of massive connectivity. Recently, Modern Random Access protocols have emerged as a promising solution to provide massive connections for IoT. One main protocol of this family is Irregular Repetition Slotted Aloha (IRSA), which can asymptotically reach the optimal throughput of 1 packet/slot. Despite this, the problem is not yet solved due to lower throughput in non-asymptotic cases with smaller frame sizes. In this paper, we propose a new variant of IRSA protocol named Deep-Learning and Sensing-based IRSA (DS-IRSA) to optimise the performance of IRSA in short frame IoTs, where a sensing phase is added before the transmission phase and users' actions in both phases are managed by a deep reinforcement learning (DRL) method. Our goal is to learn to interact and ultimately to learn a sensing protocol entirely through Deep Learning. In this way, active users can coordinate well with each other and the throughput of the whole system can be well improved. Simulation results show that our proposed scheme convergence quickly towards the optimal performance of almost 1 packet/slot for small frame sizes and with enough minislots and can achieve higher throughput in almost all cases.
Geology of the Chesapeake and Ohio Canal National Historical Park and Potomac River Corridor, District of Columbia, Maryland, West Virginia, and Virginia

The Chesapeake and Ohio Canal National Historical Park is 184.5 mi long and extends from Washington, D.C., to Cumberland, Md. The canal passes through three physiographic provinces including the Piedmont, Valley and Ridge, and the Blue Ridge; the map area also includes rocks of the Coastal Plain and Appalachian Plateaus provinces. Each province contains unique packages of rocks that influenced the character of the canal and towpath. The ages of the bedrock encountered along the length of the park range from Mesoproterozoic to Jurassic and represent a variety of tectonic and depositional environments. The different rock types and surficial deposits dictated the various construction methods for the canal, which was excavated in Quaternary flood-plain deposits as well as through bedrock. The ancient course of the Potomac River and the deposits it left behind also influenced the location of the canal and towpath. The engineers made good use of the many rock types to construct the locks, dams, aqueducts, and culverts that guided water from the Potomac River into the canal and maintained the water level as canal boats traveled between higher elevations in western Maryland to sea level in Washington, D.C. The canal and towpath provide a unique transect across the central Appalachian region for examining the rich geologic diversity and history.
Return migration to Guyana

An analysis of return migration to Guyana is presented using official data and results from a survey of 98 heads of households. The relevant research on return migration to the Caribbean in general is first reviewed. Consideration is given to the number of returning migrants migrant characteristics country from which they are returning reasons for original migration length of stay abroad reasons for return and satisfaction since return. (ANNOTATION)
Global Environment: Water, Air, and Geochemical Cycles

This book is designed to fill the gap between elementary nonscience books on global environment and change and specialized volumes. The scope is on the global environment and specific examples encompass the globe and have strong reference support. Topics covered in the chapters include the following: global environment in terms of atmospheric, water, and oceanic circulations; air chemistry, the greenhouse effect and the ozone hole; global chemical environment; chemical weathering and water chemistry; lakes in terms of physical, biological and chemical processes and cycles; marine littoral environments; and the open ocean.
Rediscovering the collection: Cook Islands material culture in the Museum of New Zealand Te Papa Tongarewa

Artefacts from the Cook Islands have been collected since the Museum of New Zealand Te Papa Tongarewa (Te Papa) opened in 1865 as the Colonial Museum. In this article we provide a historical overview of the Cook Islands collection at Te Papa. We discuss the strengths and weaknesses of the collection, review some of the factors influencing its growth, and consider the possibilities for future collection development. This article is an output of a survey of the Cook Islands collection carried out between 2007 and 2009.
Multiprocessor Resource Allocation for Hard-Real-Time Streaming with a Dynamic Job-Mix

An embedded multiprocessor that can run multiple hard-real-time (HRT) jobs simultaneously has to guarantee that enough resources are available to meet the timing constraints. It is essential that both application model and hardware be tailored to this goal. Moreover, suitable resource allocation and scheduling are needed. This paper proposes a resource allocator that gives guarantees for HRT streaming applications. Because new jobs arrive during operation, resource allocation is performed at run-time. This provides admission control. Resource budget enforcement is handled by local schedulers. We formalize our resource allocation problem and show that it is NP-complete. We developed heuristics to tackle the problem during runtime and evaluated them. A modified First-fit Vector Bin-Packing algorithm provides a good solution; it can allocate 95% of the resources, while handling a large number of job arrivals and departures on a heavily loaded system.
UCB Provably Learns From Inconsistent Human Feedback

In this paper, we study how to learn from inconsistent human feedback in the setting of combinatorial bandits with semi-bandit feedback – where an online learner in every time step chooses a size-k set of arms, observes a stochastic reward for each arm, and endeavors to maximize the sum of the per-arm rewards in the set. We consider the challenging setting where these per-arm rewards are not only set-dependent, but also inconsistent: the expected reward of arm “a” can be larger than arm “b” in one set, but smaller in another. Incon-sistency is often observed in practice, falls outside the purview of many popular semi-bandit models, and in general can result in it being combinatorially hard to find the optimal set. Motivated by the observed practice of using UCB-based algorithms even in settings where they are not strictly justified, our main contribution is to present a simple assumption - weak optimal set consistency. We show that this assumption allows for inconsistent set-dependent arm rewards, and also subsumes many widely used models for semi-bandit feedback. Most importantly, we show that it ensures that a simple UCB-based algorithm finds the optimal set, and achieves O (cid:16) min( k 3 n log T ϵ , k 2 √ nT log T ) (cid:17) regret (which nearly matches the lower bound).
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Purpose – The purpose of the paper is to describe and analyze differences in patients ’ quality perceptions of private and public primary care centers in Sweden. Design/methodology/approach – The article explores the differences in quality perceptions between patients of public and private primary care centers based on data from a large patient survey in Sweden. The survey covers seven dimensions, and in this paper the measure Overall impression was used for the comparison. With more than 80,000 valid responses, the survey covers all primary care centers in Sweden which allowed for a detailed analysis of differences in quality perceptions among patients from the different categories of owners. Findings – The article contributes with a detailed description of different types of private owners: not-for-profit and for profit, as well as corporate groups and independent care centers. The results show a higher quality perception for independent centers compared to both public and corporate groups. Research limitations/implications – The small number of not-for-profit centers (21 out of 1,117 centers) doesnotallowforclearconclusionsforthisgroup.Theresults,however,indicateanevenhigherpatientqualityperceptionfornot-for-profitcenters.Thestudyfocusondescribingdifferencesinqualityperceptionsbetween theownercategories.Futureresearchcancontributewithexplanationstowhyindependentcarecentersreceivehigherpatientsatisfaction. Social implications – The results from the study have policy implications both in a Swedish as well as international perspective. The differentiation between different types of private owners made in this paper opens up for interesting discussions on privatization of healthcare and how it affects patient satisfaction. Originality/value – The main contribution of the paper is the detailed comparison of different categories of private owners and the public owners.
Ultrasound-guided percutaneous needle electrolysis and rehabilitation and reconditioning program following a hamstring injury reduces “return to play” time in professional soccer players: A case series

This case series follows the treatment protocol after a grade 2 injury to the proximal semitendinosus muscle using US-guided Percutaneous Needle Electrolysis (PNE) and Rehab & Reconditioning program (RRP) in two professional soccer players. The injury was diagnosed using magnetic resonance imaging (MRI) and ultrasound imaging. The players received one session of PNE 48 hours after the injury. The indoor's phase of the RRP started 24 hours after the PNE technique and then the player proceeded to perform an on-field's phase, following which the players returned to train with the team. The effectiveness of the program was measured by comparing the match-performance data collected through Global Positioning System (GPS) in two matches before and five matches after injury and with ultrasound imaging analyzing the evolution of the muscle injury. No adverse effects were identified during or after the US-Guided PNE technique. Both players missed a single competitive game because of injury (layoff= 16 and 14 days). The GPS variables studied showed similar values before and after injury. PNE and PRR protocol improves the results of the initial phase of muscle repair and reduces the time to return to training and return to play, maintaining the GPS parameters that the players need in high performance.
Have Productivity and Pay Decoupled in the UK?

In the long-run at the macro level, the growth in real pay of workers tends to follow that of labour productivity. In recent years, however, there have been concerns that this relationship has broken down and that pay has become “decoupled” from productivity, growing much more slowly, and leading to a fall in the labour share. This has been a well-documented phenomenon in the United States (US) since the early 1980s. By contrast, we show that in the United Kingdom (UK), employee mean hourly compensation has grown at the same rate as labour productivity between 1981 and 2019. However, there has been a divergence between median employee hourly wage growth and productivity growth of about 25 percentage points. About three-ﬁfths of this “overall decoupling” is due to increasing inequality (mean wages growing faster than median wages) and one-third is due to the increased non-wage compensation costs, in particular employer pension contributions. However, this analysis relates to employee compensation. The average self-employed worker has seen their income grow by only 50 per cent, compared to 80 per cent for the average employee. Using micro-data, we show that this gap can essentially be explained by (i) the growth in the numbers of “solo self-employed” (who have relatively low incomes), and (ii) a much greater fall in hours worked by the self-employed than for the employed. Finally, if we “correct” the labour share for self-employment and non-wage labour costs, the UK labour share has fallen by about 3.5 percentage points over the last four decades.