Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral Image Classification From Multi-Sensor Compressive Measurements

Spectral image classification is an active research topic in remote sensing. In this sense, various multi-sensor spectral image fusion algorithms have been recently evaluated via pixel-based classification. In general, the sizes of multi-sensor images challenge the storing and processing capabilities of sensing systems. Therefore, different image fusion algorithms from measurements captured by multi-resolution compressive spectral imaging (CSI) sensors have been proposed. However, the computational costs for reconstructing and fusing spectral images from compressive measurements are high, and these approaches do not consider the huge amount of information embedded in acquired data. In this article, a spectral image classification scheme from multi-sensor CSI projections is developed. Specifically, this scheme includes a feature extraction procedure that exploits the fact that CSI data contain relevant information of the spectral image, and therefore, low-dimensional features can be obtained from measurements. Furthermore, a fusion model is presented to combine the information of the extracted features with the aim of estimating high-resolution classification attributes. Then, a pixel-based classifier is applied to the fused features with the goal of labeling the corresponding high-resolution spectral image. The performance of the proposed classification scheme is compared to other methods on the Salinas Valley data set for different supervised classifiers and various downsampling settings. Extensive simulations on the Pavia University data set are also shown, where the proposed method outperforms other classification approaches that reconstruct and fuse from compressive measurements. Finally, the effectiveness of the proposed classification approach is validated in real multi-sensor data.
A Super-Resolution Convolutional-Neural-Network-Based Approach for Subpixel Mapping of Hyperspectral Images

A new subpixel mapping (SPM) method based on a super-resolution convolutional neural network (SRCNN) is proposed to generate subpixel land cover maps for hyperspectral images. The SRCNN is used to restore the image spatial resolution from a coarse input image, which is equivalent to interpolation. First, an efficient subpixel convolutional neural network, which is a state-of-the-art SRCNN, is utilized to calculate the subpixel soft class value via a transfer learning strategy. Then, a classifier is used to transform the subpixel soft class values to hard-classified land cover maps with the constraint of fraction images. Experiments on three different hyperspectral images demonstrate that the SPM accuracy of the proposed SRCNN-based method is significantly better than those of three traditional SPM methods. In addition, the SRCNN-based SPM method has a simplified calculation process, does not require training data, and is less time consuming. This article provides a new solution for SPM of hyperspectral images.
Locally private Jaccard similarity estimation

Jaccard Similarity has been widely used to measure the distance between two sets (or preference profiles) owned by two different users. Yet, in the private data collection scenario, it requires the untrusted curator could only estimate an approximately accurate Jaccard similarity of the involved users but without being allowed to access their preference profiles. This paper aims to address the above requirements by considering the local differential privacy model. To achieve this, we initially focused on a particular hash technique, MinHash, which was originally invented to estimate the Jaccard similarity efficiently. We designed the PrivMin algorithm to achieve the perturbation of MinHash signature by adopting Exponential mechanism and build the Locally Differentially Private Jaccard Similarity Estimation (LDP‐JSE) protocol for allowing the untrusted curator to approximately estimate Jaccard similarity. Theoretical and empirical results demonstrate that the proposed protocol can retain a highly acceptable utility of the estimated similarity as well as preserving privacy.
SGD on Random Mixtures: Private Machine Learning under Data Breach Threats

In this work, we propose Stochastic Gradient Descent on Random Mixtures (SGDRM) as a simple way of protecting data under data breach threats. We prove that SGDRM converges to a critical point for the least-squares problem and for deep neural networks with linear activations. We also conduct extensive experiments, and observe that SGDRM can be applied to general deep learning tasks as well.
Table Recomputation-Based Higher-Order Masking Against Horizontal Attacks

Masking is a class of well-known countermeasure against side-channel analysis by employing the idea of secret sharing. The theoretical security proof model of higher-order masking was initiated by Ishai, Sahai, and Wagner, and Barthe <italic>et al.</italic> pushed forward it by proposing a more refine security definition named as <inline-formula> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula>-SNI security. In CHES 2016, a new attack called horizontal side-channel attacks (HSCAs) came forward and successfully broke the Rivain–Prouff countermeasure, which has been proved to satisfy the <inline-formula> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula>-SNI security. It presents a dilemma: instead of more secure, masking with higher-order may be more vulnerable due to the HSCA. Although there already exists an effective countermeasure for the Rivain–Prouff scheme, it is quite difficult to apply this method in the table recomputation-based higher-order masking schemes, such as the scheme introduced by Coron in EUROCRYPT 2014. To fill this gap, we propose a new table recomputation-based higher-order masking scheme, named as table compression masking (TCM) scheme. While meeting the <inline-formula> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula>-SNI security, our new countermeasure is also secure against the HSCA. We give the formal security proof under the <inline-formula> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula>-SNI security definition, as well as a heuristic security analysis considering the HSCA. Our analysis shows that, by dividing the full lookup table into many distinct parts and shifting them by refreshed shares, the same share will never be manipulated for more than twice in TCM scheme. This feature gives a heuristic security against HSCA. To our best knowledge, our countermeasure is the first solution for table recomputation-based higher-order masking to resist HSCA.
A Learning Analytics Methodology for Understanding Social Interactions in MOOCs

One of the characteristics of massive open online courses (MOOCs) is that the overall number of social interactions tend to be higher than in traditional courses, hindering the analysis of social learning. Learners typically ask or answer questions using the forum. This makes messages a rich source of information, which can be used to infer learners’ behavior and outcomes. It is not feasible for teachers to process all forum messages and automated tools and analysis are required. Although there are some tools for analyzing learners’ interactions, there is a need for methodologies and integrated tools that help to interpret the learning process based on social interactions in the forum. This paper presents the 3S (Social, Sentiments, Skills) learning analytics methodology for analyzing forum interactions in MOOCs. This methodology considers a temporal analysis combining the social, sentiments, and skill dimensions that can be extracted from forum data. We also introduce LATƎS, a learning analytics tool for edX/Open edX related to the three dimensions (3S), which includes visualizations to guide the proposed methodology. We apply the 3S methodology and the tool to an MOOC on Java programming. Results showed, among others, the action–reaction effect produced when learners increase their participation after instructor's events. Moreover, a decrease of positive sentiments over time and before deadlines of open-ended assignments was also observed and that there were certain skills, which caused more troubles (e.g., arrays and loops). These results acknowledge the importance of using a learning analytics methodology to detect problems in MOOCs.
Upaya Kendali Mutu dan Biaya Program Rujuk Balik Menggunakan Pendekatan Root Cause Analysis

Program Rujuk Balik (PRB) merupakan program yang berpotensi memberi banyak manfaat bagi peserta BPJS Kesehatan yang menderita penyakit kronis. Kajian ini bertujuan mengkaji proses pelaksanaan PRB Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan. Data diperoleh dengan panduan wawancara terstruktur yang ditanyakan kepada manajemen BPJS Kesehatan, penanggung jawab PRB di Puskesmas dan Rumah Sakit serta penanggung jawab apotek yang bekerjasama dengan BPJS Kesehatan. Identifikasi kendala proses pelaksanaan PRB menggunakan pendekatan sistem, sedangkan pendekatan Root Cause Analysis digunakan untuk mencari penyebab yang mendasarinya. Hasil penelitian menengarai masalah utama pelaksanaan PRB adalah 1) ketersediaan obat PRB di Puskesmas terbatas, 2) penumpukan pasien di Fasilitas Kesehatan Rujukan Tingkat Lanjut (FKRTL) sehingga waktu tunggu memanjang, 3) notifikasi status pasien â€œpotensi PRBâ€ pada program VCLAIM di FKRTL cenderung diabaikan, 4) rendahnya kepatuhan tenaga medis di FKRTL dalam melengkapi form rujuk balik, dan 5) sosialisasi mekanisme PRB kepada masyarakat masih kurang. Penyebab dasar timbulnya masalah ini adalah tidak adanya metode evaluasi dan sumber daya manusia dari BPJS yang secara khusus melakukan proses pengawasan dan evaluasi berkala. Direkomendasikan kepada BPJS agar menugaskan staf khusus untuk memantau PRB secara berkesinambungan dan menyeluruh, serta bersama kolegium profesional kedokteran untuk segera menyusun standar ketentuan pasien stabil setiap penyakit yang termasuk dalam PRB.
Stimulated Brillouin scattering in optical fibers with end reflections excited by broadband pump waves

Abstract. The transient theory of stimulated Brillouin scattering (SBS) in optical fibers is used to investigate the effects of weak feedback of incident broadband laser fields exhibiting different spectral shapes. The proper boundary conditions are given for the forward- and backward-propagating electric fields inside the fiber with a finite amount of reflectivity at the fiber ends. When the linewidth of the laser approaches the Brillouin frequency shift, a weak reflection of the laser field overlaps with the SBS gain and can be amplified in the fiber, resulting in an apparent reduction in SBS threshold. It is shown that the spectral shape of the incident field and its overlap with the SBS gain spectrum affects the SBS signal. Numerical examples are presented for incident laser fields that (1) simulate a pulsed, multilongitudinal-mode laser and (2) are a constant amplitude with a sinusoidal phase modulation. For the pulsed field with mode spacing much less than the Brillouin linewidth, calculations show that an end reflection of 0.01% (−40 dB) of the laser field with a linewidth equal to 0.75 of the Brillouin frequency shift can reduce the effective SBS threshold by a factor of 1.5. When the incident laser field is constant amplitude and phase modulated with a sinusoid, calculations predict sharp and distinct enhancement of the reflected power when the ratio of the Brillouin frequency shift to the modulation frequency is an integer for end reflections as low as 0.001% (−50 dB). Experiments performed with a sinusoidal phase modulated signal confirm the theoretical predictions. These results have implications on the design of high-power fiber laser systems that utilize spectral broadening to suppress SBS.
Nonlinearly transformed risk measures: properties and application to optimal reinsurance

ABSTRACT We propose the novel class of nonlinearly transformed risk measures (NTRMs) and apply them to the standard reinsurance problem in which an insurant seeks the risk-minimizing reinsurance contract. NTRMs transform both the outcomes and the probabilities of the insurant's uncertain final wealth by means of nonlinear functions. We prove relevant properties of NTRMs and show that they include popular Conditional Value-at-Risk (CVaR), Weighted Expected Shortfall (WES), Tail Nonlinearly Transformed Risk Measure (TNT), and Disutility Based Risk Measure (DBRM) as special cases. Regarding the reinsurance problem, we show that, under NTRMs, the optimal contract is of stop-loss type. We determine the optimal deductibles and provide comparative statics with respect to the insurant's risk aversion and initial wealth. Regarding comparative risk aversion, we show that the recently proposed WES, TNT, and DBRM help to overcome the restrictive all-or-nothing reinsurance decisions that prevail under CVaR. We further address the comparative statics with respect to initial wealth and show that under CVaR as well as WES and TNT, initial wealth is irrelevant: increasing initial wealth does not alter the optimal deductible. We show that NTRMs are able to overcome this shortcoming when the nonlinear transformation function of the outcomes is appropriately chosen.
Parenthetical Constructions in English and Arabic: A Contrastive Study

تعنى هذه الدراسة بموضوع التراكيب الاعتراضية و ما يميزها من سمات في اللغتين الإنكليزية والعربية، وتهدف إلى تسليط الضوء على وجوه التماثل ووجوه الاختلاف بين اللغتين في هذا الجانب النحوي. تطرق البحث إلى دراسة الأنماط المختلفة للتراكيب الاعتراضية في اللغتين ، فتناول بالتفصيل جملة الصلة غير الحصرية، والبدل غير الحصري والجملة التعليقية والمنادى و أدوات التعجب كونها أقوالا معترضة لا يؤثر حذفها في الجملة تركيبا أو معنى. اعتمدت الدراسة المنهج التحليلي الذي جاء به (راندولفكويرك) و زملاؤه في سلسلة كتب النحو التي صدرت خلال عقدي السبعينيات و الثمانينيات، و بخاصة كتاب قواعد اللغة الانكليزية المعاصرة (1972) و كتاب القواعد الشاملة للغة الانكليزية (1985)،كما اعتمدت الدراسة على ما ورد بخصوص القول المعترض في كتب النحو الأخرى لمؤلفين آخرين ، ليصبح المنهج التحليلي الذي اعتمده ا الباحثان اصطفائيا. يتناول البحث في قسمه الأخير النتائج التي توصل إليها الباحثان.
Tight parameterized preprocessing bounds: sparsification via low-degree polynomials

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
A Review of the Science and Logic Associated with Approach Used in the Universal Soil Loss Equation Family of Models

Soil erosion caused by rain is a major factor in degrading agricultural land, and agricultural practices that conserve soil should be used to maintain the long-term sustainability of agricultural land. The Universal Soil Loss Equation (USLE) was developed in the 1960s and 1970s to predict the long-term average annual soil loss from sheet and rill erosion on field-sized areas as an aid to making management decisions to conserve soil. The USLE uses six factors to take account of the effects of climate, soil, topography, crops, and crop management, and specific actions designed to conserve soil. Although initially developed as an empirical model based on data from more than 10,000 plot years of data collected in plot experiments in the USA, the selection of the independent factors used in the model was made taking account of scientific understanding of the drivers involved in rainfall erosion. In addition, assumptions and approximations were needed to make an operational model that met the needs of the decision makers at that time. Those needs have changed over time, leading to the development of the Revised USLE (RUSLE) and a second version of that, the Revised USLE, Version 2 (RUSLE2). While the original USLE model was not designed to predict short-term variations in erosion well, these developments have involved more use of conceptualization in order to deal with the time-variant impacts of the drivers involved in rainfall erosion. The USLE family of models is based on the concept that the “unit” plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope, provides a physical situation where the effect of climate and soil on rainfall erosion can be determined without the need to consider the impact of the four other factors. The science and logic associated with this approach is reviewed. The manner by which the soil erodibility factor is determined from plot data ensures that the long-term average annual soil loss for the unit plot is predicted well, even when the assumption that event soil loss is directly related to the product of event rainfall energy, and the maximum 30-min intensity is not wholly appropriate. RUSLE2 has a capacity to use CLIGEN, the weather generator used in WEPP, and so can predict soil losses based on individual storms in a similar way to WEPP. Including a direct consideration of runoff in determining event erosivity enhances the ability to predict event soil losses when runoff is known or predicted well, but similar to more process-based models, this ability is offset by the difficulty in predicting runoff well.
IGP Flexible Algorithm

IGP protocols traditionally compute best paths over the network based on the IGP metric assigned to the links. Many network deployments use RSVP-TE based or Segment Routing based Traffic Engineering to enforce traffic over a path that is computed using different metrics or constraints than the shortest IGP path. This document proposes a solution that allows IGPs themselves to compute constraint based paths over the network. This document also specifies a way of using Segment Routing (SR) Prefix-SIDs and SRv6 locators to steer packets along the constraint-based paths.
A Miniaturized Dual-Band Dual-Polarized Band-Notched Slot Antenna Array With High Isolation for Base Station Applications

In this article, a miniaturized dual-band dual-polarized slot antenna array is proposed for base station applications. It consists of two closely located $1 \times 4$ subarrays for the digital cellular system (DCS) and wideband code division multiple access (WCDMA) operations. The band-notched slot antenna (BNSA) is designed and adopted as the subarray element with radiation-suppression characteristic at notch frequencies. The notch frequency for one subarray is designed at the center operating frequency of the other subarray. As a result, the radiation from DCS subarray could be effectively suppressed within the WCDMA band and vice versa, resulting in high isolation between two subarrays with a small separation distance. By using the artificial magnetic conductor (AMC) reflector, the array height could be reduced by half, as compared with the perfect electric conductor (PEC) counterpart. In addition, a four-way unequal power divider is designed for the array’s beam-pattern synthesis. For the demonstration, the proposed array is fabricated and measured. The measurement results show that the dual-band array achieves the impedance bandwidths of 1.65–1.88 and 1.89–2.22 GHz, the isolations over 25 dB, the realized boresight gains of 12.2–12.8 and 12.3–13.2 dBi, and the sidelobe levels below −18 dB. The results indicate that the proposed array is a promising candidate for base-station applications.
Predictive Mapping of Soil Properties for Precision Agriculture Using Geographic Information System (GIS) Based Geostatistics Models

In precision Agriculture, geostatistical methods as a predictive tool have been extensively utilized. The approach estimates soil properties spatial variability and dependency. This study was carried out in Ovia north east Local Government Area of Edo State of Nigeria in order to map soil properties (Sand, Clay, pH, OC, P, N and CEC) and redict their spatial variability. Twenty-nine (29) soil samples were collected randomly from Typic Kandiudults soil type under three different land use, teak forest plantation, shrub, and arable farm. The soil samples were air-dried and passed through a 2 mm sieve before being analyzed for pH(CaCl2), SOC, Sand, Clay, Phosphorus, Nitrogen, and CEC. Generated data were statistically and geostatistically computed to explain the spatial variability of soil properties. The traditional method of soil analysis and interpretation are tedious, time-consuming with escalating budgets thus geostatical approach. Available phosphorus yielded large variability with CV=57.08% followed by clay content with CV=49.03%. Spherical, Gaussian, Hole Effect model, Stable, Exponential and Circular models were fitted for all the soil parameters. The result revealed that soil pH, Sand content, TN and CEC were moderate spatially autocorrelated with nugget/sill value of 0.32, 0.21, 0.49 and 0.30 respectively. SOC also gave a moderate spatially autocorrelated with nugget/sill value of 0.44. And Clay and Available phosphorus were strong spatially autocorrelated with nugget/sill value of 0.15 and 0.13 respectively. Cross-validation of the output maps using the semivariogram showed that the interpolation models are superior to assuming mean for any unsampled area. The output maps will help soil users within the area to proffer best management technology to improve crop, fiber and water production.
Image Dehazing Algorithm Based on Multi Mixed Prior Theory

In order to eliminate the prior blind area and improve the clarity of the fogging image, an image dehazing algorithm based on the multi mixed prior theory is proposed. First, the pixel area of the image is partitioned by dual constraints. Then the atmospheric transmittance of the dual constrained region is obtained by using the multi mixture priori theory. Finally, the guided filter algorithm is used to optimize the transmittance map. The experimental results show that compared with the traditional algorithm, the algorithm has a better visual effect and higher evaluation indexes. The algorithm proposed in this paper has a good restoration effect for fog image restoration.
Does Slight Skewness Matter?

Researchers feel they have the green light to ignore low levels of skewness because the central limit theorem indicates that such levels do not seriously compromise the validity of significance tests (e.g., Rouaud, 2013). And yet, significance testing is not the only issue, and the present focus is on two other issues where the central limit theorem does not come to the rescue. First, there is the question of how well the sample location statistic estimates the population location parameter. Surprisingly, skewness increases the precision of the estimation, and this increase in precision is impressive even with very low levels of skewness. Thus, by ignoring low levels of skewness, researchers are throwing away an important advantage. Second, experimental manipulations can cause differences in means across conditions, when there is no difference in locations across conditions; so the experiment seems to have worked based on means, when it really has not worked based on locations. Thus, moderate effect sizes can be caused by slight changes in skewness. For both reasons, it is recommended that researchers always attend to skewness, even when it is slight; and consider locations whenever they consider means.