Reviewerly/exHarmony huggingface dataset:
An adversarial learning algorithm for mitigating gender bias in face recognition.

State-of-the-art face recognition networks implicitly encode gender information while being trained for identity classification. Gender is often viewed as an important face attribute to recognize humans. But, the expression of gender information in deep facial features appears to contribute to gender bias in face recognition, i.e. we find a significant difference in the recognition accuracy of DCNNs on male and female faces. We hypothesize that reducing implicitly encoded gender information will help reduce this gender bias. Therefore, we present a novel approach called `Adversarial Gender De-biasing (AGD)' to reduce the strength of gender information in face recognition features. We accomplish this by introducing a bias reducing classification loss $L_{br}$. We show that AGD significantly reduces bias, while achieving reasonable recognition performance. The results of our approach are presented on two state-of-the-art networks.
Weakly-Supervised Multi-view Multi-instance Multi-label Learning

Multi-view, Multi-instance, and Multi-label Learning (M3L) can model complex objects (bags), which are represented with different feature views, made of diverse instances, and annotated with discrete non-exclusive labels. Existing M3L approaches assume a complete correspondence between bags and views, and also assume a complete annotation for training. However, in practice, neither the correspondence between bags, nor the bags' annotations are complete. To tackle such a weakly-supervised M3L task, a solution called WSM3L is introduced. WSM3L adapts multimodal dictionary learning to learn a shared dictionary (representational space) across views and individual encoding vectors of bags for each view. The label similarity and feature similarity of encoded bags are jointly used to match bags across views. In addition, it replenishes the annotations of a bag based on the annotations of its neighborhood bags, and introduces a dispatch and aggregation term to dispatch bag-level annotations to instances and to reversely aggregate instance-level annotations to bags. WSM3L unifies these objectives and processes in a joint objective function to predict the instance-level and bag-level annotations in a coordinated fashion, and it further introduces an alternative solution for the objective function optimization. Extensive experimental results show the effectiveness of WSM3L on benchmark datasets.
Prediction of rainfall using improved deep learning with particle swarm optimization

Rainfall is a natural factor that is very important for farmers or certain institutions to predict the planting period of a plant. The problem is that rainfall is very difficult to predict. Trials to get optimal rainfall prediction have been carried out by BMKG through research with variety of methods in various fields, including meteorology, climatology and geophysics. The results of the study unfortunately obtained a less optimal success rate in predicting rainfall. Today, there are many new methods for predicting events. These methods include Deep Learning (DL) and Particle Swarm Optimization (PSO). The use of the Deep Learning method is very susceptible to initial weights that are less than optimal, so it requires a process of optimization using a metaheuristic technique, which is the PSO algorithm, because this algorithm has a level of complexity that is much lower than genetic algorithms. In this study, this method is utilized to predict rainfall by determining the exact regression equation model according to the number of layers in hidden nodes based on the size of the kernel and the weight between the layers. This research is approved achieved get more optimal rainfall prediction results that those of previous research that without optimization with PSO.
Mutual Information Estimation using LSH Sampling

Learning representations in an unsupervised or self-supervised manner is a growing area of research. Current approaches in representation learning seek to maximize the mutual information between the learned representation and original data. One of the most popular ways to estimate mutual information (MI) is based on Noise Contrastive Estimation (NCE). This MI estimate exhibits low variance, but it is upper-bounded by log(N), where N is the number of samples. In an ideal scenario, we would use the entire dataset to get the most accurate estimate. However, using such a large number of samples is computationally prohibitive. Our proposed solution is to decouple the upper-bound for the MI estimate from the sample size. Instead, we estimate the partition function of the NCE loss function for the entire dataset using importance sampling (IS). In this paper, we use locality-sensitive hashing (LSH) as an adaptive sampler and propose an unbiased estimator that accurately approximates the partition function in sub-linear (near-constant) time. The samples are correlated and non-normalized, but the derived estimator is unbiased without any assumptions. We show that our LSH sampling estimate provides a superior bias-variance trade-off when compared to other state-of-the-art approaches.
Latent Space Factorisation and Manipulation via Matrix Subspace Projection

We tackle the problem disentangling the latent space of an autoencoder in order to separate labelled attribute information from other characteristic information. This then allows us to change selected attributes while preserving other information. Our method, matrix subspace projection, is much simpler than previous approaches to latent space factorisation, for example not requiring multiple discriminators or a careful weighting among their loss functions. Furthermore our new model can be applied to autoencoders as a plugin, and works across diverse domains such as images or text. We demonstrate the utility of our method for attribute manipulation in autoencoders trained across varied domains, using both human evaluation and automated methods. The quality of generation of our new model (e.g. reconstruction, conditional generation) is highly competitive to a number of strong baselines.
Analysis of Data Mining Using K-Means Clustering Algorithm for Product Grouping

Rizki Barokah Store is one of the stores that every day sell a variety of basic materials of daily necessities such as food, drinks, snacks, toiletries, and so on. However, some problems occur in the Rizki Barokah Store is often a build-up of product stocks that resulted in the product has expired. This is due to an error in making decisions on the product stock. In addition to these problems, with the amount of sales data stored on the database, the store has not done data mining and grouping to know the potential of the product. Whereas data-processing technology can already be done using data mining techniques. To overcome the period of the land, the technique used in data mining with the clustering method using the algorithm K-means. With the use of these techniques, the purpose of this research is to grouping products based on products of interest and less interest, advise on the stock of products, and know the products of interest and less demand.
Projection-based adiabatic elimination of bipartite open quantum systems

Adiabatic elimination methods allow the reduction of the space dimension needed to describe systems whose dynamics exhibit separation of timescale. For open quantum systems, it consists in eliminating the fast part assuming it has almost instantaneously reached its steady state and obtaining an approximation of the evolution of the slow part. These methods can be applied to eliminate a linear subspace within the system Hilbert space or, alternatively, to eliminate a fast subsystem in a bipartite quantum system. In this work, we extend an adiabatic elimination method used for removing fast degrees of freedom within a system [Phys. Rev. A 101, 042102 (2020)] to eliminate a subsystem from an open bipartite quantum system. As an illustration, we apply our technique to a dispersively coupled two-qubit system and in the case of the open Rabi model.
Learning to Stop While Learning to Predict

We would like to thank anonymous reviewers for providing constructive feedbacks. This work is supported in part by NSF grants CDS&E-1900017 D3SC, CCF-1836936 FMitF, IIS-1841351, CAREER IIS-1350983 to L.S. and grants from King Abdullah University of Science and Technology, under award numbers BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, REI/1/0018-01-01, and URF/1/4098-01-01.
Construction of Cyclic DNA Codes Over the Ring Z<sub>4</sub> + <i>v</i>Z<sub>4</sub>

In this work, we investigate DNA codes of odd length over the finite ring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R=\mathbb {Z}_{4}+v \mathbb {Z}_{4},v^{2}=v$ </tex-math></inline-formula> . Firstly, we give a map <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{n}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\{A, T, G,C\}^{2n}$ </tex-math></inline-formula> . Then cyclic codes of odd length satisfying the reverse and reverse-complement constraint are characterized over such ring. Moreover, when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> is a reverse-complement cyclic code over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> , we prove that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi (C)$ </tex-math></inline-formula> is a cyclic DNA code by using the method different from all the previously known ones, which helps us finding a new DNA code with 256 codewords. Finally, we consider the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GC$ </tex-math></inline-formula> content of code <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> over finite ring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{4} + v \mathbb {Z}_{4}$ </tex-math></inline-formula> by means of the Gray images of the minimal generating set of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> .
Rethinking The Pid Optimizer For Stochastic Optimization Of Deep Networks

Stochastic gradient descent with momentum (SGD-Momentum) always causes the overshoot problem due to the integral action of the momentum term. Recently, an ID optimizer is proposed to solve the overshoot problem with the help of derivative information. However, the derivative term suffers from the interference of the high-frequency noise, especially for the stochastic gradient descent method that uses minibatch data in each update step. In this work, we propose a complete PID optimizer, which weakens the effect of the D term and adds a P term to more stably alleviate the overshoot problem. To further reduce the interference of the high-frequency noise, two effective and efficient methods are proposed to stabilize the training process. Extensive experiments on three widely used benchmark datasets with different scales, i.e., MNIST, Cifar10 and TinyImageNet, demonstrate the superiority of our proposed PID optimizer on various popular deep neural networks.
3D hypothesis clustering for cross-view matching in multi-person motion capture

Abstract We present a multiview method for markerless motion capture of multiple people. The main challenge in this problem is to determine cross-view correspondences for the 2D joints in the presence of noise. We propose a 3D hypothesis clustering technique to solve this problem. The core idea is to transform joint matching in 2D space into a clustering problem in a 3D hypothesis space. In this way, evidence from photometric appearance, multiview geometry, and bone length can be integrated to solve the clustering problem efficiently and robustly. Each cluster encodes a set of matched 2D joints for the same person across different views, from which the 3D joints can be effectively inferred. We then assemble the inferred 3D joints to form full-body skeletons for all persons in a bottom–up way. Our experiments demonstrate the robustness of our approach even in challenging cases with heavy occlusion, closely interacting people, and few cameras. We have evaluated our method on many datasets, and our results show that it has significantly lower estimation errors than many state-of-the-art methods.
Federated Learning with Only Positive Labels

We consider learning a multi-class classification model in the federated setting, where each user has access to the positive data associated with only a single class. As a result, during each federated learning round, the users need to locally update the classifier without having access to the features and the model parameters for the negative classes. Thus, naively employing conventional decentralized learning such as the distributed SGD or Federated Averaging may lead to trivial or extremely poor classifiers. In particular, for the embedding based classifiers, all the class embeddings might collapse to a single point. To address this problem, we propose a generic framework for training with only positive labels, namely Federated Averaging with Spreadout (FedAwS), where the server imposes a geometric regularizer after each round to encourage classes to be spreadout in the embedding space. We show, both theoretically and empirically, that FedAwS can almost match the performance of conventional learning where users have access to negative labels. We further extend the proposed method to the settings with large output spaces.
Quantum path computing: computing architecture with propagation paths in multiple plane diffraction of classical sources of fermion and boson particles

Quantum computing (QC) architectures utilizing classical or coherent resources with Gaussian transformations are classically simulable as an indicator of the lack of QC power. Simple optical setups utilizing wave-particle duality and interferometers achieve QC speed-up with the cost of exponential complexity of resources in time, space or energy. However, linear optical networks composed of single-photon inputs and photon number measurements such as boson sampling achieve solving problems which are not efficiently solvable by classical computers while emphasizing the power of linear optics. In this article, quantum path computing (QPC) setup is introduced as the simplest optical QC satisfying five fundamental properties all-in-one: exploiting only the coherent sources being either fermion or boson, i.e., Gaussian wave packet of standard laser, simple setup of multiple plane diffraction (MPD) with multiple slits by creating distinct propagation paths, standard intensity measurement on the detector, energy-efficient design and practical problem solving capability. MPD is unique with non-Gaussian transformations by realizing an exponentially increasing number of highly interfering propagation paths while making classical simulation significantly hard. It does not require single-photon resources or number resolving detection mechanisms making the experimental implementation of QC significantly low complexity. QPC setup is utilized for the solutions of specific instances of two practical and hard number theoretical problems: partial sum of Riemann theta function and period finding to solve Diophantine approximation. Quantumness of MPD with negative volume of Wigner function is numerically analyzed, and open issues for the best utilization of QPC are discussed.
The Development of Interactive Media Assisted by Macromedia Flash to Improve the Ability of Understanding the Fiction Story Information in Elementary School Students

This study aims to: (1) Analyze the effectiveness level of interactive learning media assisted by Macromedia Flash on story text learning that will be developed at Public Elementary School 060856 Medan Perjuangan Subdistrict. (2) Analyze how the level of interactive learning media assisted by Macromedia Flash in story text learning that will be developed at Public Elementary School 060856 Medan Perjuangan Subdistrict. This study uses Borg &amp; Gall development research. Based on the analysis of learning media products assisted by Macromedia Flash from the experts, it was declared very effective and feasible to use with criteria of the validity of the material experts 85.42%, the design expert 91.00%, and the linguist 85.00%. Based on the completeness data of student learning outcomes before using interactive learning media assisted by Macromedia Flash obtained an average score of 62 while after using a large scale completeness reached an average of 90 with a gain score of 0.74 into the high category. And the student questionnaire responses to the effectiveness of interactive learning media assisted by Macromedia Flash reached 90% of 30 students. Based on this data interactive learning media assisted by Macromedia Flash are said to be effectively used in learning with the assumption that students' understanding of story text is higher when using interactive learning media assisted by Macromedia Flash.
Mining Discriminative K-Mers in DNA Sequences Using Sketches and Hardware Acceleration

Extracting discriminative k-mers is an important and challenging problem in DNA sequence analysis with applications in metagenomics and motif discovery. Despite the availability of multiple computational tools designed for this purpose, most discriminative k-mer discovery methods suffer from long execution times and high memory usage when processing large datasets. This paper presents a novel approach for discriminative k-mer discovery in DNA sequences, which leverages streaming and sketch algorithms to reduce space complexity and expose data parallelism, enabling the use of parallel platforms for accelerating the execution of computationally-intensive operations. To assess the performance of our method, we designed and implemented two versions of the algorithm that leverage parallelization at different levels: (i) a software version tailored for multithreading and vector instructions in commodity CPUs, and (ii) a custom architecture implemented on a Field-Programmable Gate Array (FPGA) accelerator that exploits fine-grain parallelism and deep pipelining on reconfigurable logic. Experimental results show that, when mining discriminative k-mers from a set of well-known ChIP-seq sequences, our parallel software implementation executes at least 15% faster than exact-counting tools, and requires at least five times less memory when processing large datasets. More importantly, we designed a custom FPGA-based accelerator for our algorithm on a Xilinx KCU1500 board, which achieves speedups above 78x with the largest datasets, compared to our parallel software implementation. The accelerator uses less than 3% of the logic resources available on the on-board XCKU115 Kintex-7 Ultrascale FPGA, and between 12% and 70% of the memory resources, depending on the size of the dataset.
Spatial Interpolators for Intra-Frame Resampling of SAR Videos: A Comparative Study Using Real-Time HD, Medical and Radar Data

Background: Real-time video coding is a very interesting area of research with extensive applications into remote sensing and medical imaging. Many research works and multimedia standards for this purpose have been developed. Some processing ideas in the area are focused on second-step (additional) compression of videos coded by existing standards like MPEG 4.14. Materials and Methods: In this article, an evaluation of some techniques with different complexity orders for video compression problem is performed. All compared techniques are based on interpolation algorithms in spatial domain. In details, the acquired data is according to four different interpolators in terms of computational complexity including fixed weights quartered interpolation (FWQI) technique, Nearest Neighbor (NN), Bi-Linear (BL) and Cubic Cnvolution (CC) interpolators. They are used for the compression of some HD color videos in real-time applications, real frames of video synthetic aperture radar (video SAR or ViSAR) and a high resolution medical sample. Results: Comparative results are also described for three different metrics including two reference- based Quality Assessment (QA) measures and an edge preservation factor to achieve a general perception of various dimensions of the mentioned problem. Conclusion: Comparisons show that there is a decidable trade-off among video codecs in terms of more similarity to a reference, preserving high frequency edge information and having low computational complexity.
A business model analysis of blockchain technology-based startup

Blockchain is drawing attention as rising technology with the advantages of security, transparency, and immutability by a decentralized network structure.However, blockchain technology is still an immature technology and lacks common standards.The researches on blockchain technology have been mainly focused on the financial sector but rarely applied to the supply chain in industry sectors.Especially, the blockchain technologies developed by technology entrepreneurs are still challenging to apply to an actual business due to a lack of understanding of the possibility of creating value.Therefore, it is necessary to provide technological entrepreneurs with an understanding of the business model and the feasibility of creating value with the new technology like blockchain.To address the issue, this study investigates how blockchain technology is effectively applicable and what value can be achieved from it.The purpose of this study is to analyze a livestock traceability system using blockchain technology and investigate its business model in terms of the value proposition, value delivery, and value creation.This study would provide insights into the business value creation of blockchain technology.
Application and Study of Musical Artificial Intelligence in Music Education Field

Abstract Artificial Intelligence (AI) is one of the fastest-growing key technologies in the history of human science and technology in the past half-century, and remarkable achievements have been made in the fields of robotics, language identification, image identification, natural language processing, and expert systems, etc. In this paper, the status quo of the general lack of AI basic knowledge in postgraduates at conservatories of music and art is analyzed. The necessity of offering popular science courses of “Music and AI” is pointed out. The interdisciplinary research findings in the field of AI and music education are expounded, classified and summarized, and the contents of popular science course “Music and Artificial Intelligence” are proposed and described in detail.