Reviewerly/exHarmony huggingface dataset:
MarianCG: a code generation transformer model inspired by machine translation

Abstract The idea that computers can build their own programs is extremely significant, and many researchers are working on this challenge. Code generation is described as the process of generating executable code that can be run directly on the computer and fulfills the natural language requirements. It is an intriguing topic that might assist developers to learn a new software technology or programming language, or it could be a simple technique to help in coding through the description of the natural language code developer. In this paper, we present MarianCG, a code generation Transformer model used to tackle the code generation challenge of generating python code from natural language descriptions. Marian neural machine translation (NMT), which is the core model of the Microsoft Translator, is the basis for our NL-to-Code translation engine and is the heart of the teaching model. MarianMT is the teacher language model in our study, and it is one of the most successful machine translation transformers. In our approach, we use a sinusoidal positional embedding technique to represent the position of each token in the text, as well as no layer normalization embedding. Our code generation approach, MarianCG, is based on fine-tuning a machine translation pre-trained language model. This allows us to demonstrate that the pre-trained translation model can also operate and work as a code generation model. The proposed model outperforms recent state-of-the-art models in the problem of code generation when trained on the CoNaLa and DJANGO datasets. MarianCG model scores a BLEU score of 34.43 and an exact match accuracy of 10.2% on the CoNaLa dataset. Also, this model records a BLEU score of 90.41 and an exact match accuracy of 81.83% on the DJANGO dataset. The implementation of MarianCG model and relevant resources are available at https://www.github.com/AhmedSSoliman/MarianCG-NL-to-Code .
Will adoption of blockchain technology be challenging: evidence from Indian banking industry

Purpose Blockchain technology is predicted by many to be the most disruptive technology which might bring accessibility, efficiency and transparency in the financial industry. This study aims to understand the challenges likely to be faced by the Indian banking industry while adopting the technology. Design/methodology/approach The study adopted a qualitative approach to explore the challenges faced by the banking industry in India. Semi-structured in-depth interviews with senior executives and academicians in the finance and the information technology industries helped gain explorative insights about the challenges. Findings Thematic analysis suggested a framework comprising five challenges while adopting blockchain technology. These challenges relate to technology, organisation, operation, regulator and environmental context. Originality/value The paper contributes to the limited literature on the nascent blockchain technology adoption in banking industry in an emerging country context.
Multidimensional hyperspin machine

Abstract From condensed matter to quantum chromodynamics, multidimensional spins are a fundamental paradigm, with a pivotal role in combinatorial optimization and machine learning. Machines formed by coupled parametric oscillators can simulate spin models, but only for Ising or low-dimensional spins. Currently, machines implementing arbitrary dimensions remain a challenge. Here, we introduce and validate a hyperspin machine to simulate multidimensional continuous spin models. We realize high-dimensional spins by pumping groups of parametric oscillators, and show that the hyperspin machine finds to a very good approximation the ground state of complex graphs. The hyperspin machine can interpolate between different dimensions by tuning the coupling topology, a strategy that we call “dimensional annealing”. When interpolating between the XY and the Ising model, the dimensional annealing substantially increases the success probability compared to conventional Ising simulators. Hyperspin machines are a new computational model for combinatorial optimization. They can be realized by off-the-shelf hardware for ultrafast, large-scale applications in classical and quantum computing, condensed-matter physics, and fundamental studies.
Top-Down Synthesis for Library Learning

This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL). The algorithm builds abstractions directly from initial DSL primitives, using syntactic pattern matching of intermediate abstractions to intelligently prune the search space and guide the algorithm towards abstractions that maximally capture shared structures in the corpus. We present an implementation of the approach in a tool called Stitch and evaluate it against the state-of-the-art deductive library learning algorithm from DreamCoder. Our evaluation shows that Stitch is 3-4 orders of magnitude faster and uses 2 orders of magnitude less memory while maintaining comparable or better library quality (as measured by compressivity). We also demonstrate Stitch’s scalability on corpora containing hundreds of complex programs that are intractable with prior deductive approaches and show empirically that it is robust to terminating the search procedure early—further allowing it to scale to challenging datasets by means of early stopping.
A particle swarm optimization algorithm with empirical balance strategy

As an important research direction of swarm intelligence algorithm, particle swarm optimization (PSO) has become a popular evolutionary method and received extensive attention in the past decades. Despite many PSO variants have been proposed, how to maintain a good balance between the exploration and exploitation abilities, and how to jump out of the local optimal position are still challenges. In this article, based on empirical balance strategy, a new particle swarm optimization (EBPSO) algorithm is presented. Firstly, based on an adaptive adjustment mechanism, the algorithm can choose a better strategy from two search equations, which can maintain the balance between the exploration and exploitation abilities. Secondly, to utilize the information of individual historical optimal solution and the optimal solution of the current population, a weight for adjusting their influence is introduced into the search equation. Thirdly, by introducing the diversity of population, a moving equation for dynamically adjusting the search ability of the algorithm is proposed. Finally, to avoid falling into local optimum and to search the potential location, a dynamic random search mechanism is proposed, which is designed by using the information of the current optimal solution. Compared with some state-of-the-art algorithms, the experimental results show that EBPSO has excellent solution quality and convergence characteristic on almost all test problems.
Implementation of a smart energy meter using blockchain and Internet of Things: A step toward energy conservation

The looming energy crisis is affecting every sector of the world. The dire need to conserve energy has compelled researchers to bring automation to the power sector. The conservation of energy is one of the biggest challenges Third-World countries are facing in general and in Europe due to the Russian–Ukrainian war. There is a need to introduce such systems that can prevent energy loss and let users buy and sell excessive electricity they have. In the field of power and electricity, the Internet of Things (IoT) plays an active role in the conservation of energy. The new concept of smart grids is widely used for efficient transmission. The technique of blockchain can further reduce the wastage of energy and efficient consumption if it is used with smart grids. This article proposes a smart energy meter based on smart grids and blockchain. The proposed implementation is a demonstration containing a few microgrids, each with its very own blockchain. The users will use energy by making transactions, following the smart contracts. The focus is on the peer-to-peer transactions in a microgrid controlled by blockchain. The architectural design outcomes are a smart energy meter, a smart contract on the Ethereum blockchain, and an android application to monitor and control transactions and energy trade via smart contracts with other consumers.
Deep Deterministic Policy Gradient-Based Autonomous Driving for Mobile Robots in Sparse Reward Environments

In this paper, we propose a deep deterministic policy gradient (DDPG)-based path-planning method for mobile robots by applying the hindsight experience replay (HER) technique to overcome the performance degradation resulting from sparse reward problems occurring in autonomous driving mobile robots. The mobile robot in our analysis was a robot operating system-based TurtleBot3, and the experimental environment was a virtual simulation based on Gazebo. A fully connected neural network was used as the DDPG network based on the actor–critic architecture. Noise was added to the actor network. The robot recognized an unknown environment by measuring distances using a laser sensor and determined the optimized policy to reach its destination. The HER technique improved the learning performance by generating three new episodes with normal experience from a failed episode. The proposed method demonstrated that the HER technique could help mitigate the sparse reward problem; this was further corroborated by the successful autonomous driving results obtained after applying the proposed method to two reward systems, as well as actual experimental results.
A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

Introduction Gastric cancer is the fifth most common cancer in the world. At the same time, it is also the fourth most deadly cancer. Early detection of cancer exists as a guide for the treatment of gastric cancer. Nowadays, computer technology has advanced rapidly to assist physicians in the diagnosis of pathological pictures of gastric cancer. Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning. Therefore, this paper compares the performance of multiple algorithms in anticipation of applying ensemble learning to a practical gastric cancer classification problem. Methods The complementarity of sub-size pathology image classifiers when machine performance is insufficient is explored in this experimental platform. We choose seven classical machine learning classifiers and four deep learning classifiers for classification experiments on the GasHisSDB database. Among them, classical machine learning algorithms extract five different image virtual features to match multiple classifier algorithms. For deep learning, we choose three convolutional neural network classifiers. In addition, we also choose a novel Transformer-based classifier. Results The experimental platform, in which a large number of classical machine learning and deep learning methods are performed, demonstrates that there are differences in the performance of different classifiers on GasHisSDB. Classical machine learning models exist for classifiers that classify Abnormal categories very well, while classifiers that excel in classifying Normal categories also exist. Deep learning models also exist with multiple models that can be complementarity. Discussion Suitable classifiers are selected for ensemble learning, when machine performance is insufficient. This experimental platform demonstrates that multiple classifiers are indeed complementarity and can improve the efficiency of ensemble learning. This can better assist doctors in diagnosis, improve the detection of gastric cancer, and increase the cure rate.
Adaptive Fine-Grained Sketch-Based Image Retrieval

The recent focus on Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) has shifted towards generalising a model to new categories without any training data from them. In real-world applications, however, a trained FG-SBIR model is often applied to both new categories and different human sketchers, i.e., different drawing styles. Although this complicates the generalisation problem, fortunately, a handful of examples are typically available, enabling the model to adapt to the new category/style. In this paper, we offer a novel perspective – instead of asking for a model that generalises, we advocate for one that quickly adapts, with just very few samples during testing (in a few-shot manner). To solve this new problem, we introduce a novel model-agnostic meta-learning (MAML) based framework with several key modifications: (1) As a retrieval task with a margin-based contrastive loss, we simplify the MAML training in the inner loop to make it more stable and tractable. (2) The margin in our contrastive loss is also meta-learned with the rest of the model. (3) Three additional regularisation losses are introduced in the outer loop, to make the meta-learned FG-SBIR model more effective for category/style adaptation. Extensive experiments on public datasets suggest a large gain over generalisation and zero-shot based approaches, and a few strong few-shot baselines.
Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories

Tracking pixels in videos is typically studied as an optical flow estimation problem, where every pixel is described with a displacement vector that locates it in the next frame. Even though wider temporal context is freely available, prior efforts to take this into account have yielded only small gains over 2-frame methods. In this paper, we revisit Sand and Teller’s “particle video” approach, and study pixel tracking as a long-range motion estimation problem, where every pixel is described with a trajectory that locates it in multiple future frames. We re-build this classic approach using components that drive the current state-of-the-art in flow and object tracking, such as dense cost maps, iterative optimization, and learned appearance updates. We train our models using long-range amodal point trajectories mined from existing optical flow data that we synthetically augment with multi-frame occlusions. We test our approach in trajectory estimation benchmarks and in keypoint label propagation tasks, and compare favorably against state-of-the-art optical flow and feature tracking methods.
Panoramic Human Activity Recognition

To obtain a more comprehensive activity understanding for a crowded scene, in this paper, we propose a new problem of panoramic human activity recognition (PAR), which aims to simultaneously achieve the recognition of individual actions, social group activities, and global activities. This is a challenging yet practical problem in real-world applications. To track this problem, we develop a novel hierarchical graph neural network to progressively represent and model the multi-granular human activities and mutual social relations for a crowd of people. We further build a benchmark to evaluate the proposed method and other related methods. Experimental results verify the rationality of the proposed PAR problem, the effectiveness of our method and the usefulness of the benchmark. We have released the source code and benchmark to the public for promoting the study on this problem.
Prompt Learning with Cross-Modal Feature Alignment for Visual Domain Adaptation

Exploring the capacity of pre-trained large-scale models to learn common features of multimodal data and the effect of knowledge transfer on downstream tasks are two major trends in the multimedia field. However, existing studies usually use pre-trained models as feature extractors, or as the teacher model to achieve knowledge distillation of downstream tasks. Therefore, the cross-modal knowledge transfer mechanism and the knowledge forgetting problem of pre-trained large models have not been fully investigated.To address the above issues, this paper explores the fine-tuning strategy, feature selection strategy and semantic guidance approach in the migration process of pre-trained large models.Aiming at the problem of knowledge forgetting during “fine-tuning”, an image classification algorithm (PMHANet) integrating a pre-trained large-scale model and heterogeneous feature alignment is proposed.More importantly, this provides a cross-modal knowledge transfer paradigm for multimodal pre-training of large models.We conducted experiments on VireoFood-172 and NUS-WIDE and found that large models trained on datasets such as COCO performed better on the similar domain dataset NUS-WIDE than the small domain dataset VireoFood-172; PMHANet effectively implements multimodal representation enhancement in downstream tasks based on a partially fine-tuned pre-trained large model to achieve SOTA performance on both datasets.
Fast Hyperspectral Image Dehazing With Dark-Object Subtraction Model

Haze in hyperspectral images is a common phenomenon, which severely degrades the quality of the acquired data and limits its applications. In this letter, a dark-object subtraction model is proposed for haze removal of hyperspectral images, which mainly is composed of three steps. First, a haze density map is estimated according to the haze characteristic in different spectral channels. Then, we design a saliency measure method to automatically calculate haze abundance of different channels. Finally, the haze-free image is obtained through solving the dark-object subtraction model. Experiments on real and simulated datasets demonstrate that our method consistently outperforms other state-of-the-art dehazing techniques in terms of reconstructed performance and computational cost.
Intensification of water supply and sanitation systems in the city of Nizhneudinsk

The paper proposes an approach to intensifying the operation of water supply and sanitation systems. The work was conducted using electronic modelling, calculation methods of hydraulic parameters and numerical modelling, as well as optimization of technical solutions for adjustment of operation modes, minimization of water leakage, pressure stabilization in the network, elimination of blockages and failures of regulating and stop valves. The efficacy of the proposed approach was demonstrated by the example of the city of Nizhneudinsk. As a result of the conducted research, information and computing systems and complexes were developed for simulating different modes of operation, assessing the performance, reliability and regime controllability of water supply and wastewater disposal systems, storing, analysing and accumulating information on the state of the system under analysis and its individual parameters. Such an approach served as a basis for developing a prospective scheme of development and reconstruction of the city water supply and sewage systems and provided an automated control system of its technological processes.
Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition

Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images. We propose an attention similarity knowledge distillation approach, which transfers attention maps obtained from a high resolution (HR) network as a teacher into an LR network as a student to boost LR recognition performance. Inspired by humans being able to approximate an object’s region from an LR image based on prior knowledge obtained from HR images, we designed the knowledge distillation loss using the cosine similarity to make the student network’s attention resemble the teacher network’s attention. Experiments on various LR face related benchmarks confirmed the proposed method generally improved recognition performances on LR settings, outperforming state-of-the-art results by simply transferring well-constructed attention maps. The code and pretrained models are publicly available in the https://github.com/gist-ailab/teaching-where-to-look .
ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-verified Image-Caption Associations for MS-COCO

Image-Text matching (ITM) is a common task for evaluating the quality of Vision and Language (VL) models. However, existing ITM benchmarks have a significant limitation. They have many missing correspondences, originating from the data construction process itself. For example, a caption is only matched with one image although the caption can be matched with other similar images and vice versa. To correct the massive false negatives, we construct the Extended COCO Validation (ECCV) Caption dataset by supplying the missing associations with machine and human annotators. We employ five state-of-the-art ITM models with diverse properties for our annotation process. Our dataset provides $$\times $$ 3.6 positive image-to-caption associations and $$\times $$ 8.5 caption-to-image associations compared to the original MS-COCO. We also propose to use an informative ranking-based metric mAP@R, rather than the popular Recall@K (R@K). We re-evaluate the existing 25 VL models on existing and proposed benchmarks. Our findings are that the existing benchmarks, such as COCO 1K R@K, COCO 5K R@K, CxC R@1 are highly correlated with each other, while the rankings change when we shift to the ECCV mAP@R. Lastly, we delve into the effect of the bias introduced by the choice of machine annotator. Source code and dataset are available at https://github.com/naver-ai/eccv-caption
Vision-Based Human Pose Estimation via Deep Learning: A Survey

Human pose estimation (HPE) has attracted a significant amount of attention from the computer vision community in the past decades. Moreover, HPE has been applied to various domains, such as human–computer interaction, sports analysis, and human tracking via images and videos. Recently, deep learning-based approaches have shown state-of-the-art performance in HPE-based applications. Although deep learning-based approaches have achieved remarkable performance in HPE, a comprehensive review of deep learning-based HPE methods remains lacking in literature. In this article, we provide an up-to-date and in-depth overview of the deep learning approaches in vision-based HPE. We summarize these methods of 2-D and 3-D HPE, and their applications, discuss the challenges and the research trends through bibliometrics, and provide insightful recommendations for future research. This article provides a meaningful overview as introductory material for beginners to deep learning-based HPE, as well as supplementary material for advanced researchers.
Classification of Facial Expressions Under Partial Occlusion for VR Games

Facial expressions are one of the most common way to ex- ternalize our emotions. However, the same emotion can have different effects on the same person and has different effects on different people. Based on this, we developed a system capable of detecting the facial expressions of a person in real-time, occluding the eyes (simulating the use of virtual reality glasses). To estimate the position of the eyes, in or- der to occlude them, Multi-task Cascade Convolutional Neural Networks (MTCNN) were used. A residual network, a VGG, and the combination of both models, were used to perform the classification of 7 different types of facial expressions (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral), classifying the occluded and non-occluded dataset. The combi- nation of both models, achieved an accuracy of 64.9% for the occlusion dataset and 62.8% for no occlusion, using the FER-2013 dataset. The primary goal of this work was to evaluate the influence of occlusion, and the results show that the majority of the classification is done with the mouth and chin. Nevertheless, the results were far from the state-of-the- art, which is expect to be improved, mainly by adjusting the MTCNN.