Reviewerly/exHarmony huggingface dataset:
Using S Gates and Relative Phase Toffoli Gates to Improve T-Count in Quantum Boolean Circuits

Toffoli gates are an important primitive in reversible Boolean logic. In quantum computation, these Toffoli gates are composed using other elementary gates, most notably the Clifford+T basis. However, in fault-tolerant implementations of quantum circuits, a non-transversal gate like the T gate incurs extra cost relative to transversal gates like the S and CNOT gates. Relative-phase Toffoli Gates (RTOF) have been proposed as a way to minimize this "T-depth" at the cost of incurring a relative phase that could skew the final quantum states. While previous research has proposed a way to use transversal S gates to eliminate this relative phase, this paper proposes a novel form of the RTOF that incorporates S gates in order to simplify generation of the logic to return the phase
Addressing the challenges of handling missing data in Data science applications

Missing data is a common issue in data science applications that can significantly impact the accuracy and reliability of data analysis and modeling. This paper explores the challenges associated with missing data and presents various approaches and techniques to effectively handle missing data in data science applications. We discuss both traditional statistical methods and advanced machine learning techniques that can be employed to address missing data. Additionally, we highlight the importance of understanding the underlying mechanisms causing missingness and the implications it has on data analysis. Through this paper, we aim to provide a comprehensive overview of the existing methods and methods for dealing with missing data, offering insights into their practical applicability and future research directions in this field.
Instance Paradigm Contrastive Learning for Domain Generalization

Domain Generalization (DG) aims to develop models that can learn from data in source domains and generalize to unseen target domains. Recently, some domain generalization algorithms have emerged, but most of them were designed with complex modules. Among all the prior methods under DG settings, contrastive learning has become a promising solution for simplicity and efficiency. However, existing contrastive learning neglects distribution shifts that causes severe domain confusions. In this paper, we propose an instance paradigm contrastive learning framework, introducing contrast between original features and novel paradigms to alleviate domain-specific distractions. And then we explore hard-pair information, an essential factor in contrastive learning, based on domain label and feature similarity. Moreover, to produce domain-invariant instance paradigms, we generate multiple views of the original images and design a novel channel-wise attention mechanism to dynamically combine features from all the views. Furthermore, a test-time feature integration module is designed to mimic the paradigms during the training process to improve generalization ability. Extensive experiments show that our method achieves state-of-the-art performance. The proposed algorithm can also serve as a plug-and-play module which improves performance of existing methods with a relatively large margin.
Acceleration of Large Transformer Model Training by Sensitivity-Based Layer Dropping

Transformer models are widely used in AI applications such as Natural Language Processing (NLP), Computer Vision (CV), etc. However, enormous computation workload be-comes an obstacle to train large transformer models efficiently. Recently, some methods focus on reducing the computation workload during the training by skipping some layers. How-ever, these methods use simple probability distribution and coarse-grained probability calculation, which significantly affect the model accuracy. To address the issue, in this paper we propose a novel method to accelerate training—Sensitivity-Based Layer Dropping (SBLD). SBLD uses lay-er-wise sensitivity data to switch on/off transformer layers in proper order to keep high accuracy. Besides, we adjust the probability of skipping transformer layers with a scheduler to accelerate training speed and get faster convergence. Our results show that SBLD solves the accuracy drop issue com-pared with prior layer dropping methods. Our SBLD method can decrease end-to-end training time by 19.67% during training of GPT-3 Medium model, the same time increasing the accuracy by 1.65% w.r.t. baseline. Furthermore, for SwinV2-L model the obtained Top-1 and Top-5 accuracies are also higher vs. the baseline. Thus, the proposed method is efficient and practical to improve the large transformer model training.
Portal Berita Berbasis Web Pada PT. Jambi Ekspres Televisi (JEKTV)

The development of technology has a positive impact in the delivery of news or information to the public to be easier and faster without any distance limitation in accessing the news. News portal is one of the media that is widely accessed by the public to get information through the internet. At PT. Jambi Ekspres Televisi (JEKTV) does not have a news portal to deliver such information quickly. The methodology used in designing this news portal is the prototype method. The purpose of designing this news portal is expected to help the company's performance in serving the news needed by the community quickly and the community can receive the news information up to date.
IDENTIFICATION OF TEUNGKU CHIK MUHAMMAD AMIN'S LEGACY

Teungku Chik Muhammad Amin is a scholar from Aceh who is descended from South Yemen's Arab community. Along with promoting society's understanding of Islam and the fight against Dutch forces, he is a person who is quite tenacious. This study's objectives were to learn more about Teungku Chik Muhammad Amin's life, identify his remains, and assess their state of preservation. The explicative (explanatory) or descriptive methods used in this study give a general overview of the archaeological data discovered in terms of time, form, and space as well as demonstrating the correlation between various research variables. In Meunasah Mancang Village, Tiro Truseb District, Pidie Regency, data were gathered by direct observation, interviews, literature reviews, and documentation. Aspects of the data's morphology, technology, and stylistic composition were characterized and examined. The identification has revealed various Teungku Chik Muhammad Amin relics, including Zawiyah Cut, a study hall, an old pond or ablution site, and an old well. Teungku Chik Muhammad Amin's remains have similarly gotten less attention from the parties involved, as seen by their current state. The government of Pidie Regency and other relevant parties are urged to be able to preserve this historical legacy as the cultural heritage of the Acehnese people.
Global Convergence of Two-Timescale Actor-Critic for Solving Linear Quadratic Regulator

The actor-critic (AC) reinforcement learning algorithms have been the powerhouse behind many challenging applications. Nevertheless, its convergence is fragile in general. To study its instability, existing works mostly consider the uncommon double-loop variant or basic models with finite state and action space. We investigate the more practical single-sample two-timescale AC for solving the canonical linear quadratic regulator (LQR) problem, where the actor and the critic update only once with a single sample in each iteration on an unbounded continuous state and action space. Existing analysis cannot conclude the convergence for such a challenging case. We develop a new analysis framework that allows establishing the global convergence to an epsilon-optimal solution with at most an order of epsilon to -2.5 sample complexity. To our knowledge, this is the first finite-time convergence analysis for the single sample two-timescale AC for solving LQR with global optimality. The sample complexity improves those of other variants by orders, which sheds light on the practical wisdom of single sample algorithms. We also further validate our theoretical findings via comprehensive simulation comparisons.
Copyright-Certified Distillation Dataset: Distilling One Million Coins into One Bitcoin with Your Private Key

The rapid development of neural network dataset distillation in recent years has provided new ideas in many areas such as continuous learning, neural network architecture search and privacy preservation. Dataset distillation is a very effective method to distill large training datasets into small data, thus ensuring that the test accuracy of models trained on their synthesized small datasets matches that of models trained on the full dataset. Thus, dataset distillation itself is commercially valuable, not only for reducing training costs, but also for compressing storage costs and significantly reducing the training costs of deep learning. However, copyright protection for dataset distillation has not been proposed yet, so we propose the first method to protect intellectual property by embedding watermarks in the dataset distillation process. Our approach not only popularizes the dataset distillation technique, but also authenticates the ownership of the distilled dataset by the models trained on that distilled dataset.
PiCor: Multi-Task Deep Reinforcement Learning with Policy Correction

Multi-task deep reinforcement learning (DRL) ambitiously aims to train a general agent that masters multiple tasks simultaneously. However, varying learning speeds of different tasks compounding with negative gradients interference makes policy learning inefficient. In this work, we propose PiCor, an efficient multi-task DRL framework that splits learning into policy optimization and policy correction phases. The policy optimization phase improves the policy by any DRL algothrim on the sampled single task without considering other tasks. The policy correction phase first constructs an adaptive adjusted performance constraint set. Then the intermediate policy learned by the first phase is constrained to the set, which controls the negative interference and balances the learning speeds across tasks. Empirically, we demonstrate that PiCor outperforms previous methods and significantly improves sample efficiency on simulated robotic manipulation and continuous control tasks. We additionally show that adaptive weight adjusting can further improve data efficiency and performance.
Multiagent MST Cover: Pleasing All Optimally via a Simple Voting Rule

Given a connected graph on whose edges we can build roads to connect the nodes, a number of agents hold possibly different perspectives on which edges should be selected by assigning different edge weights. Our task is to build a minimum number of roads so that every agent has a spanning tree in the built subgraph whose weight is the same as a minimum spanning tree in the original graph. We first show that this problem is NP-hard and does not admit better than ((1-o(1)) ln k)-approximation polynomial-time algorithms unless P = NP, where k is the number of agents. We then give a simple voting algorithm with an optimal approximation ratio. Moreover, our algorithm only needs to access the agents' rankings on the edges. Finally, we extend our problem to submodular objective functions and Matroid rank constraints.
Batik Discharge Method is Keeping Traditional Batik Sarongs Alive

This project was partly the outcome of a visit to East Coast batik makers, specifically in Kelantan and Terengganu. There was a lot of activity there due to the visit to the Ayu Fashion Batik workshop in Tumpat, Kelantan. The observation at the session helped contribute to a better knowledge of the process of methodical batik discharge. An interview with the owner of a batik producer provides a more in-depth explanation of the process, which reveals that this approach was relatively based on an experiment in the batik technique to vary the batik sarong and maintain and keep the tradition.
Can You Answer This? – Exploring Zero-Shot QA Generalization Capabilities in Large Language Models (Student Abstract)

The buzz around Transformer-based language models (TLM) such as BERT, RoBERTa, etc. is well-founded owing to their impressive results on an array of tasks. However, when applied to areas needing specialized knowledge (closed-domain), such as medical, finance, etc. their performance takes drastic hits, sometimes more than their older recurrent/convolutional counterparts. In this paper, we explore zero-shot capabilities of large LMs for extractive QA. Our objective is to examine performance change in the face of domain drift i.e. when the target domain data is vastly different in semantic and statistical properties from the source domain and attempt to explain the subsequent behavior. To this end, we present two studies in this paper while planning further experiments later down the road. Our findings indicate flaws in the current generation of TLM limiting their performance on closed-domain tasks.
APF-APSO: UAV swarm cooperation algorithm for multi-target search in complex reconnaissance environments

Multi-target search is a key part of UAV swarm cooperative reconnaissance. UAVs need to avoid threats in complex reconnaissance environments. In this paper, an optimization algorithm combining the artificial potential field with adaptive particle swarm algorithm (APF-APSO) is proposed for UAV swarm cooperative multi-target search mission. Firstly, we combine the PSO algorithm with the APF algorithm, adding a repulsive force field generated by the UAV's search path and using the force field function as a fitness function, which improves the obstacle avoidance and search efficiency of the UAV swarm. Secondly, a method for self-adaptive adjustment of personal cognition and social cognition factors is proposed, and an adaptive adjustment strategy of inertia weight is introduced to avoid the search falling into local optimum. The simulation results show that the proposed algorithm has higher search efficiency compared with other two PSO baseline algorithms in the same situation.
Third Eye Smart Aid for Visually Impaired

Abstract Smartphones are less likely to be considered as assistive technology for visual impairment among a large majority of health care providers, excluding vision rehabilitation professionals, and the public who are not familiar with accessible features and apps. They face significant challenges in terms of accessibility and inclusion in the smartphone environment. The world of money has completely changed, but some things remain the same. The quickly expanding utilization of CREDIT/ DEBIT cards and all the other different forms of money transfer mechanisms, including electronic payment, has certainly made its mark in today’s world. However, paper money is still broadly used in today’s world for ordinary exchanges because of its convenience. Due to similarity of the texture of the paper, and the different sizes that exist between different categories, visually impaired people face problems with monetary transactions because of their inability to recognize paper currency. The inability to read printed documents is a disadvantage for blind people. Speech based applications can help improve support for visually impaired people. The proposed system aims to provide assistive technology for currency recognition, text recognition to help visually impaired people.
Application of a Self-supervised Learning Technique for Monitoring Industrial Spaces

Supervised learning has reached a bottleneck as they require expensive and time-consuming annotations. In addition, in some problems, such as in industrial spaces, it is not always possible to acquire a large number of images. Self-supervised learning helps these issues by extracting information from the data itself, without requiring labels and has achieved good performance, closing the gap between supervised and self-supervised learning. This work presents the application of a self-supervised learning method - SwAV, that classifies anomalies in an industrial space, evaluates its performance and compares the results to the supervised paradigm.
Social Relation Reasoning Based on Triangular Constraints

Social networks are essentially in a graph structure where persons act as nodes and the edges connecting nodes denote social relations. The prediction of social relations, therefore, relies on the context in graphs to model the higher-order constraints among relations, which has not been exploited sufficiently by previous works, however. In this paper, we formulate the paradigm of the higher-order constraints in social relations into triangular relational closed-loop structures, i.e., triangular constraints, and further introduce the triangular reasoning graph attention network (TRGAT). Our TRGAT employs the attention mechanism to aggregate features with triangular constraints in the graph, thereby exploiting the higher-order context to reason social relations iteratively. Besides, to acquire better feature representations of persons, we introduce node contrastive learning into relation reasoning. Experimental results show that our method outperforms existing approaches significantly, with higher accuracy and better consistency in generating social relation graphs.
PMaF: Deep Declarative Layers for Principal Matrix Features

We explore two differentiable deep declarative layers, namely least squares on sphere (LESS) and implicit eigen decomposition (IED), for learning the principal matrix features (PMaF). It can be used to represent data features with a low-dimensional vector containing dominant information from a high-dimensional matrix. We first solve the problems with iterative optimization in the forward pass and then backpropagate the solution for implicit gradients under a bi-level optimization framework. Particularly, adaptive descent steps with the backtracking line search method and descent decay in the tangent space are studied to improve the forward pass efficiency of LESS. Meanwhile, exploited data structures are used to greatly reduce the computational complexity in the backward pass of LESS and IED. Empirically, we demonstrate the superiority of our layers over the off-the-shelf baselines by comparing the solution optimality and computational requirements.
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners

Representation learning has been evolving from traditional supervised training to Contrastive Learning (CL) and Masked Image Modeling (MIM). Previous works have demonstrated their pros and cons in specific scenarios, i.e., CL and supervised pre-training excel at capturing longer-range global patterns and enabling better feature discrimination, while MIM can introduce more local and diverse attention across all transformer layers. In this paper, we explore how to obtain a model that combines their strengths. We start by examining previous feature distillation and mask feature reconstruction methods and identify their limitations. We find that their increasing diversity mainly derives from the asymmetric designs, but these designs may in turn compromise the discrimination ability. In order to better obtain both discrimination and diversity, we propose a simple but effective Hybrid Distillation strategy, which utilizes both the supervised/CL teacher and the MIM teacher to jointly guide the student model. Hybrid Distill imitates the token relations of the MIM teacher to alleviate attention collapse, as well as distills the feature maps of the supervised/CL teacher to enable discrimination. Furthermore, a progressive redundant token masking strategy is also utilized to reduce the distilling costs and avoid falling into local optima. Experiment results prove that Hybrid Distill can achieve superior performance on different benchmarks.
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses

Graph Neural Networks (GNNs) are emerging as a powerful tool for learning from graph-structured data and performing sophisticated inference tasks in various application domains. Although GNNs have been shown to be effective on modest-sized graphs, training them on large-scale graphs remains a significant challenge due to lack of efficient data access and data movement methods. Existing frameworks for training GNNs use CPUs for graph sampling and feature aggregation, while the training and updating of model weights are executed on GPUs. However, our in-depth profiling shows the CPUs cannot achieve the throughput required to saturate GNN model training throughput, causing gross under-utilization of expensive GPU resources. Furthermore, when the graph and its embeddings do not fit in the CPU memory, the overhead introduced by the operating system, say for handling page-faults, comes in the critical path of execution. To address these issues, we propose the GPU Initiated Direct Storage Access (GIDS) dataloader, to enable GPU-oriented GNN training for large-scale graphs while efficiently utilizing all hardware resources, such as CPU memory, storage, and GPU memory with a hybrid data placement strategy. By enabling GPU threads to fetch feature vectors directly from storage, GIDS dataloader solves the memory capacity problem for GPU-oriented GNN training. Moreover, GIDS dataloader leverages GPU parallelism to tolerate storage latency and eliminates expensive page-fault overhead. Doing so enables us to design novel optimizations for exploiting locality and increasing effective bandwidth for GNN training. Our evaluation using a single GPU on terabyte-scale GNN datasets shows that GIDS dataloader accelerates the overall DGL GNN training pipeline by up to 392X when compared to the current, state-of-the-art DGL dataloader.
Analisis Perbandingan Metode Regresi Linier, Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah

The need for a place to live is one that many people prepare, both millennials and adults and the elderly. With the continued increase in population growth in Indonesia and increasing public interest in buying a place to live early on, this can make not all groups of people have a place to live or a house that is quite livable. Related to this, the public needs up-to-date information related to predictions of house prices both for housing and second-hand housing prices for planning purposes in the future. The purpose of this study is to carry out a comparative analysis of the prediction results of house prices with several Machine Learning algorithms consist of Linear Regression, Random Forest Regression and Gradient Boosted Trees Regression. Evaluation for all the method applying Cross-Validation. The evaluation is seen from the smallest Root Mean Square Error (RMSE) error rate of each testing method. The results of this study are the Random Forest Regression obtained an RMSE value of 0.440, the Linear Regression model obtained an RMSE value of 0.515 and the RMSE value of Gradient Boosted Trees Regression of 0.508. The results were obtained from testing a dataset of 2011 records with a division of 80% for data training and 20% for data testing, the data has 6 attributes used in testing including house prices, land area, building area, number of bathrooms, number of bedrooms and the number of garages. In this study, prediction results using the Random Forest Regression method yielded the highest accuracy of 81.5% compared to the Linear Regression and Gradient Boosted Trees Regression methods.