Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Spaces: Occupancy Detection using Adaptive Back-Propagation Neural Network

In recent years, the concept of smart spaces and occupancy detection is crucial for creating more efficient and sustainable buildings that prioritize both energy efficiency and occupant comfort. By leveraging advanced technologies and real-time data analysis, building managers can optimize energy consumption, reduce operating costs, and improve the overall user experience of the space. One important aspect of this optimization process is the detection of occupancy in smart buildings, which can help to control various systems like Heating, Ventilation, Air Conditioning (HVAC) and lighting. Accurate occupancy detection is a key factor in reducing energy consumption and cost optimization for building control systems, particularly in the areas of HVAC and lighting. To this end, Adaptive Back-Propagation Neural Network (ABPNN) algorithms is applied to a dataset generated by CO2, humidity, temperature, and light sensors to enhance the accuracy of occupancy detection in smart spaces.In this study, the ABPNN based Bayesian Regularization (BR) algorithm is found to have the highest precision rate, with 70% (5701 samples) of the dataset used for training, 15% (1221 samples) for validation, and 15% (1221 samples) for testing. Furthermore, the BR algorithm demonstrated an impressive accuracy rate of 99.8% when compared to other algorithms. Smart spaces with advanced sensors and BR algorithms, such as the BR algorithm, accurately detects occupancy and optimize systems like HVAC and lighting to improve energy efficiency and occupant comfort. This reduces energy consumption and operating costs, while providing insights for optimizing space usage and improving overall building performance.
Delay Root Cause Analysis and 3D Modeling of LTE Control Communication Using Machine Learning

This study investigates and evaluates delay root cause analysis and 3D modeling of LTE control communication utilizing sophisticated machine learning for network testing. The research studied LTE protocols for 5th-generation mobile telephony and provided guidelines for controlling LTE frequency for background knowledge, although it used an independent technique that did not employ LTE standards. 512 elements of input-output MIMO were employed for 100-GHz and 128 elements for mid-band sub-6-GHz. LOS is always 0.5. This paper is about LTE, not 3D modeling of LTE control path loss type communication using machine learning. This work’s route loss depends on cross-pol beam LTE polarization (±45o). The receiver (Rx) operations and transmitter (Tx) activities in the estimated distance of 0.5 km at an approximate altitude of 15.25 m. Distance, handover authentication, rain, atmosphere, and sub-6GHz vs 100GHz weather conditions affect path loss. The methodology has enhanced the spatial variety by boosting transmitting power and transmitting efficiency. Authorizing and sanctioning ANN-based LTE frequency for both mid-band sub-6-GHz and 100-GHz is possible due to its planning and development using open-source material and strategy with high transmission power and rate under doubtful handover confirmation using MIMO input/yield receiving wires. This theory examines LTE innovation dimensioning as unbiased for various handover verification and allows input boundary alterations for various organization arrangement setups for LTE recurrent data transmission from 6 GHz to 100 GHz for three climate sorts. This cycle should be seen as an undeniable level way to examine LTE networks under various air conditions. Using signal handling tool compartment and explicit AI-based ANN calculation from AI toolkit in MATLAB R2019a, it is possible to create a result answer for three climate types in a dataset with an LTE communication level of exactness of downpour assimilation and abundance foliage miss fort.
Studying and Complementing the Use of Identifiers in Logs

Logs contain a large amount of curated run-time information about the process of a software. Modern software systems have become more complex and larger in scale. They are typically executed in parallel or distributively, resulting in interleaved software logs and making log analysis challenging. Despite extensive research on automated logging analysis, none to our knowledge focuses on the use of logs, and they rarely augment logs to help with simpler analysis. Software log IDs are unique identifiers that developers can use to group and filter log entries. However, we found that, on average, only 21% of logging statements produce IDs, which can lead to loss of information in the log file. We propose LTID, a static analysis approach on log IDs, to remediate the aforementioned issue by extracting a dependency relation between log statements from source code. We build a dependency graph using static analysis and compute the dominance relations of each logging statement. We then propagate IDs to logs that do not contain them based on the dependency graph. We studied 21 well-known Java open-source software subjects and were able to inject IDs on average into 12% of logs without IDs. Through an open coding process, we also establish a categorization, which has a Cohen’s Kappa agreement coefficient of 0.74, of the information gained to better understand the relations recovered by the ID propagation process.
Using Secret Sharing to Store Cryptocurrency

ABSTRACT We describe Shamir's secret sharing scheme and explain how it can be used for secure and redundant cryptocurrency storage. We include samples of individual and group assignments that can be used in an upper-division cryptology class for students who are familiar with modular arithmetic. It takes about one class to cover Shamir's secret sharing, but additional time can be spent on the described coding project about splitting mnemonic Bitcoin seeds into shares. We also provide references for topics of further study that can you use for student research projects.
Multimodal Mutual Attention-Based Sentiment Analysis Framework Adapted to Complicated Contexts

Sentiment analysis has broad application prospects in the field of social opinion mining. The openness and invisibility of the internet makes users’ expression styles more diverse and thus results in the blooming of complicated contexts in which different unimodal data have inconsistent sentiment tendencies. However, most sentiment analysis algorithms only focus on designing multimodal fusion methods without preserving the individual semantics of each unimodal data. To avoid misunderstandings caused by ambiguity and sarcasm in complicated contexts, we propose a multimodal mutual attention-based sentiment analysis (MMSA) framework adapted to complicated contexts, which consists of three levels of subtasks to preserve the unimodal unique semantics and enhance the common semantics, to mine the association between unique semantics and common semantics and to balance decisions from unique and common semantics. In the framework, a multiperspective and hierarchical fusion (MHF) module is developed to fully fuse multimodal data, in which different modalities are mutually constrained and the fusion order is adjusted in the next step to enhance cross-modal complementarity. To balance the data, we calculate the loss by applying different weights to positive and negative samples. The experimental results on the CH-SIMS multimodal dataset show that our method outperforms existing multimodal sentiment analysis algorithms.The code of this work is available at https://gitee.com/viviziqing/mmsacode.
Effect of Big Data Analytics on Internal Audit Case: Credit Suisse

New digital technologies are disrupting the organization of companies, leading them to change their internal processes and rethink their businesses. This is particularly the case for audit firms, which have to deal with the arrival on the market of specialists in digital and big data analysis that could eventually come to compete with them. This article aims to examine the impact of integrating analytics into the internal audit process on its effectiveness by studying the case of Credit Suisse while using the analytical descriptive method. The research results confirm the positive impact of big data analysis on the effectiveness of internal audit and emphasize the need to adopt advanced analytical technological techniques by this function.
Increased efficiency of short-pulse laser-generated proton beams from novel flat-top cone targets

Ion-driven fast ignition (IFI) may have significant advantages over electron-driven FI due to the potentially large reduction in the amount of energy required for the ignition beam and the laser driver. Recent experiments at the Los Alamos National Laboratory’s Trident facility employing novel Au flat-top cone targets have produced a fourfold increase in laser-energy to ion-energy efficiency, a 13-fold increase in the number of ions above 10MeV, and a few times increase in the maximum ion energy compared to Au flat-foil targets. Compared to recently published scaling laws, these gains are even greater. If the efficiency scales with intensity in accordance to flat-foil scaling, then, with little modification, these targets can be used to generate the pulse of ions needed to ignite thermonuclear fusion in the fast ignitor scheme. A proton energy of at least 30MeV was measured from the flat-top cone targets, and particle-in-cell (PIC) simulations show that the maximum cutoff energy may be as high as 40–45MeV.
Modified Heat Method using GMRES for Geodesic Distance

The computation of geodesic distance has applications in a wide variety of fields. Several attempts have been made in the last decade to compute geodesic on curved surfaces by formulating a distance function for the whole mesh including the heat method by Keenan crane. The proposed method first creates a vector unit field with a gradient equal to the true geodesic function and then integrates the unit vector field over the surface. Integrating the unit vector field over the surface requires solving a Poisson equation, which consists of two sparse linear systems of equations. The heat method proposed by Keenan crane uses a direct solver to solve the Poisson equation which increases the total memory and time consumption of the original heat method and makes it unsuitable for large meshes. The proposed method uses the generalized minimal residual method (GMRES) for solving the set of sparse linear systems of equations generated. The use of an iterative solver not only significantly reduces the memory consumption but also computes geodesic distance in less time than the heat method for large meshes which makes the proposed method preferable for large meshes. The result shows that the proposed method can efficiently compute the geodesic distance for bigger mesh in less time than the heat method and Biconjugate gradient stabilized method with significantly reduced memory usage for considered mesh data.
Parametric analysis of viscoelastic hyperboloidal helical rod

The objective of this study is to perform a pioneering research about a viscoelastic hyperboloidal helical rod having a standard type of distortional behavior and a Kelvin type of bulk compressibility. Field equations are based on the Timoshenko beam theory, and the exact curvatures of the hyperboloidal geometry are considered through the formulation. The numerical analysis is carried out by the mixed finite element method, considering the rotary inertia, in the Laplace space, and the results are transformed back to time space numerically using the modified Durbin’s algorithm. A cantilevered hyperboloidal helical rod having solid circular, hollow circular, and thin-walled hollow circular cross sections is handled, and the rod is loaded by rectangular and triangular impulsive types of point load at the tip. Through the analysis, different values of retardation time, three different relaxation functions associated with shear modulus, and three different creep functions associated with bulk modulus are handled. Finally, a benchmark example is presented, and the influence of the loading and the material parameters on the helix geometry is discussed.
Fabrication of Nanorollers

There exist many difficulties to fabricate a machine in nanometer-scale. One of the key technologies for nanofabrication is how to get a nanocomponent with definite sizes. In this paper, we explore a new method to fabricate nanorollers whose size can be controlled by cutting long carbon nanotube into pieces at appointed positions using the tip of atomic force microscope (AFM). Sample was prepared so that parts of carbon nanotubes are pressed by gold coating to keep them fixed while cutting. The relaxation process of a nanoroller while manipulating was observed.
Compact noncontraction semigroups of affine operators

We analyze compact multiplicative semigroups of affine operators acting in a finite-dimensional space. The main result states that every such semigroup is either contracting, that is, contains elements of arbitrarily small operator norm, or all its operators share a common invariant affine subspace on which this semigroup is contracting. The proof uses functional difference equations with contraction of the argument. We look at applications to self-affine partitions of convex sets, the investigation of finite affine semigroups and the proof of a criterion of primitivity for nonnegative matrix families. Bibliography: 32 titles.
Deployable Robotic Structures via Passive Rigidity on A Soft, Growing Robot

Deployable and reconfigurable structures use shape-changing designs to transform between different forms and create usable structures, often from small initial packages. While these structures create reliable transformations, the exact shapes must be defined at design and manufacturing time. However, many applications in unstructured environments would benefit from deployable structures that can adjust to the circumstances of the application on demand. To address this need for autonomous behavior, we propose deployable robotic structures, combining soft shape-changing robots with passive and permanent stiffening. The specific implementation in this paper uses chemical curing capable of creating stiffness change at arbitrary locations along a soft growing robot without impeding the function of the robot or requiring a continuous supply of energy to maintain its rigidity. In structural testing, the application of this method is able to drastically increase load resistances axially by an average of 64 N and transversely by an average of 2.18 Nm. Finally, two demonstrations are performed, which show how this combination of soft growing robot and permanent stiffening can increase the structure's carrying capacity and expand the robot's navigational capabilities, showing the potential of deployable robotic structures.
All-optical switching of nonlinear hyperbolic metamaterials in visible and near-infrared regions

Taking advantage of the Kerr nonlinearities of metallic nanolayers and graphene (G) and the well-known pump–probe method, we show that 200-nm-wide slabs of hyperbolic metamaterial (HMM) composed of 14 vertically stacked unit cells can be switched all optically between the negative refraction regime (ON state) and the no-transmission regime (OFF state), when illuminated by appropriately chosen TE-polarized Gaussian pump and TM-polarized CW probe. Finite element method numerical simulations show that the HMM slab with unit cells made of Au/Al2O3 nanolayers suffering from a relatively large plasmonic loss exhibits an ON state transmittance spectrum limited to 52%–72% over the visible range of 585–600 nm, when a 500 V/μm pump signal is utilized. Similar numerical results obtained for the HMM slab whose unit cells are made of Au/Al2O3/G/Al2O3/Au nanolayers show that a 4 V/μm pump signal is enough to stimulate the Kerr effect, resulting in a ∼97% ON state transmittance over the ultra-narrowband of 803–805 nm. Moreover, simulations show that when the unit cells are made of Ag/Si/G/Si/Ag nanolayers, a 95-nm-wide ON state transmittance of 49%–99% over the IR range of 800–895 nm can be obtained with a 19.5 V/μm pump. The proposed HMM structures can be used as all-optical switches in future optical digital systems.
Optical trapping of nanowires

The ability to hold and manipulate nanowires using optical beams opens up a range of applications from force sensing to directed assembly. For this reason, optical trapping of nanowires has received much recent interest. In the following article we present a detailed computational investigation of the stability and general behaviour of these systems. It is found that relatively high index wires can be trapped. Furthermore, the properties of the trap vary with the parameters of the nanowire in characteristic ways. For example, the trap stiffness in the direction parallel to the axes of the beam and the wire falls off with increasing length, and can be made arbitrarily small. At the same time the other translational stiffness coefficients attain a limit in which the stiffness perpendicular to the polarization direction is approximately one half of that in the parallel direction. Rotational stiffness coefficients are seen, conversely, to increase steadily with length. These observations are explained in terms of a simple analytical model that supports the numerical calculations.
Neurodegenerative clinical records analyzer: detection of recurrent patterns within clinical records towards the identification of typical signs of neurodegenerative disease history

When treating structured health-system-related knowledge, the establishment of an over-dimension to guide the separation of entities becomes essential. This is consistent with the information retrieval processes aimed at defining a coherent and dynamic way – meaning by that the multilevel integration of medical textual inputs and computational interpretation – to replicate the flow of data inserted in the clinical records. This study presents a strategic technique to categorize the clinical entities related to patients affected by neurodegenerative diseases. After a pre-processing range of tasks over paper-based and handwritten medical records, and through subsequent machine learning and, more specifically, natural language processing operations over the digitized clinical records, the research activity provides a semantic support system to detect the main symptoms and locate them in the appropriate clusters. Finally, the supervision of the experts proved to be essential in the correspondence sequence configuration aimed at providing an automatic reading of the clinical records according to the clinical data that is needed to predict the detection of neurodegenerative disease symptoms.
A Granular Computing-Based Deep Neural Network Approach for Automatic Evaluation of Writing Quality

As a subjective behaviour relying on expert experience, automatic evaluation of writing quality always remains a technical issue. It requires both effective semantic understanding and structure analysis towards writing contents. To deal with this challenge, this paper combines speed superiority of granular computing and the effective approximation ability of deep neural network towards nonlinear mapping relationships. On this basis, a granular computing-based deep neural network approach for automatic evaluation of writing quality, is developed in this paper. Specifically, the granular computing is used as the front-end processor of deep neural network, so as to reduce the following information density. Then, the deep neural network serves as the main backbone structure to extract semantic features of writing contents. Such combination of two modules can improve processing speed in large-scale textual analysis scenes, under insurance of evaluation performance. The simulation experiments are also conducted to test performance of the proposed technical framework, and the results show that both high accuracy and proper running speed are endowed with the proposal.
Science Capabilities of the Common Habitat

The Common Habitat is the primary habitable element in a conceptual architecture feasibility study for long-duration space exploration with an eight-person crew size. On lunar or planetary surfaces, the Common Habitat forms the core of a Surface Base Camp. In microgravity, the Common Habitat is the core of the Deep Space Exploration Vehicle (DSEV), an in-space transportation spacecraft. The Common Habitat employs a horizontal orientation that is divided internally into a lower deck, mid deck, and upper deck, roughly separating outfitting into individual, work, and group functions. On a planetary surface, the habitat is incorporated into a base camp, located near the south pole in the case of the Moon. The Mars base camp is currently location agnostic. Each base camp is divided into habitation, landing, resource production, and power zones. In microgravity, the habitat is incorporated into the Deep Space Exploration Vehicle, a vessel capable of transporting the crew within the inner solar system. In addition to crew and teleoperated control of external science assets, the Common Habitat employs a suite of life and physical science laboratory systems to enable it to support science investigations across a variety of destination environments, primarily featuring the Moon and Mars, along with the intervening interplanetary space. Other potential destinations include Near Earth Asteroids and Venus and Earth orbits. Located in the aft starboard section of the Common Habitat mid deck, the life science laboratory supports primarily biology and human research. In its baseline configuration, the laboratory includes horizontal work surfaces, freezers, multiple gloveboxes, sample transfer/exposure capability, large instruments, and reconfigurable ISPR-compatible payload bays. The Medical Care Facility and exercise facility can also support life science research. In the aft port section of the mid deck, the physical science laboratory supports physics, chemistry, materials science, geology, and remote sensing (including astrophysics, heliophysics, Earth science, planetary science, and meteorology). It provides a baseline of similar ISPR-compatible payload bays and adds additional freezers, including those for cryogenic sample storage, a remote sensing workstation, more gloveboxes, also with sample transfer/exposure, combustion chambers, fluid mechanics chambers, and a gas chromatograph. It leverages the Command & Control Center for teleoperations of mobile science assets. Both laboratories are highly modular, with the ability to swap out both payloads and instruments on an as-needed basis. The integrated science outfitting of the Common Habitat positions its crew to contribute to all of NASA's Moon to Mars science objectives and extend human understanding into the inner solar system.