Reviewerly/exHarmony huggingface dataset:
Stochastic Optimization for Spectral Risk Measures

Spectral risk objectives - also called $L$-risks - allow for learning systems to interpolate between optimizing average-case performance (as in empirical risk minimization) and worst-case performance on a task. We develop stochastic algorithms to optimize these quantities by characterizing their subdifferential and addressing challenges such as biasedness of subgradient estimates and non-smoothness of the objective. We show theoretically and experimentally that out-of-the-box approaches such as stochastic subgradient and dual averaging are hindered by bias and that our approach outperforms them.
A Numerical Approach to Sequential Multi-Hypothesis Testing for Bernoulli Model

In this paper we deal with the problem of sequential testing of multiple hypotheses. The main goal is minimizing the expected sample size (ESS) under restrictions on the error probabilities. We take, as a criterion of minimization, a weighted sum of the ESS's evaluated at some points of interest in the parameter space aiming at its minimization under restrictions on the error probabilities. We use a variant of the method of Lagrange multipliers which is based on the minimization of an auxiliary objective function (called Lagrangian) combining the objective function with the restrictions, taken with some constants called multipliers. Subsequently, the multipliers are used to make the solution comply with the restrictions. We develop a computer-oriented method of minimization of the Lagrangian function, that provides, depending on the specific choice of the parameter points, optimal tests in different concrete settings, like in Bayesian, Kiefer-Weiss and other settings. To exemplify the proposed methods for the particular case of sampling from a Bernoulli population we develop a set of computer algorithms for designing sequential tests that minimize the Lagrangian function and for the numerical evaluation of test characteristics like the error probabilities and the ESS, and other related. We implement the algorithms in the R programming language. The program code is available in a public GitHub repository. For the Bernoulli model, we made a series of computer evaluations related to the optimality of sequential multi-hypothesis tests, in a particular case of three hypotheses. A numerical comparison with the matrix sequential probability ratio test is carried out. A method of solution of the multi-hypothesis Kiefer-Weiss is proposed, and is applied for a particular case of three hypotheses in the Bernoulli model.
THE IMPACT OF THE DIGITAL PARADIGM OF SOCIAL DEVELOPMENT ON THE STRUCTURES OF HUMAN HEALTH

Objective of the study is to identify the method­ological and methodological possibilities of the philosophy of education in improving the edu­cational process in Russian society. The methodological basis of the research con­sists of the concepts of T. Parsons, E. Durkheim, which allow us to consider human health as a complex system including somatic, psychologi­cal and social structures; the theory of a healthy society by E. Fromm, which substantiates the dependence of human mental health on external social processes; the provisions of the theory of digital transformation of society, revealing the influence of modern technologies on the life­style of a person and society; ideas of the con­cept of “risk society”, allowing to analyze the threats to human health of the digital society. &nbsp; Research results. This article analyzes the trends of digitalization of society and their nega­tive consequences for human health. The digital&nbsp; environment carries risks to the physical, psy­chological and social health of a person. Threats to human physical health are caused by total computerization, which contributes to the de­velopment of a sedentary lifestyle, which leads to an increase in diseases of the musculoskeletal system, cardiovascular and endocrine systems, respiratory organs and vision. Risks to people’s psychological health are associated with the ac­tive use of digital devices, which has a negative impact on the cognitive, verbal and socioemotional development of the individual. Negative consequences for social health lie in the plane of the emergence of a new form of socialization of the individual - cybersocialization, during which the processes of desocialization of the in­dividual, atomization of society, replacement of the real Self with the virtual Self, the formation of quasi-values. &nbsp; Prospects of the study. The problem of the in­fluence of the digital paradigm of social devel­opment on the structures of human health is of scientific and practical interest due to the need to understand the risks and threats caused by the digitalization of society, as well as to minimize their negative consequences. &nbsp; &nbsp
The Acknowledgments in Student Theses: Eastern and Western Ideology

People's ideologies in various Western and Eastern countries influence language use, including acknowledgment writing. This qualitative study attempts to establish Eastern and Western ideologies in the acknowledgments of the student's final projects using a critical discourse approach. Eastern ideology was examined through 57 acknowledgments of student theses from eastern countries (Indonesia, Malaysia, Saudi Arabia, and Singapore) written in Indonesian and English. In comparison, Western ideology was examined through 53 acknowledgments of student theses from western countries (United States of America, England, and Germany) written in English. The linguistic features of Fairclough and Santoso were used to reveal the Eastern and Western ideology found in the acknowledgments of students’ final projects. The findings revealed that the language used to acknowledge eastern ideology was rigid. In Muslim-majority countries, gratitude to God comes first, and there are expressions of gratitude to parents. Gratitude expressions found in some eastern countries were based on the hierarchy of positions. The Western ideology in the acknowledgment, on the other hand, can be seen in the use of more expressive languages. Moreover, the acknowledgment format is not rigid and is arranged according to the hierarchy of works' contributions. The findings also indicate that identity has an impact on language.
Developing Instructional Material of Teaching Grammar Through Moodle to English Education Department Students of Universitas Muhammadiyah Makassar

This research aims at developing instructional material for English grammar through Moodle at UNISMUH Makassar. it tries to develop the material by identifying students’ need in learning grammar, to describe instructional material of English grammar through Moodle. This research and development (R&amp;D) is conducted by adapting ASSURE models which consist of Analyze, State Objective, Select, Utilize, Require, and Evaluation.The data were gained through questionnaire, the questionnaire is employed to do need analysis to get students’ need in instructional material, and to describe instructional material.meanwhile, questionnaire for validator to conduct development evaluation. Finally, the product of the study is Instructional material of teaching grammar through Moodle
Mosque Financial Management Information System Using Naive Bayes Algorithm

The mosque is a place for Muslims to carry out worship activities, social activities and other religious activities, one of the activities carried out by mosque administrators is financial management provided by the community.Financial management activities must be carried out carefully because financial information is very important for the community, regardless of the amount it must be accurately informed so as not to cause misunderstandings between managers and the community.The problem at this time is that there is no software for mosque financial managers and donors who are not on time in giving donations to mosques, so that it becomes a consideration for the eligibility of donors.With these problems, the researchers designed a financial management information system that can facilitate DKM administrators in managing finances and neat documentation.The Naive Bayes algorithm aims to classify data into certain classes, then the pattern can be used to estimate the eligibility of donors, so that the Mosque Prosperity Council (DKM) knows whether or not regular donors are eligible.
PENGARUH EFIKASI DIRI (SELF EFFICACY) DAN PEMANFAATAN FASILITAS BELAJAR TERHADAP HASIL BELAJAR AKUNTANSI SISWA KELAS XI MAS NURUSSA’ADAH TEBO

The purpose of this study was to determine how much influence Self Efficacy and Utilization of Learning Facilities had on the Accounting Learning Outcomes of Class XI Students Mas Nurussa'adah Tebo. This research is included in quantitative research. The method used in this research is the ex-post facto method. Data collection techniques in the form of questionnaires were distributed to the entire population, the population in this study was relatively small, as many as 59 students, the technique used was saturated sampling technique. Saturated sampling technique is a sampling technique in which each member of the population is sampled. This research uses multiple linear regression model. All data was processed using Microsoft Excel 2010 Software and Statistical Product And Service Solution (SPSS) release 22.0 for windows. The results of this study indicate that the self-efficacy variable has a partial positive effect on learning outcomes, this is evidenced by the obtained tcount &gt; ttable or 2,665 &gt; 2.0025. The variable utilization of learning facilities has a partial positive effect on learning outcomes, this is evidenced by the value of tcount greater than ttable or 8.719 &gt; 2.0025. Self-efficacy and the use of learning facilities simultaneously have a positive effect on learning outcomes, this is evidenced by the value of Fcount greater than Ftable or 42.868 &gt; 3.2 with the value of multiple linear regression equation Y = 33,442 + 0.187 X1 + 0.718 X2 + e. Based on the results of the research above, it can be concluded that self-efficacy and the use of learning facilities have a positive and significant effect on student learning outcomes.Keywords: Entrepreneurial Attitude, Social Support, Entrepreneurial Intention
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning.
Classification of semantic segmentation using fully convolutional networks based unmanned aerial vehicle application

The classification of semantic segmentation-based unmanned aerial vehicle (UAV) application based on the datasets used in this work and the necessary data preprocessing steps for the optimization and implementation of the models are also involved. The optimization of the various models was done using the evaluation metrics and loss functions because deep neural networks (DNNs) are just about writing a cost function and its subsequent optimization. convolutional neural network (CNN) is a common type of artificial neural network (ANN) that has found application in numerous tasks, such as image and video recognition, image classification, recommender systems, financial time series, medical image analysis, and natural language processing. CNN is developed to automatically and adaptively learn spatial feature hierarchies via backpropagation using numerous building blocks, such as pooling, convolution, and fully connected layers. The result of identification was excellent. The image segmentation was detected and comprehend the actual components of an image down to the pixel level. The result created an entire image segmentation masks with instances using the new label editor in the label box.
Linguistics Pattern on Acehnese Reduplicative System: Classifications and Meanings

The significance of reduplication in language studies was to construct a linguistics pattern. However, linguists tend to disregard the functions of individual creativity and the core language meaning-making approach at the discourse level. Therefore, we should investigate this intriguing topic more to describe the repetition of words. Many studies analyzed classification reduplication in Acehnese through documentation techniques, in contrast, this current study investigated classifications and meanings of reduplication in Acehnese through observation and interview. Sulaiman's paradigm as the framework stated that reduplication had three parts: complete, affix, and ablaut reduplication. In addition, the researchers used the Ali et al. framework, which suggested eight meanings of reduplication, mainly grammatical meanings. The findings revealed that full reduplication was the most common among the 100 data of utterances. Furthermore, the meanings of reduplications are to convey the collective and distributive meaning, to express resemblance, and to express simultaneously and continuously were not discovered. The current study looked at how people could use morphological perspectives on reduplication in community discussions. Acehnese reduplication may vanish.
Iban plaited mat motif classification with adaptive smoothing

&lt;span lang="EN-US"&gt;Decorative mats plaited by the Iban communities in Borneo contains motifs that reflect their traditional beliefs. Each motif has its own special meaning and taboos. A typical mat motif contains multiple smaller patterns that surround the main motif hence is likely to cause misclassification. We introduce a classification framework with adaptive sampling to remove smaller features whilst retaining larger (and discriminative) image structures. Canny filter and probabilistic hough transform are gradually applied to a clean greyscale image until a threshold value pertaining to the image’s structural information is reached. Morphological dilation is then applied to improve the appearance of the retained edges. The resulting image is described using binary robust invariant scalable keypoints (BRISK) features with random sample consensus (RANSAC). We reported the classification accuracy against six common image deformations at incremental degrees: scale+rotation, viewpoint, image blur, joint photographic experts group (JPEG) compression, scale and illumination. From our sensitivity analysis, we found the optimal threshold for adaptive smoothing to be 75.0%. The optimal scheme obtained 100.0% accuracy for JPEG compression, illumination, and viewpoint set. Using adaptive smoothing, we achieved an average increase in accuracy of 11.0% compared to the baseline.&lt;/span&gt;
Using Multiple Instance Learning to Build Multimodal Representations

Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e.g., image classification, visual grounding, and cross-modal retrieval. In this work, we establish a connection between multimodal representation learning and multiple instance learning. Based on this connection, we propose a generic framework for constructing permutation-invariant score functions with many existing multimodal representation learning approaches as special cases. Furthermore, we use the framework to derive a novel contrastive learning approach and demonstrate that our method achieves state-of-the-art results in several downstream tasks.
Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection

LiDAR and camera fusion techniques are promising for achieving 3D object detection in autonomous driving. Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds to enhance detection accuracy. Nevertheless, the restricted resolution of 2D feature maps impedes accurate re-projection and often induces a pronounced boundary-blurring effect, which is primarily attributed to erroneous semantic segmentation. To well handle this limitation, we propose a general multi-modal fusion framework Multi-Sem Fusion (MSF) to fuse the semantic information from both the 2D image and 3D points scene parsing results. Specifically, we employ 2D/3D semantic segmentation methods to generate the parsing results for 2D images and 3D point clouds. The 2D semantic information is further reprojected into the 3D point clouds with calibration parameters. To handle the misalignment between the 2D and 3D parsing results, we propose an Adaptive Attention-based Fusion (AAF) module to fuse them by learning an adaptive fusion score. Then the point cloud with the fused semantic label is sent to the following 3D object detectors. Furthermore, we propose a Deep Feature Fusion (DFF) module to aggregate deep features at different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3D object detection benchmarks by comparing them with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most importantly, the proposed approach significantly outperforms other approaches and sets state-of-the-art results on the nuScenes testing benchmark.
Graph Analysis in Decentralized Online Social Networks with Fine-Grained Privacy Protection

Graph analysts cannot directly obtain the global structure in decentralized social networks, and analyzing such a network requires collecting local views of the social graph from individual users. Since the edges between users may reveal sensitive social interactions in the local view, applying differential privacy in the data collection process is often desirable, which provides strong and rigorous privacy guarantees. In practical decentralized social graphs, different edges have different privacy requirements due to the distinct sensitivity levels. However, the existing differentially private analysis of social graphs provide the same protection for all edges. To address this issue, this work proposes a fine-grained privacy notion as well as novel algorithms for private graph analysis. We first design a fine-grained relationship differential privacy (FGR-DP) notion for social graph analysis, which enforces different protections for the edges with distinct privacy requirements. Then, we design algorithms for triangle counting and k-stars counting, respectively, which can accurately estimate subgraph counts given fine-grained protection for social edges. We also analyze upper bounds on the estimation error, including k-stars and triangle counts, and show their superior performance compared with the state-of-the-arts. Finally, we perform extensive experiments on two real social graph datasets and demonstrate that the proposed mechanisms satisfying FGR-DP have better utility than the state-of-the-art mechanisms due to the finer-grained protection.
Multi-objective load balancing in cloud infrastructure through fuzzy based decision making and genetic algorithm based optimization

Cloud computing became a popular technology which influence not only product development but also made technology business easy. The services like infrastructure, platform and software can reduce the complexity of technology requirement for any ecosystem. As the users of cloud-based services increases the complexity of back-end technologies also increased. The heterogeneous requirement of users in terms for various configurations creates different unbalancing issues related to load. Hence effective load balancing in a cloud system with reference to time and space become crucial as it adversely affect system performance. Since the user requirement and expected performance is multi-objective use of decision-making tools like fuzzy logic will yield good results as it uses human procedure knowledge in decision making. The overall system performance can be further improved by dynamic resource scheduling using optimization technique like genetic algorithm.
Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense

Federated recommendation (FedRec) can train personalized recommenders without collecting user data, but the decentralized nature makes it susceptible to poisoning attacks. Most previous studies focus on the targeted attack to promote certain items, while the untargeted attack that aims to degrade the overall performance of the FedRec system remains less explored. In fact, untargeted attacks can disrupt the user experience and bring severe financial loss to the service provider. However, existing untargeted attack methods are either inapplicable or ineffective against FedRec systems. In this paper, we delve into the untargeted attack and its defense for FedRec systems. (i) We propose ClusterAttack, a novel untargeted attack method. It uploads poisonous gradients that converge the item embeddings into several dense clusters, which make the recommender generate similar scores for these items in the same cluster and perturb the ranking order. (ii) We propose a uniformity-based defense mechanism (UNION) to protect FedRec systems from such attacks. We design a contrastive learning task that regularizes the item embeddings toward a uniform distribution. Then the server filters out these malicious gradients by estimating the uniformity of updated item embeddings. Experiments on two public datasets show that ClusterAttack can effectively degrade the performance of FedRec systems while circumventing many defense methods, and UNION can improve the resistance of the system against various untargeted attacks, including our ClusterAttack.
COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec

We introduce COOL-CHIC, a Coordinate-based Low Complexity Hierarchical Image Codec. It is a learned alternative to autoencoders with 629 parameters and 680 multiplications per decoded pixel. COOL-CHIC offers compression performance close to modern conventional MPEG codecs such as HEVC and is competitive with popular autoencoder-based systems. This method is inspired by Coordinate-based Neural Representations, where an image is represented as a learned function which maps pixel coordinates to RGB values. The parameters of the mapping function are then sent using entropy coding. At the receiver side, the compressed image is obtained by evaluating the mapping function for all pixel coordinates. COOL-CHIC implementation is made open-source.
Cryptanalysis and designing chaos-based irreversible and parallel key expansion module over Galois field

From the security criteria of irreversibility, parallelizability and independence, we cryptanalyzed the key expansion modules of candidate block ciphers of AES, the results revealed that there exist some weaknesses inside, which may be explored by the attacker. Hence, we designed a more secure key expansion module that the round-key can satisfy three criteria above. First, we constructed a non-degenerate 2D chaotic map (2D-{\pi}eCM) with ergodicity in phase space and sufficient large chaotic range. Then based on 2D-{\pi}eCM and polynomial multiplication over Galois field, we designed an irreversible key expansion module, which could transform the initial key of arbitrary length to desired number of independent round keys in parallel. Security and statistical results demonstrated the flexible and effectiveness of the proposed key expansion module.