Reviewerly/exHarmony huggingface dataset:
Interrelation of equivariant Gaussian processes and convolutional neural networks

Currently there exists rather promising new trend in machine leaning (ML) based on the relationship between neural networks (NN) and Gaussian processes (GP), including many related subtopics, e.g., signal propagation in NNs, theoretical derivation of learning curve for NNs, QFT methods in ML, etc. An important feature of convolutional neural networks (CNN) is their equivariance (consistency) with respect to the symmetry transformations of the input data. In this work we establish a relationship between the many-channel limit for CNNs equivariant with respect to two-dimensional Euclidean group with vector-valued neuron activations and the corresponding independently introduced equivariant Gaussian processes (GP).
Reinforcement Learning for Self-exploration in Narrow Spaces

In narrow spaces, motion planning based on the traditional hierarchical autonomous system could cause collisions due to mapping, localization, and control noises, especially for car-like Ackermann-steering robots which suffer from non-convex and non-holonomic kinematics. To tackle these problems, we leverage deep reinforcement learning which is verified to be effective in self-decision-making, to self-explore in narrow spaces without a given map and destination while avoiding collisions. Specifically, based on our Ackermann-steering rectangular-shaped ZebraT robot and its Gazebo simulator, we propose the rectangular safety region to represent states and detect collisions for rectangular-shaped robots, and a carefully crafted reward function for reinforcement learning that does not require the waypoint guidance. For validation, the robot was first trained in a simulated narrow track. Then, the well-trained model was transferred to other simulation tracks and could outperform other traditional methods including classical and learning methods. Finally, the trained model is demonstrated in the real world with our ZebraT robot.
Experimental Multi-state Quantum Discrimination in the Frequency Domain with Quantum Dot Light

The quest for the realization of effective quantum state discrimination strategies is of great interest for quantum information technology, as well as for fundamental studies. Therefore, it is crucial to develop new and more efficient methods to implement discrimination protocols for quantum states. Among the others, single photon implementations are more advisable, because of their inherent security advantage in quantum communication scenarios. In this work, we present the experimental realization of a protocol employing a time-multiplexing strategy to optimally discriminate among eight non-orthogonal states, encoded in the four-dimensional Hilbert space spanning both the polarization degree of freedom and photon energy. The experiment, built on a custom-designed bulk optics analyser setup and single photons generated by a nearly deterministic solid-state source, represents a benchmarking example of minimum error discrimination with actual quantum states, requiring only linear optics and two photodetectors to be realized. Our work paves the way for more complex applications and delivers a novel approach towards high-dimensional quantum encoding and decoding operations.
AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification

Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority class. Cost-sensitive learning tackles this problem by treating the classes differently, formulated typically via a user-defined fixed misclassification cost matrix provided as input to the learner. Such parameter tuning is a challenging task that requires domain knowledge and moreover, wrong adjustments might lead to overall predictive performance deterioration. In this work, we propose a novel cost-sensitive boosting approach for imbalanced data that dynamically adjusts the misclassification costs over the boosting rounds in response to model's performance instead of using a fixed misclassification cost matrix. Our method, called AdaCC, is parameter-free as it relies on the cumulative behavior of the boosting model in order to adjust the misclassification costs for the next boosting round and comes with theoretical guarantees regarding the training error. Experiments on 27 real-world datasets from different domains with high class imbalance demonstrate the superiority of our method over 12 state-of-the-art cost-sensitive boosting approaches exhibiting consistent improvements in different measures, for instance, in the range of [0.3%-28.56%] for AUC, [3.4%-21.4%] for balanced accuracy, [4.8%-45%] for gmean and [7.4%-85.5%] for recall.
Gaussian dynamics equation in normal product form

In this paper, we discuss the normal product form of the density operator of multimode Gaussian states, and obtain the correlation equation between the kernel matrix R of the Gaussian density operator in the normal product form and its kernel matrix G in the standard quadratic form. Further, we explore the time evolution mechanism of R and obtain the Gaussian dynamical equation under the normal product R=i(RJH-HJR). Our work is devoted to searching for another mechanism for Gaussian dynamics. By exploring the description of the normal ordered density matrix under the coherent state representation, we find that our mechanism is feasible and easy to operate.
Quantum Computing Methods for Supply Chain Management

Quantum computing is expected to have transformative influences on many domains, but its practical deployments on industry problems are underexplored. We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management. Many problems in supply chain management involve large state and action spaces and pose computational challenges on classic computers. We develop a quantized policy iteration algorithm to solve an inventory control problem and demonstrative its effectiveness. We also discuss in-depth the hardware requirements and potential challenges on implementing this quantum algorithm in the near term. Our simulations and experiments are powered by \texttt{IBM Qiskit} and the \texttt{qBraid} system.
"Financial Resource Management Challenges for Principals in Public Secondary Schools in Langata Subcounty"

Additionally, students, should be provided with adequate teaching/learning materials; the teachers, heads of departments, finance committee, top management and the principal should treat the students fully as learners, and they should not dismiss any student from the school due to lack of fees; they should also provide counselling/ social workers departments to check on the students welfare and their mental problems regularly.Finally, the study recommended
Fast, Accurate and Object Boundary-Aware Surface Normal Estimation from Depth Maps

This paper proposes a fast and accurate surface normal estimation method which can be directly used on depth maps (organized point clouds). The surface normal estimation process is formulated as a closed-form expression. In order to reduce the effect of measurement noise, the averaging operation is utilized in multi-direction manner. The multi-direction normal estimation process is reformulated in the next step to be implemented efficiently. Finally, a simple yet effective method is proposed to remove erroneous normal estimation at depth discontinuities. The proposed method is compared to well-known surface normal estimation algorithms. The results show that the proposed algorithm not only outperforms the baseline algorithms in term of accuracy, but also is fast enough to be used in real-time applications.
A Secure Cloud Framework for Big Data Analytics Using a Hybrid Encryption Method

Abstract This paper proposes a hybrid encryption system that is based on Elliptical Curve Cryptography (ECC) and Fully Homomorphic Encryption (FHE). FHE can be used as powerful public-key encryption that allows any user to do computations on encrypted data. To develop a data analytics technique in a distributed environment, a data clustering approach is used to divide big data into several subsets. In this method, many virtual nodes process big data simultaneously and in parallel. Three datasets are used to evaluate the proposed framework. The results show that in the distributed model the execution time is decreased up to 48% using more processors working on data. Additionally, the generated results are compared with a centralized model to evaluate the efficiency and performance of the proposed framework. Experiments show that the developed framework can improve the performance of big data analytics while making sure that data is protected and secured.
Fault-tolerant quantum algorithm for dual-threshold image segmentation

Abstract The intrinsic high parallelism and entanglement characteristics of quantum computing have made quantum image processing techniques a focus of great interest. One of the most widely used techniques in image processing is segmentation, which in one of their most basic forms can be carried out using thresholding algorithms. In this paper, a fault-tolerant quantum dual-threshold algorithm has been proposed. This algorithm is based on Clifford+T gates. Because T gates add fault tolerance but have the trade-off of having a much higher cost than other quantum gates,our focus has been on reducing the number of T gates. This has allowed adding noise tolerance, computational cost reduction and fault toleranceto the state-of-the-art dual-threshold segmentation circuits. Sincethe dual-threshold image segmentation involves the comparison operation,as part of this work we have implemented two comparator circuits.These circuits optimise the metrics T-count and T-depth with respect to the best circuit comparators currently available in the literature.
Walk Like Me: Video to Video Action Transfer

&lt;p&gt;We present a novel method for transferring human actions from a source to a target video with improved human motion smoothness and better image quality. Unlike the previous image-to-image pose transfer and synthesis work, which in actuality transfer information from an image to another image, we take consideration of temporal pose variations. Recent methods aiming at video-to-video human motion transfer suffer from the generated image quality. In contrast our proposed Video-to-video Action Transfer GAN (VAT-GAN) has achieved better image quality by employing a cascaded sequence of Action Transfer Blocks (ATB) with multi-resolution structure similarity (MR-SSIM) loss. The addition of MR-SSIM loss has resulted in improved generated image quality compared to the previously used ℓ1 based loss. To ensure temporal human motion smoothness, we make the ATBs aware of temporal pose variations of the target video. The proposed VAT-GAN is evaluated on an existing dataset proposed by Chan et al. [1] as well as on new test videos downloaded from the internet consisting of unseen persons, actions and poses. The proposed VAT-GAN has achieved better visual and quantitative performance compared to the existing state of the art methods both in terms of image quality and human motion smoothness. The code and the results including synthesized videos will be made publicly available: https://github.com/MehwishG/VAT-GAN. &lt;/p&gt;
Confidence-Guided Data Augmentation for Improved Semi-Supervised Training

We propose a new strategy to improve the accuracy and robustness of image classification. First, we train a baseline CNN model. Then, we identify challenging regions in the feature space by identifying all misclassified samples, and correctly classified samples with low confidence values. These samples are then used to train a Variational AutoEncoder (VAE). Next, the VAE is used to generate synthetic images. Finally, the generated synthetic images are used in conjunction with the original labeled images to train a new model in a semi-supervised fashion. Empirical results on benchmark datasets such as STL10 and CIFAR-100 show that the synthetically generated samples can further diversify the training data, leading to improvement in image classification in comparison with the fully supervised baseline approaches using only the available data.
A Decade of Code Comment Quality Assessment: A Systematic Literature Review

Code comments are important artifacts in software systems and play a paramount role in many software engineering (SE) tasks related to maintenance and program comprehension. However, while it is widely accepted that high quality matters in code comments just as it matters in source code, assessing comment quality in practice is still an open problem. First and foremost, there is no unique definition of quality when it comes to evaluating code comments. The few existing studies on this topic rather focus on specific attributes of quality that can be easily quantified and measured. Existing techniques and corresponding tools may also focus on comments bound to a specific programming language, and may only deal with comments with specific scopes and clear goals (e.g., Javadoc comments at the method level, or in-body comments describing TODOs to be addressed). In this paper, we present a Systematic Literature Review (SLR) of the last decade of research in SE to answer the following research questions: (i) What types of comments do researchers focus on when assessing comment quality? (ii) What quality attributes (QAs) do they consider? (iii) Which tools and techniques do they use to assess comment quality?, and (iv) How do they evaluate their studies on comment quality assessment in general? Our evaluation, based on the analysis of 2353 papers and the actual review of 47 relevant ones, shows that (i) most studies and techniques focus on comments in Java code, thus may not be generalizable to other languages, and (ii) the analyzed studies focus on four main QAs of a total of 21 QAs identified in the literature, with a clear predominance of checking consistency between comments and the code. We observe that researchers rely on manual assessment and specific heuristics rather than the automated assessment of the comment quality attributes.
CLAIRE -- Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications

We study the performance of CLAIRE -- a diffeomorphic multi-node, multi-GPU image-registration algorithm, and software -- in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full-resolution registrations obtained with CLAIRE to lower-resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality -- but not always. For example, downsampling a synthetic image from $1024^3$ to $256^3$ decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low-contrast high-resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in a reasonable time. The highest resolution considered is CLARITY images of size $2816\times3016\times1162$. To the best of our knowledge, this is the first study on image registration quality at such resolutions.
Selective Token Generation for Few-shot Natural Language Generation

Natural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability. Among them, additive learning that incorporates a task-specific adapter on top of the fixed large-scale PLM has been popularly used in the few-shot setting. However, this added adapter is still easy to disregard the knowledge of the PLM especially for few-shot natural language generation (NLG) since an entire sequence is usually generated by only the newly trained adapter. Therefore, in this work, we develop a novel additive learning algorithm based on reinforcement learning (RL) that selectively outputs language tokens between the task-general PLM and the task-specific adapter during both training and inference. This output token selection over the two generators allows the adapter to take into account solely the task-relevant parts in sequence generation, and therefore makes it more robust to overfitting as well as more stable in RL training. In addition, to obtain the complementary adapter from the PLM for each few-shot task, we exploit a separate selecting module that is also simultaneously trained using RL. Experimental results on various few-shot NLG tasks including question answering, data-to-text generation and text summarization demonstrate that the proposed selective token generation significantly outperforms the previous additive learning algorithms based on the PLMs.
Error type analysis and data labeling through topic modeling of error text in operating smart factory

Data labeling is the task of supplementing data corresponding to the correct answer in training data for AI(Artificial Intelligence). Data labeling helps improve the performance of AI learning models, but is often done manually. Automating data labeling tasks so that high-quality learning data can be efficiently produced can serve as a foundation for the development of artificial intelligence.BR In this paper, we proposed a method using topic modeling to automate data labeling of error data produced in the smart factory control system. Error text files were created by extracting major error-related items and error messages from the database accumulated in the smart factory operating environment. Before the topic modeling, frequently appearing words were extracted through basic analysis of error text, and main causes of errors were roughly identified by visualizing them with bar graphs and word clouds. After that, major topics related to errors were extracted by applying topic modeling to the error text. Based on the key words included in the topics, meanings were given to each topic, error types were derived, and error type codes were also assigned. Coherence and Perplexity were calculated to derive the optimal number of topics, and 4-5 topics were found to be optimal. This paper is meaningful in that it confirmed the possibility of automating data labeling in big data including text data.
IMPELEMENTASI ALGORITMA KRIPTOGRAFI KLASIK CAESAR UNTUK RANCANG BANGUN APLIKASI E-VOTING BERBASIS WEB (STUDI KASUS : SMAN 10 TANGERANG)

Elections or we are familiar with the term voting are not only carried out in the election of the legislative council of political parties in a country, the election is also carried out in a school and university organization in the election of candidates for the chairman of the organization or student president. The voting system is always carried out by voting if political opponents or opponents of potential leaders experience the same vote. Voting is an activity carried out to choose a candidate for a predetermined election. In today's modern era, a lot of work is done automatically, including voting, which was previously done manually. Voting that is done manually is done by voting on paper, and the results are calculated on each paper which makes the calculation process take longer. Created a system that can facilitate the voting process. E-Voting is a candidate selection system and the most votes are made and processed in a digital system. System In this voting process there is a process for nominating candidates, selecting candidates, and voting results. With the existence of e-voting to conduct candidate selection with clear and not manipulated data. The data security process is carried out by encrypting data from an existing database using Caesar's Classical Cryptography Algorithm.&#x0D;
Information-Theoretically Private Matrix Multiplication From MDS-Coded Storage

We study two problems of private matrix multiplication, over a distributed computing system consisting of a master node, and multiple servers that collectively store a family of public matrices using Maximum-Distance-Separable (MDS) codes. In the first problem of Private and Secure Matrix Multiplication (PSMM) from colluding servers, the master intends to compute the product of its confidential matrix $\mathbf{A}$ with a target matrix stored on the servers, without revealing any information about $\mathbf{A}$ and the index of target matrix to some colluding servers. In the second problem of Fully Private Matrix Multiplication (FPMM) from colluding servers, the matrix $\mathbf{A}$ is also selected from another family of public matrices stored at the servers in MDS form. In this case, the indices of the two target matrices should both be kept private from colluding servers. We develop novel strategies for the two PSMM and FPMM problems, which simultaneously guarantee information-theoretic data/index privacy and computation correctness. We compare the proposed PSMM strategy with a previous PSMM strategy with a weaker privacy guarantee (non-colluding servers), and demonstrate substantial improvements over the previous strategy in terms of communication and computation overheads. Moreover, compared with a baseline FPMM strategy that uses the idea of Private Information Retrieval (PIR) to directly retrieve the desired matrix multiplication, the proposed FPMM strategy significantly reduces storage overhead, but slightly incurs large communication and computation overheads.
AdaInject: Injection Based Adaptive Gradient Descent Optimizers for Convolutional Neural Networks

The convolutional neural networks (CNNs) are generally trained using stochastic gradient descent (SGD) based optimization techniques. The existing SGD optimizers generally suffer with the overshooting of the minimum and oscillation near minimum. In this paper, we propose a new approach, hereafter referred as AdaInject, for the gradient descent optimizers by injecting the second order moment into the first order moment. Specifically, the short-term change in parameter is used as a weight to inject the second order moment in the update rule. The AdaInject optimizer controls the parameter update, avoids the overshooting of the minimum and reduces the oscillation near minimum. The proposed approach is generic in nature and can be integrated with any existing SGD optimizer. The effectiveness of the AdaInject optimizer is explained intuitively as well as through some toy examples. We also show the convergence property of the proposed injection based optimizer. Further, we depict the efficacy of the AdaInject approach through extensive experiments in conjunction with the state-of-the-art optimizers, namely AdamInject, diffGradInject, RadamInject, and AdaBeliefInject on four benchmark datasets. Different CNN models are used in the experiments. A highest improvement in the top-1 classification error rate of $16.54\%$ is observed using diffGradInject optimizer with ResNeXt29 model over the CIFAR10 dataset. Overall, we observe very promising performance improvement of existing optimizers with the proposed AdaInject approach. The code is available at: \url{https://github.com/shivram1987/AdaInject}.