Miracle-dz/UniFAR-FTUs huggingface dataset:
FastSecNet: An Efficient Cryptographic Framework for Private Neural Network Inference

Private neural network inference has demonstrated great importance in various privacy-critical scenarios. However, the primary challenge remaining in prior works is that the evaluation on encrypted data levies prohibitively high runtime and communication overhead. In this work, we present <monospace>FastSecNet</monospace>, an efficient two-party cryptographic framework for private inference in the dealer-based pre-processing setting. Specifically, 1) <monospace>FastSecNet</monospace> provides an efficient ReLU protocol for the evalution of non-linear layers, which is built up on a recent advanced cryptographic primitive, function secret sharing (FSS). The core of this construction are an optimized ReLU representation and a customized FSS-based ReLU protocol. 2) For linear layer evaluation, we first propose an efficient PRG-based pre-processing protocol based on the fact that one of the inputs is uniformly random in the offline phase. Then, the online phase only communicates one element and consists of lightweight secret-sharing operations in a ring. Extensive evaluations conducted on 4 real-world datasets and 9 neural network models demonstrate that during the online phase, <monospace>FastSecNet</monospace> achieves <inline-formula> <tex-math notation="LaTeX">$14\times $ </tex-math></inline-formula> less runtime and <inline-formula> <tex-math notation="LaTeX">$18\times $ </tex-math></inline-formula> less communication cost compared to the state-of-the-art.
Passenger choice analysis for seat capacity control: a pilot project in Scandinavian Airlines

A yield management research project in Scandinavian Airlines System (SAS) focuses upon passenger preferences in situations where the preferred flight, class alternative is sold out. The passenger then could deviate to a competitor; be recaptured onto another SAS flight; or pay the higher fare on a higher booking class (be a buyup). Class allocations possibly should reflect preferences for such options, particularly when prices are becoming operational control variables. Passenger preferences were estimated from choice analyses of interviews and logged bookings transactions. Needs of developing SAS present origin-to-destination heuristic were identified, and alternatives for revising the class allocation models were assessed. Issues to be resolved prior to an implementation were identified.
The Use of Hydrogels for the Treatment of Bone Osteosarcoma via Localized Drug-Delivery and Tissue Regeneration: A Narrative Review

Osteosarcoma is a malignant tumor of bone that leads to poor mortality and morbidity. Management of this cancer through conventional methods involves invasive treatment options that place patients at an increased risk of adverse events. The use of hydrogels to target osteosarcoma has shown promising results both in vitro and in vivo to eradicate tumor cells while promoting bone regeneration. The loading of hydrogels with chemotherapeutic drugs provides a route for site-specific targeted therapy for osteosarcoma. Current studies demonstrate tumor regression in vivo and lysis of tumor cells in vitro when exposed to doped hydrogel scaffolds. Additionally, novel stimuli-responsive hydrogels are able to react with the tissue microenvironment to facilitate the controlled release of anti-tumor drugs and with biomechanical properties that can be modulated. This narrative review of the current literature discusses both in vitro and in vivo studies of different hydrogels, including stimuli-responsive, designed to treat bone osteosarcoma. Future applications to address patient treatment for this bone cancer are also discussed.
Musical Source Separation Using Time-Frequency

o This article deals with the source separation prob- lem for stereo musical mixtures using prior information about the sources (instrument names and localization). After a brief review of existing methods, we design a family of probabilistic mixture generative models combining modied positive Independent Sub- space Analysis (ISA), localization models and Segmental Models (SM). We express source separation as a Bayesian estimation problem and we propose efcient resolution algorithms. The resulting separation methods rely on a variable number of cues including harmonicity, spectral envelope, azimuth, note duration and monophony. We compare these methods on two synthetic mixtures with long reverberation. We show that they outperform methods exploiting spatial diversity only and that they are robust against approximate localization of the sources.
Geometric Feature Detection of Space Targets Based on Color Space

The identification and estimation of space non-cooperative target is a prerequisite for space rendezvous and on-orbit service, most of which have geometric structures such as docking loops, antennas, solar panels, and the rest are covered with multiple layers of material for thermal insulation. In this paper, a color space-based method to detect space noncooperative target geometric feature is proposed. First, we split the 3 channels of the RGB image and convert it into another format in HSV (Hue, Saturation, Value) color space. Second, by comparing the 6-channel images under different lighting conditions, it is determined that the Hue channel is used for fine identification of key features. Finally, experiments demonstrate the effectiveness of our proposed method, which applied pass-through filtering method by Hue Channel to detect geometric features under extreme lighting conditions in space.
The Role of Belgian Airborne Sniffer Measurements in the MARPOL Annex VI Enforcement Chain

The Royal Belgian Institute of Natural Sciences launched its airborne sniffer program in 2015 whereby a custom-built sniffer sensor was installed onboard the Belgian coastguard aircraft enabling the measurement of SO2 and NOx emitted by ocean-going vessels (OGVs). The data gathered on non-compliant OGVs were subsequently sent to port inspection authorities, who were then able to trigger inspections more rapidly than had they not had the data from the aircraft. This study reveals the added value of airborne alerts on port inspection effectiveness, a subject that had not been previously documented. This article demonstrates that airborne alerts have not only led to increased sanctions but have also drastically improved the efficiency of port inspection authorities, leading to a 50% reduction in the enforcement cost per confirmed violation. Port inspection authorities were able to follow up on 46% of the generated Fuel Sulphur Content (FSC) alerts. Of the alerts that were followed up, 43% were confirmed as non-compliant after inspection. This means that 20% of the total number of generated airborne alerts, which includes those that were not able to be followed up, met conditions for legal sanctioning. In contrast, for NOx alerts, only limited follow-ups were conducted by port inspection authorities. None of the alerts were confirmed with those inspections, mainly due to the lack of inspection mechanisms for real-world NOx emissions under IMO and EU regulations. In addition, for this study, a large-scale remote FSC measurement validation analysis was conducted for the first time, comparing airborne FSC measurements and FSC reference data. In order to obtain FSC reference data, onboard measurements from exhaust gas cleaning systems (EGCSs) were collected, together with fuel samples from Belgian port inspection authorities. The validation analysis revealed that the empiric deviation in the airborne FSC measurements with the FSC reference data was 9%, which was significantly lower than the 25% uncertainty used in the reporting of the alerts. This study helps pave the way for an increased role of airborne monitoring in the MARPOL Annex VI enforcement chain.
Hybrid CNNLBP using Facial Emotion Recognition based on Deep Learning Approach

Facial Emotion Recognition System is one of the emerging technologies and it has been a quite challenging task to recognize the human facial expressions using computer algorithms in emotion analysis rather than the humans. In human’s ideology, facial expressions like sorrowful, joyful, frightened, anger, disgust are assumed as a fundamental part. The main objective of Facial emotion Recognition System research is to focus on the traits of people with criminal associations facial expressions. As the traditional algorithms haven’t met the human needs, where Machine learning and deep learning algorithms have gained great success. There are many techniques applied to gain efficient results in identifying facial expressions for Human-machine interaction, monitoring security and treating patients in the medical field are some of the applications of facial expression recognition. So, it is necessary to build a model-based system to understand human emotions in different scenarios. The Convolutional Neural Network (CNN) algorithm is used to know the facial expressions of humans. On this framework, the CNN layers are upgraded, and the LBP is incorporated with it to merge multiple networking algorithms to develop the human emotion model. In the end, to check the validity of the new technique, CNNLBP is implemented. The experimental result show that accuracy of emotion recognition in between the training and testing phases, the CNNLBP model achieves an average accuracy of 98.3% compared to other traditional models.
Turbulence Management and Relaminarisation

The last two decades have witnessed an intensifying effort in learning how to manage flow turbulence: it has in fact now become one of the most challenging and prized technological goals in fluid dynamics. The goal itself is of course not new. More than a hundred years ago, Reynolds already listed factors conducive to laminar and to turbulent flow (including among them curvature and acceleration). Furthermore, it is in retrospect clear that there were several early instances of successful turbulence management. Examples are the reduction in drag achieved with a ring-trip placed on the front of a sphere or the insertion of a splitter-plate behind a circular cylinder; by the early 1950s there were numerous exercises at boundary layer control. Although many of these studies were interesting and suggestive, they led to no spectacularly successful practical application, and the effort petered out in the late 1950s.
Lie Recognition with Multi-Modal Spatial–Temporal State Transition Patterns Based on Hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory

Recognition of lying is a more complex cognitive process than truth-telling because of the presence of involuntary cognitive cues that are useful to lie recognition. Researchers have proposed different approaches in the literature to solve the problem of lie recognition from either handcrafted and/or automatic lie features during court trials and police interrogations. Unfortunately, due to the cognitive complexity and the lack of involuntary cues related to lying features, the performances of these approaches suffer and their generalization ability is limited. To improve performance, this study proposed state transition patterns based on hands, body motions, and eye blinking features from real-life court trial videos. Each video frame is represented according to a computed threshold value among neighboring pixels to extract spatial–temporal state transition patterns (STSTP) of the hand and face poses as involuntary cues using fully connected convolution neural network layers optimized with the weights of ResNet-152 learning. In addition, this study computed an eye aspect ratio model to obtain eye blinking features. These features were fused together as a single multi-modal STSTP feature model. The model was built using the enhanced calculated weight of bidirectional long short-term memory. The proposed approach was evaluated by comparing its performance with current state-of-the-art methods. It was found that the proposed approach improves the performance of detecting lies.
Carotenoid pixels characterization under color space tests and RGB formulas for mesocarp of mango's fruits cultivars

This study experimented the pulp (mesocarp) of fourteen cultivars were healthy ripe of Mango fruits (Mangifera indica L.) selected after picking from Mango Spp. namely Taimour [Ta], Dabsha [Da], Aromanis [Ar], Zebda [Ze], Fagri Kelan [Fa], Alphonse [Al], Bulbek heart [Bu], Hindi- Sinnara [Hi], Compania [Co], Langra [La], Mestikawi [Me], Ewais [Ew], Montakhab El Kanater [Mo] and Mabroka [Ma] . Under seven color space tests included (RGB: Red, Green and Blue), (CMY: Cyan, Magenta and Yellow), (CMY: Cyan, Magenta and Yellow), (HSL: Hue, Saturation and Lightness), (CMYK%: Cyan%, Magenta%, Yellow% and Black%), (HSV: Hue, Saturation and Value), (HºSB%: Hueº, Saturation% and Brightness%) and (Lab). (CMY: Cyan, Magenta and Yellow), (HSL: Hue, Saturation and Lightness), (CMYK%: Cyan%, Magenta%, Yellow% and Black%), (HSV: Hue, Saturation and Value), (HºSB%: Hueº, Saturation% and Brightness%) and (Lab). Addition, nine formula of color space tests included (sRGB 0÷1, CMY, CMYK, XYZ, CIE-L*ab, CIE-L*CH, CIE-L*uv, Yxy and Hunter-Lab) and (RGB 0÷FF/hex triplet) and Carotenoid Pixels Scale. Utilizing digital color photographs as tool for obtainment the natural color information for each cultivar then the result expounded with chemical pigment estimations. Our location study in the visual yellow to orange color degrees from the visible color of electromagnetic spectrum in wavelength between (~570 to 620) nm and frequency between (~480 to 530) THz. The results found carotene very strong influence in band Red while chlorophyll (a & b) was very lower subsequently, the values in band Green was depressed. Meanwhile, the general ratios percentage for carotenoid pixels in bands Red, Green and Blue were 50%, 39% and 11% as orderliness opposite the ratios percentage for carotene, chlorophyll a and chlorophyll b which were 63%, 22% and 16% approximately. According to that the pigments influence in all color space tests and RGB formulas. Band Yellow% in color test (CMYK%) as signature color for carotene. Bands K% and band C were equal zero in almost cells indicted to a mystical induction for chlorophyll (a & b). The results detection two bands regard as numeric chromatic filter. In RGB formulas the digits of carotenoid pixels under the effects of the various bands followed two characters including (Separation and Isotopic) effects these consider numeric chromatography. Digits of carotenoid pixels physically are disparate the trend and features under each band mostly. The RGB formulas present treatment for the symmetrically values in the columns data of the total pigments percentage and color space tests. Our objective physical study for pigments carotene to present standard evolution for pigment estimations. Addition to study the possibility to obtainment numeric chromatography for separation accuracy of the pigments.
A preliminary study on view independent panoptic scene change detection

Exploring the indoor environment and finding unknown objects that appeared in a scene are important for research of scene understanding by a robot. However, background subtraction is traditionally used for segmenting unknown object regions, and it cannot be directly used for a moving camera on the robot. In this paper, we propose a task called view-independent panoptic scene change detection, which is the task of segmenting unknown object regions by comparing two images from different viewpoints before and after the objects appear. In this paper, we propose a method for segmenting unknown object regions by modeling a segmented known instance region as background. For the background modeling, we introduce two methods: histogram-based and deep metric-learning-based methods. In addition, we create a new panoptic scene change detection dataset consisting of images taken from different camera views. Through experiments, we confirm that the proposed method can segment regions of unknown class instances; the deep metric-learning-based method performs more accurately than the histogram-based method, achieving good performance on the change detection dataset.
Research on evaluation method of appearance quality of cooked rice based on image processing technology

In order to evaluate the appearance quality of cooked rice accurately,the appearance eigenvalues of cooked rice was measured by image processing technology,the correlativity between eigenvalues and sensory evaluation was investigated.The results showed that the difference in variety of indica rice was bigger than japonica rice,and the subjectivity existed by sensory evaluation.There was a good correlation between test indicators based on image processing technology and sensory evaluation of cooked rice.The regression equations and the regression coefficients which were built by various sensory evaluation indices(as dependent variables) reached significant level,and the predicted results were consistent with the results from sensory evaluation,the fitting degree between predicted value and sensory score was high up to 97.6%. So appearance sensory evaluation can be replaced partly by image processing technique to evaluate the appearance of cooked rice accurately.
Retrieval of Precipitable Water from Observations in the Split Window over Varying Surface Temperatures

Abstract The split window technique makes use of two differentially absorbing channels in the 11 μm region to remove the attenuating effects of atmospheric absorption so as to achieve a better estimate of the underlying skin temperature than could be produced by a single channel measurement. Since the primary absorber in this region is water vapor, it follows that split window measurements should be able to produce bulk water vapor retrievals as well. When observations are made with split window channels under conditions where the surface contribution to measured radiance changes, but the atmospheric contribution does not, it is possible to estimate the ratio of the transmittance of the two split window channels. This transmittance ratio is inversely related to precipitable water. This paper applies this technique to observations from the Advanced Very High Resolution Radiometer, and the VISSR Atmospheric Sounder, and demonstrates the capability of both instruments to determine precipitable water under tw.
Optimal Design for Synchronization of Kuramoto Oscillators in Tree Networks

In this paper, we develop an optimization framework to optimize phase synchronization in tree networks of coupled Kuramoto oscillators. We consider the \emph{phase cohesiveness} metric, that accounts for the maximum phase difference across all the edges in the network. we address three different optimization problems: (\emph{i}) The \emph{optimal critical coupling problem} in which we design the natural frequency of each oscillator to minimize the required coupling for synchronization (\emph{ii}) the \emph{nodal-frequency design problem}, in which we design the natural frequencies to optimize the phase cohesiveness, and (\emph{iii}) the \emph{edge-weight design problem}, in which we design the link weights. We assume that tuning the natural frequencies and/or modifying the link weights have an associated cost and develop an optimization framework to find the optimal allocation of resources under certain assumptions. We illustrate the effectiveness of our approach via numerical simulations.
Method for detecting surface defects of ceramic tile based on improved Cascade RCNN

In view of the problems of small scale and low contrast of ceramic tile defects, various types of ceramic tile surface defects, and difficulty in realizing high-precision ceramic tile defect detection, a ceramic tile surface defect detection model based on improved Cascade RCNN is proposed to locate and identify the types of ceramic tile surface defects in different texture backgrounds. The improved ResNest network is used to improve the classification ability of the algorithm and optimize the performance of the model. The improved feature pyramid enhanced the feature extraction ability of the algorithm, and improved the accuracy of detecting small-scale defects and low-contrast defects of ceramic tiles. The whole connection structure of the last layer of cascade detectors was modified to double-head structure, which improved the ability of detectors to perform classification and regression tasks, and solved the problem of various kinds of defects on the surface of ceramic tiles. After data collection, 2810 tiles defect pictures were obtained, and then 7934 tiles defect slices were obtained by image segmentation, and the slices were made into tiles defect data sets. Experiments show that this method can achieve 77.8% MAP and 93.9% average positive detection rate, which is higher than Faster RCNN and original Cascade RCNN.
In-field strain and temperature measurements in a (RE)Ba2Cu3O7−x coil via Rayleigh-backscattering interrogated optical fibers

(RE)Ba2Cu3O7-x (REBCO) conductors have overcome technical challenges related to manufacturing quality, length homogeneity, scale-up of piece-length, and joints. There is one remaining technical challenge, however, which is common to all high temperature superconductors and lies in effective detection of failure to prevent material degradation. An innovative technique based on optical fibers interrogated by Rayleigh backscattering has been shown to have advantages over voltage taps at detecting incipient faults. Prior work has experimentally demonstrated the technique in several implementation scenarios, including direct integration of optical fibers into superconducting conductors and cables to create a class of ‘SMART’ conductors and cables that are able to monitor their own health. In this paper, the magnet monitoring technique based on Rayleigh backscattering interrogated optical fibers has been experimentally studied in a model coil subject to external magnetic field, where different fiber integration methods are used to increase selectivity of the fiber sensor to temperature. Results show that the spectral shift displays different features during strain and thermal transients. The implications of the results in terms of potential and limitations of each sensor as well as strain-temperature decoupling are discussed.
Socket Preservation after Tooth Extraction: Particulate Autologous Bone vs. Deproteinized Bovine Bone

Background: The technique of socket preservation after tooth extraction allows for less volumetric decrease after tooth extraction. The aim of this retrospective study was to evaluate differences between alveolar socket preservation performed with deproteinized bovine bone graft and autologous particulate bone graft taken from the mandibular ramus. Materials and Methods: This retrospective study enrolled a total of 21 consecutive patients. A total of 11 patients underwent socket preservation with deproteinized bovine bone graft and collagen matrix (group A), and 10 patients underwent socket preservation performed with particulate autologous bone taken from the mandibular ramus and collagen matrix (group B). All patients received cone beam computed tomography (CBCT) before socket preservation and after four months. Alveolar bone width (ABW) values and alveolar bone height (ABH) values were measured at the first and second CBCT, and the reduction of the values in the two groups was compared. Statistical analysis was performed using Student’s t-test for independent variables, and p values < 0.05 were considered statistically significant. Results: There were no statistically significant differences between ABW reduction of group A and ABW reduction of group B (t-test value p = 0.28). There were no statistically significant differences between ABH reduction of group A and ABH reduction of group B (t-test value p = 0.10). Conclusions: In this retrospective study, no statistical differences were found between the group that received autologous particulate bone compared to the group that received deproteinized bovine bone in socket preservation.