Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models

Due to enormous computing and storage overhead for well-trained Deep Neural Network (DNN) models, protecting the intellectual property of model owners is a pressing need. As the commercialization of deep models is becoming increasingly popular, the pre-trained models delivered to users may suffer from being illegally copied, redistributed, or abused. In this paper, we propose DeepDIST, the first end-to-end secure DNNs distribution framework in a black-box scenario. Specifically, our framework adopts a dual-level fingerprint (FP) mechanism to provide reliable ownership verification, and proposes two equivalent transformations that can resist collusion attacks, plus a newly designed similarity loss term to improve the security of the transformations. Unlike the existing passive defense schemes that detect colluding participants, we introduce an active defense strategy, namely damaging the performance of the model after the malicious collusion. The extensive experimental results show that DeepDIST can maintain the accuracy of the host DNN after embedding fingerprint conducted for true traitor tracing, and is robust against several popular model modifications. Furthermore, the anti-collusion effect is evaluated on two typical classification tasks (10-class and 100-class), and the proposed DeepDIST can drop the prediction accuracy of the collusion model to 10% and 1% (random guess), respectively.
Save the Tiny, Save the All: Hierarchical Activation Network for Tiny Object Detection

Tiny object detection (TOD) remains a challenging problem due to the extremely small size and weak feature presentations of tiny objects. Many effective methods have improved the detection of small objects below <inline-formula> <tex-math notation="LaTeX">$32\times 32$ </tex-math></inline-formula> pixels to some extent, but the performance is still poor for the tiny objects below <inline-formula> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula> pixels. In this paper, we find that the aliasing between the features and object scales, namely feature-scale-aliasing, leads to the misalignment between feature subspaces and detection subspaces, and thus results in the interference of features, especially for tiny objects. To alleviate this, we propose a Hierarchical Activation (HA) method to obtain scale-specific feature subspaces by activating object features at different scales hierarchically. To this end, we design a Scale-Guided Feature Activation (SGFA) to decompose the original object-aliasing feature spaces into a group of scale-specific feature subspaces by scale-guided activation maps. Then, Scale-Specific Feature re-Coupling (SSFC) is used to enhance the feature subspaces by adaptively aggregating the feature subspaces from different groups. In addition, we propose to complement the scale-specific detailed information by a designed Detailed Information Compensation (DIC) method. Implementing HA, a multi-scale keypoint-based detector is constructed to improve the tiny object detection, referred to as Hierarchical Activation Network (HANet). Extensive experiments are carried out on three tiny object detection datasets, e.g., TinyPerson, AI-TOD, and TinyCOCO. Our HANet achieves 58.45% <inline-formula> <tex-math notation="LaTeX">$AP_{50}^{all}$ </tex-math></inline-formula>, 22.1% <inline-formula> <tex-math notation="LaTeX">$AP$ </tex-math></inline-formula>, and 15.76% <inline-formula> <tex-math notation="LaTeX">$AP$ </tex-math></inline-formula> on TinyPerson, AI-TOD, and TinyCOCO, respectively, showing a significant performance gain over the competitors.
Segmentation of Inhomogeneous Noisy Images via a Bayesian Model Coupled with Anisotropic Mesh Adaptation

Automatic image segmentation is a key process in many applications of science and engineering, from medical imaging to autonomous vehicle driving and smart agriculture monitoring. In these contexts, the presence of spatial inhomogeneities and noise challenges the robustness of segmentation strategies [1]. In this talk, a finite element-based segmentation algorithm handling images with different spatial patterns is presented. The methodology relies on a split Bregman algorithm for the minimisation of a region-based Bayesian energy functional and on an anisotropic recovery-based error estimate to drive mesh adaptation [2]. On the one hand, a Bayesian model is considered to exploit the intrinsic spatial information in inhomogeneous images [3]. To address the ill-posedness of the associated optimisation problem, a convexification technique [4] is coupled with a split Bregman algorithm for the minimisation of the regularised functional [5]. On the other hand, an anisotropic mesh adaptation procedure guarantees a smooth description of the interface between background and foreground of the image, without jagged details [2,6]. The proper alignment, sizing and shaping of the anisotropically adapted mesh elements guarantee that the increased precision is achieved with a reduced number of degrees of freedom [2,6]. Numerical experiments will be presented to showcase the performance of the resulting split-adapt Bregman algorithm on synthetic and real images featuring inhomogeneous spatial patterns. The method outperforms the standard split Bregman approach, providing accurate and robust results even in the presence of Gaussian,
Comparative Study of Flood and Long-term Mean Monthly Flow Estimation Approaches: Case Studies of Six Basins in Nepal

Most of Nepal's river basins have poor hydro- meteorological databases, with several river basins being ungauged. Thus, hydrological parameters need to be estimated using different types of computation methods. The primary goal of this study is to identify the most accurate method for calculating peak flood and long-term mean monthly flow among the most commonly used methods in Nepal. We compared the peak flood calculated using various flood computation formulas, such as Hydest, Modified Hydest, MHSP (Medium Hydropower Study Project) 1997, Modified Dickens, PCJ (Prem Chandra Jha) 1996, Rational, and Specific Discharge, to the flood calculated using gauged discharge data frequency analysis. We find that it is wise to use the Modified Hydest method in the khokana basin for all Return Periods (RPs) and in the Belkot basin (for RP ≤ 100 years), the Specific Discharge method in the Jamu basin, the MHSP 1997 method in the Belkot basin (for RP ≤ 100 years) and the Bagasoti Gaun basin (for RP ≤ 20 years). The PCJ 1996 method having the lowest cumulative value of Root Mean Square Error (RMSE) for the six studied catchments is suitable for Rabhuwa Bazar (for RP > 50 years) and Bagasoti Gaun (for RP >20 years). Similarly, the Modified Dickens method is suitable in the Bagasoti Gaun basin for RP ≤ 50 years. This paper also shows the performance of the Hydest and MHSP 1997 mean flow estimation methods and suggests different coefficients or constants to be used with the MHSP 1997, Modified MIP (Medium Irrigation Project), and Hydest methods to obtain more reliable long-term mean monthly flows. Overall, our study will help the designer choose a reliable method for design flow estimation. This study also shows that the flow obtained from even the most suitable methods needs to be adjusted. As a result, intensive research is required to adjust previous methods and develop the new one.
Discrete Trial Teaching Method to Improve Receptive Language Skills in Children with Global Developmental Delay

The purpose of this study was to determine the effectiveness of the Discrete Trial Teaching method in improving the receptive language skills of children with Global Developmental Delay. The receptive language skills provided in this intervention are recognizing body parts in the facial area. This research is an-experimental research with a single subject research, which uses the A-B-A model. Baseline measurements were carried out twice, namely before and after the implementation of the intervention. This study used 20 sessions consisting of 5 sessions for the first baseline, 10 intervention sessions and 5 sessions for the second baseline. The participant of this study was a boy aged 3 years and 6 months who was diagnosed with Global Developmental Delay. Data analysis used in this study is visual analysis which includes 6 components. The results showed that there was an increase in scores and with an upward trend and good stability, so it can be concluded that the Discrete Trial Teaching method can effectively improve the receptive language skills of children with Global Developmental Delay.
Effect of delayed timing of artificial insemination with sex-sorted semen on pregnancy per artificial insemination in synchronized dairy heifers managed in a seasonal-calving pasture-based system

Graphical Abstract Summary: This study evaluated the timing of artificial insemination (AI) with frozen-thawed sex-sorted semen on pregnancy per AI in dairy heifers. A 6-day progesterone Co-Synch protocol was used for ovulation synchronization of 823 dairy heifers, with timed AI (TAI) coincident with (TAI-0) or 8 hours (TAI-8) after the second injection of gonadotropin-releasing hormone (GnRH), corresponding to either 48 or 56 hours after removal of the progesterone-releasing intravaginal device. We hypothesized that TAI-8 heifers would have greater pregnancy rates than TAI-0 heifers by delivering viable sex-sorted semen to the uterus closer to the time of ovulation. The study demonstrated that delaying the normal timing of AI of dairy heifers with frozenthawed sex-sorted semen by 8 hours, thereby delivering sperm cells closer to the presumed time of ovulation, improved pregnancy per AI.
Locating a single new facility among existing unequally attractive facilities

The location of a new facility in a competitive environment is investigated. It is assumed that customers patronize the facility with the highest utility value rather than the closest facility. Simulations show that the new approach yields a superior location in terms of market size captured. Sensitivity analyses of the problem parameters illustrate the sensitivity of the location and the sensitivity of the market size captured to the facility's quality.
An unconditionally stable mixed discontinuous Galerkin method

In discontinuous Galerkin method continuity can be forced in various ways. One quite popular way to force continuity in discontinuous approach is the Nitsche method. The method is based on handling of non-homogenous Dirichlet boundary conditions by J.A. Nitsche (1970). However, for non-mixed problems the method is elliptic (or stable) only for large enough values of stability parameter. Lower limit of the stability parameter depends on the degrees of freedom, e.g. on polynomial order of the elements. Even though the lower limit can be computed analytically, it may cause problems in for example prefinement (i.e. refinement by increasing the polynomial order of the elements). Therefore it is interesting to notice that we can give up the stability parameter if we pose the problem in a mixed form.
Pancasila Sebagai Ideologi Yang Khas Dan Identitas Bangsa Indonesia

Sebagai pandangan hidup bangsa, Pancasila sebenarnya merupakan perwujudan dari nilai-nilai budaya milik bangsa sendiri yang diyakini kebenarannya. Pancasila digali dari budaya bangsa yang sudah ada, tumbuh, dan berkembang berabad-abad lamanya. Perwujudan dari Pancasila sebagai ideologi negara mempunyai arti menjadi cita-cita dalam penyelenggaraan negara yang terwujud melalui Ketetapan Majelis Permusyawaratan Rakyat Nomor 7 Tahun 2001 mengenai visi Indonesia masa depan. Dengan menjadi visi atau arah dari penyelenggaraan kehidupan berbangsa dan bernegara di Indonesia, yaitu terwujudnya kehidupan yang menjunjung tinggi ketuhanan, nilai kemanusiaan, kesadaran akan kesatuan, berkerakyatan, serta menjunjung tinggi nilai keadilan. Penelitian ini menggunakan studi kepustakaan yaitu, dalam proses pengambilan datanya tidak perlu terjun ke lapangan secara langsung. Namun mengambilnya dari berbagai sumber referensi yang bisa mendukung penelitian ini. Bila suatu bangsa tanpa ideologi, bangsa tersebut akan rapuh dan hilang jati dirinya. Pancasila sebagai sumber nilai menunjukkan identitas bangsa Indonesia yang memiliki nilai-nilai kemanusiaan yang luhur. Diterimanya Pancasila oleh berbagai golongan dan aliran pemikiran, membuat mereka bersedia untuk bersatu dalam negara kebangsaan Indonesia. Pancasila merupakan bentuk kesepakatan bersama masyarakat Indonesia yang plural. Ideologi Pancasila menjadi ideologi yang khas, berbeda dengan ideologi lain.
Spotted Hyena Optimization with Deep Learning-Based Automatic Text Document Summarization Model

- Automatic text summarization is an active investigation region determined as removing snippets or introductory sentences of a massive document and relating them as a short form of documents. Text Summarization can be either cost-efficient or time-efficient. An abstractive or extractive summary was studied with distinct algorithms comprising deep learning (DL), graph, and statistical-based techniques. DL has attained promising shows in comparison to the typical methods. With the development of various neural structures like the attention mechanism (usually called a transformer), there is a potential growth area for summarization tasks. Hence, this research presents a Spotted Hyena Optimization with Deep Learning based Automatic Text Summarization (SHODL-ATS) model. The SHODL-ATS technique's principal objective lies in the documents' automated summarization. To accomplish this, the presented SHODL-ATS technique performs data preprocessing to convert the data into a convenient form. The SHODL-ATS technique uses an Attention-based Bidirectional Gated Recurrent Unit (ABiGRU) model for summarizing the text documents. Finally, the SHO technique is enforced for the parameter tuning of the ABiGRU approach. To examine the achievement of the SHODL-ATS model, we validate the outcomes on benchmark datasets. The results indicate the promising achievement of the SHODL-ATS method over other existing techniques.
Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classiers

We outline a design for a FACS-based facial expression recognition system and describe in more detail the implementation of two of its main components. Firstly we look at how features that are useful from a pattern analysis point of view can be extracted from a raw input image. We show that good results can be obtained by using the method of local binary patterns (LPB) to generate a large number of candidate features and then selecting from them using fast correlation-based ltering (FCBF). Secondly we show how Platt scaling can be used to improve the performance of an error-correcting output code (ECOC) classier.
Derivative-Extended Time Domain Reduction for Coupled Systems Using Chebyshev Expansion

The time domain model reduction based on general orthogonal polynomials has been presented for linear systems. In this paper, we extend this approach by taking the derivative information of the system into account in the context of model reduction of coupled systems. We expand the derivative terms over the Chebyshev polynomial basis and show that Chebyshev coefficients of the expansion possess a specific structure, making it possible to preserve much more time domain information by employing projection methods. Besides, with the well-defined projection matrices, the resulting reduced model shares the same topological structure with the original coupled system. Two numerical examples are simulated to showcase the accuracy of incorporating the derivative information into model reduction.
A Formal Control Framework of Autonomous Vehicle for Signal Temporal Logic Tasks and Obstacle Avoidance

This article investigates the control problem of making an autonomous vehicle modeled as a nonlinear affine system, achieve both temporal logic tasks and obstacle avoidance. A new formal control framework based on the control barrier function (CBF) and potential field method is proposed, which aims at eliminating the singular points caused by the conflict between the target and the obstacle in traditional CBF methods. A feasibility detection mechanism algorithm is proposed to detect the task in terms of workspace topology and determines the structure of the framework. On the one hand, obstacle avoidance can always be fulfilled such that the safety of the system is guaranteed. On the other hand, satisfaction for the target formulas is discussed in terms of obstacles, workspace topology, and formula preference. The proposed control framework generates controllers based on different feasibility types of tasks automatically, which shows global performance in both safety guarantee and task achievement. Finally, the effectiveness of the overall framework is illustrated by two examples.
Towards Gaussian Process Models of Complex Rotorcraft Dynamics

Physical law based models (also known as white box models) are widely applied in the aerospace industry, providing models for dynamic systems such as helicopter flight simulators. To meet the criteria of real-time simulation, simplifications to the underlying physics sometimes have to be applied, leading to errors in the model’s predictions. Grey-box models use both physics-based and data-based models. They have potential to reduce the difference between a simulator’s and real rotorcraft’s response. In the current work, a preliminary step to the grey-box approach, a machine learnt data-based, i.e ‘black box’ model is applied to the dynamic response of a helicopter. The machine learning methods used are probabilistic and can capture uncertainties associated with the model’s prediction. In the current paper, machine learning is used to create a Gaussian Process (GP) non-linear autoregressive (NARX) model that predicts pitch, roll and yaw rate. The predictions are compared to a physical law based model created using FLIGHTLAB software. The GP outperforms the FLIGHTLAB model in terms of root mean squared error, when predicting the pitch, roll and yaw rate of a Bo105 helicopter.
Literacy Studies: Implementation of Problem-based Learning Models to Improve Critical Thinking Skills in Elementary School Students

Students often have difficulty in developing critical thinking skills and creative thinking in problem-solving and applying concepts learned in school. The difficulty is due to weak understanding of concepts, especially for students who are in the low category; therefore, it has an impact on the low-student learning outcomes. One of the reasons for the low-learning outcomes is the difficulty in understanding a concept given by the teacher so students tend to be passive in learning activities, which has an impact on the lack of students to think both critically and creatively in applying the concepts of the material provided by the teacher. The purpose of this study is to conceptually analyze the use of problem-based learning models to improve critical thinking skills in elementary school students. The research method used in this study is a systematic literature study. The author selected 25 articles from journals and articles published during the last 10 years from 2012 to 2022. The results of this review can improve student learning outcomes in elementary schools because this model has characteristics suitable to be applied in real-life situations; in avoiding simple answers, and allowing for multiple solutions to the situation. Keywords: Problem-based learning, critical thinking skills
Contributions to statistical learning in sparse models

The aim of this habilitation thesis is to give an overview of my works on high-dimensional statistics and statistical learning, under various sparsity assumptions. In a first part, I will describe the major challenges of high-dimensional statistics in the context of the generic linear regression model. After a brief review of existing results, I will present the theoretical study of aggregated estimators that was done in (Alquier & Lounici 2011). The second part essentially aims at providing extensions of the various theories presented in the first part to the estimation of time series models (Alquier & Doukhan 2011, Alquier & Wintenberger 2013, Alquier & Li 2012, Alquier, Wintenberger & Li 2012). Finally, the third part presents various extensions to nonparametric models, or to specific applications such as quantum statistics (Alquier & Biau 2013, Guedj & Alquier 2013, Alquier, Meziani & Peyre 2013, Alquier, Butucea, Hebiri, Meziani & Morimae 2013, Alquier 2013, Alquier 2008). In each section, we provide explicitely the estimators used and, as much as possible, optimal oracle inequalities satisfied by these estimators.
Storage assignment and order batching problem in Kiva mobile fulfilment system

ABSTRACT This article studies the storage assignment and order batching problem in the Kiva mobile fulfilment system. The storage assignment model aims to decide which product to put in which pod to maximize the product similarity and the order batching model aims to minimize the number of visits of pods. To solve the order batching problem, a heuristic is proposed, where a batch schedule is initialized with the objective of maximizing the order association or minimizing order alienation and improved by variable neighbourhood search. Computational experiments are conducted to verify the performance of the proposed model and algorithm.
A Comparison of Confidence Interval Methods of Fixed Effect in Nested Error Regression Model

Linear mixed-effects models are very popular and powerful tools in many scientific fields such as zoology, biology, and education. Estimators of fixed effects do not only depend on the variances of error terms but they also depend on random terms in mixed-effect models. When the distributions of random effects are unknown or enough sample size cannot be obtained, standard methods may fail. This study aims to determine a promising confidence interval method among existing methods in terms of coverage probability of true value of parameter. Standard and parametric bootstrap-based confidence interval methods for nested error regression model were compared in the simulation study under small samples. It is observed that parametric bootstrap-based method provides better coverage rates for small intra-correlation and profile likelihood method usually provides better results for moderate and strong correlation.
Asynchronous Motor Imagery BCI and LiDAR-Based Shared Control System for Intuitive Wheelchair Navigation

Mapping drivers’ thoughts directly to mobility system control would make driving more intuitive as if the mobility system is an extension of their own body. Such a system would allow patients with motor disabilities to drive, as it would not require any physical movement. In this article, we therefore propose a brain-controlled mobility system that analyzes real-time neural signals elicited from motor imagery, an imagination of different body movements. As such asynchronous brain–computer interfaces (BCIs) are prone to error, our system contains shared control capabilities that take into consideration continuously updated information of the surrounding environment along with electroencephalogram (EEG) signals to improve navigating performance without precise and accurate control from the driver. With our shared control method that uses a wheelchair with light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensors, we held a comparative study in which participants drove our wheelchair with and without our shared control approach using either our brain-controlled system or a keyboard in a physical environment. The experimental results show that among the five participants, the three participants that failed the driving task with the asynchronous BCI-based system could also successfully complete it using our shared control approach. Furthermore, our approach narrows the gap between driving with neural signals and driving with a widely used interface in terms of both elapsed time and safety. These results show not only the potential of brain signals for driving but also the applicability of BCIs to real-life situations.