Reviewerly/exHarmony huggingface dataset:
Decoding Strategies for Code Conciseness and Efficiency in Transformer-Generated Programs

In recent years, tremendous strides have been made in the area of program synthesis due to the leveraging of highly parameterized transformer models. Such models have demonstrated near human levels of performance on tasks such as bug detection, computer language translation, and competitive programming. Unfortunately, little research has been done in the exploration of decoding methodologies for such models, despite the semantic and structural differences between human and programming languages. In this paper, we propose extensions to commonly used decoding strategies, which incorporate additional constraints on non-concise and inefficient program generations. Our approaches have shown comparable performance on program generation tasks while producing programs requiring fewer lines of code and a reduced number of looping operations on average compared to traditional methods of decoding.
Document Knowledge Transfer for Aspect-Based Sentiment Classification Using a Left-Center-Right Separated Neural Network with Rotatory Attention

Hybrid Aspect-Based Sentiment Classification (ABSC) methods make use of domain-specific, costly ontologies to make up for the lack of available aspect-level data. This paper proposes two forms of transfer learning to exploit the plenteous amount of available document data for sentiment classification. Specifically, two forms of document knowledge transfer, pretraining (PRET) and multi-task learning (MULT), are considered in various combinations to extend the state-of-the-art LCR-Rot-hop++ model. For both the SemEval 2015 and 2016 datasets, we find an improvement over the LCR-Rot-hop++ neural model. Overall, the pure MULT model performs well across both datasets. Additionally, there is an optimal amount of document knowledge that can be injected, after which the performance deteriorates due to the extra focus on the auxiliary task. We observe that with transfer learning and L1 and L2 loss regularisation, the LCR-Rot-hop++ model is able to outperform the HAABSA++ hybrid model on the (larger) SemEval 2016 dataset. Thus, we conclude that transfer learning is a feasible and computationally cheap substitute for the ontology step of hybrid ABSC models.
Effective Information Retrieval, Question Answering and Abstractive Summarization on Large-Scale Biomedical Document Corpora

During the COVID-19 pandemic, a concentrated effort was made to collate published literature on SARS-Cov-2 and other coronaviruses for the benefit of the medical community. One such initiative is the COVID-19 Open Research Dataset which contains over 400,000 published research articles. To expedite access to relevant information sources for health workers and researchers, it is vital to design effective information retrieval and information extraction systems. In this article, an IR approach leveraging transformer-based models to enable question-answering and abstractive summarization is presented. Various keyword-based and neural-network-based models are experimented with and incorporated to reduce the search space and determine relevant sentences from the vast corpus for ranked retrieval. For abstractive summarization, candidate sentences are determined using a combination of various standard scoring metrics. Finally, the summary and the user query are utilized for supporting question answering. The proposed model is evaluated based on standard metrics on the standard CovidQA dataset for both natural language and keyword queries. The proposed approach achieved promising performance for both query classes, while outperforming various unsupervised baselines.
GRAM: Grammar-Based Refined-Label Representing Mechanism in the Hierarchical Semantic Parsing Task

In this study, we proposed an efficient method to improve the performance of the hierarchical semantic parsing task by strengthening the meaning representation of the label candidate set via inductive grammar. In particular, grammar was first synthesized from the logical representations of training annotated data. Then, the model utilizes it as additional structured information for all expression label predictions. The grammar was also used to prevent unpromising directions in the semantic parsing process dynamically. The experimental results on the three well-known semantic parsing datasets, TOP, TOPv2 (low-resource settings), and ATIS, showed that our proposed method work effectiveness, which achieved new state-of-the-art (SOTA) results on TOP and TOPv2 datasets, and competitive results on the ATIS dataset.
Novel Benchmark Data Set for Automatic Error Detection and Correction

The present paper introduces a novel benchmark data set for automatic error detection as well as error correction in text documents based on language models or other techniques. The data set contains a large number of sentences from various domains annotated with various types of errors (orthographic, grammatical, punctuation, and typography errors). The paper presents the method used to collect and annotate the documents, provides statistical analyses of the data set’s properties and evaluates two preliminary baseline models for automatic error detection on a specific benchmark task. The results show, on the one hand, the effectiveness of the proposed data set for the evaluation of automatic error detection systems. On the other hand, these initial analyses also reveal that the data set contains challenging cases that are difficult to detect. Finally, the paper discusses potential applications of the proposed data set in the development and research of error detection and error correction systems.
Semantic-Aware Contrastive Learning for Multi-Object Medical Image Segmentation

Medical image segmentation, or computing voxel-wise semantic masks, is a fundamental yet challenging task in medical imaging domain. To increase the ability of encoder-decoder neural networks to perform this task across large clinical cohorts, contrastive learning provides an opportunity to stabilize model initialization and enhances downstream tasks performance without ground-truth voxel-wise labels. However, multiple target objects with different semantic meanings and contrast level may exist in a single image, which poses a problem for adapting traditional contrastive learning methods from prevalent "image-level classification" to "pixel-level segmentation". In this article, we propose a simple semantic-aware contrastive learning approach leveraging attention masks and image-wise labels to advance multi-object semantic segmentation. Briefly, we embed different semantic objects to different clusters rather than the traditional image-level embeddings. We evaluate our proposed method on a multi-organ medical image segmentation task with both in-house data and MICCAI Challenge 2015 BTCV datasets. Compared with current state-of-the-art training strategies, our proposed pipeline yields a substantial improvement of 5.53% and 6.09% on Dice score for both medical image segmentation cohorts respectively (p-value 0.01). The performance of the proposed method is further assessed on external medical image cohort via MICCAI Challenge FLARE 2021 dataset, and achieves a substantial improvement from Dice 0.922 to 0.933 (p-value 0.01).
Adaptive pair-weight vector projection classifier for semi-supervised classification

Manifold regularization is a popular technology based on graph theory and has been widely applied to nonparallel hyperplane classifiers for semi-supervised learning. However, these classifiers adopt the fixed similarity matrix that may be sensitive to noises and outliers. Thus, we propose a novel nonparallel hyperplane classifier, named the adaptive pair-weight vector projection (APVP) classifier, for semi-supervised classification tasks. The proposed APVP unifies projection learning and graph construction into a general framework. For projection learning, APVP achieves two projection vectors by solving the pair-wise optimization problems in which we need to simultaneously maximize the between-class scatter of data and minimize both the within-class scatter of data and the adaptive manifold regularization. For graph construction, the adaptive similarity matrix is constructed during the procedure of optimization. Thus, APVP can weaken the effect of noises and outliers by the adaptive similarity matrix and strengthen the discriminant capability by the Fisher criterion. To classify unseen data, we also design a new classification rule. Experimental results show that APVP has good performance in semi-supervised scene.
CIPS-3D++: End-to-End Real-Time High-Resolution 3D-Aware GANs for GAN Inversion and Stylization

Style-based GANs achieve state-of-the-art results for generating high-quality images, but lack explicit and precise control over camera poses. Recently proposed NeRF-based GANs have made great progress towards 3D-aware image generation. However, the methods either rely on convolution operators which are not rotationally invariant, or utilize complex yet suboptimal training procedures to integrate both NeRF and CNN sub-structures, yielding un-robust, low-quality images with a large computational burden. This article presents an upgraded version called CIPS-3D++, aiming at high-robust, high-resolution and high-efficiency 3D-aware GANs. On the one hand, our basic model CIPS-3D, encapsulated in a style-based architecture, features a shallow NeRF-based 3D shape encoder as well as a deep MLP-based 2D image decoder, achieving robust image generation/editing with rotation-invariance. On the other hand, our proposed CIPS-3D++, inheriting the rotational invariance of CIPS-3D, together with geometric regularization and upsampling operations, encourages high-resolution high-quality image generation/editing with great computational efficiency. Trained on raw single-view images, without any bells and whistles, CIPS-3D++ sets new records for 3D-aware image synthesis, with an impressive FID of 3.2 on FFHQ at the 1024×1024 resolution. In the meantime, CIPS-3D++ runs efficiently and enjoys a low GPU memory footprint so that it can be trained end-to-end on high-resolution images directly, in contrast to previous alternate/progressive methods. Based on the infrastructure of CIPS-3D++, we propose a 3D-aware GAN inversion algorithm named FlipInversion, which can reconstruct the 3D object from a single-view image. We also provide a 3D-aware stylization method for real images based on CIPS-3D++ and FlipInversion. In addition, we analyze the problem of mirror symmetry suffered in training, and solve it by introducing an auxiliary discriminator for the NeRF network. Overall, CIPS-3D++ provides a strong base model that can serve as a testbed for transferring GAN-based image editing methods from 2D to 3D.
Self-Distillation with the New Paradigm in Multi-Task Learning

We tackle the problem of multi-task learningMulti-Task Learning (MTL) (MTL) for solving the correlated visual dense prediction tasks using monocular image source. With further restrictions on the model parameters, the soft-shared MTL Convnets (CNN) feature distinct models for each individual task. Hard-sharingHard-sharing-based models, on the other hand, have shared encoders but individual decoders for every task. MTLMulti-Task Learning (MTL) models have demonstrated satisfactory performances for pixel-wise dense prediction tasks like semantic segmentation, surface-normal estimation, and depth estimation from the monocular inputs. In general, they impose two inherent drawbacks: (1) such models have constraints with no leverage on the inter-task knowledge, which hinders the performance boost while jointly trained, and (2) explicitly optimization of each task-specific network is required. Incorporating the abovementioned issues into soft and hard-sharingHard-sharing-based MTL models can immensely enhance MTLMulti-Task Learning (MTL) performance. To that end, we present SD-MTCNN, hard-sharing-based and S3DMT-Net, soft-sharingSoft-sharing-based novel MTL networks. Here, we follow tries to inherit characteristics from deeper CNNConvolutional Neural Network (CNN) layers/feature maps into shallower CNN layers, hence helps in increasing the network's bandwidth. We also utilize the notion of sharing the features of the task-specific encoders, where the task-specific encoder’s feature-maps are communicated amongst each other, aka as cross-task interactions. We examine our self-distilled MTLsMulti-Task Learning (MTL) performance on two different types of visual scenes: Urban (CityScapes and ISPRS) and indoor (NYUv2 and Mini-Taskonomy), and we observe notable gains in all tasks as compared to the referred methodologies.
STRE: An Automated Approach to Suggesting App Developers When to Stop Reading Reviews

It is well known that user feedback (i.e., reviews) plays an essential role in mobile app maintenance. Users upload their troubles, app issues, or praises, to help developers refine their apps. However, reading tremendous amounts of reviews to retrieve useful information is a challenging job. According to our manual studies, reviews are full of repetitive opinions, thus developers could stop reading reviews when no more new helpful information appears. Developers can extract useful information from partial reviews to ameliorate their app and then develop a new version. However, it is tough to have a good trade-off between getting enough useful feedback and saving more time. In this paper, we propose a novel approach, named STRE, which utilizes historical reviews to suggest the time when most of the useful information appears in reviews of a certain version. We evaluate STRE on 62 recent versions of five apps from Apple's App Store. Study results demonstrate that our approach can help developers save their time by up to 98.33% and reserve enough useful reviews before stopping to read reviews such that developers do not spend additional time in reading redundant reviews over the suggested stopping time. At the same time, STRE can complement existing review categorization approaches that categorize reviews to further assist developers. In addition, we find that the missed top-word-related reviews appearing after the suggested stopping time contain limited useful information for developers. Finally, we find that 12 out of 13 of the emerging bugs from the studied versions appear before the suggested stopping time. Our approach demonstrates the value of automatically refining information from reviews.
Blind Recognition of Reed-Solomon Codes Based on Galois Field Fourier Transform and Reliability Verification

The Galois field Fourier transform (GFFT) method has been widely applied in the blind recognition of Reed-Solomon (RS) codes in recent years. However, as the code length increases, the computational complexity increases rapidly. Also, blind recognition in a poor channel environment remains challenging. To solve the above challenges, a blind recognition algorithm is proposed based on GFFT and reliability verification (RV). Low-complexity GFFT (LC-GFFT) is proposed to calculate the shortened spectrums, reducing the computational complexity and improving the correct codeword utilization rate (CUR). Next, the criteria for parameter estimation (PE) are given. The RV module is designed to verify the correctness of the estimated parameters. The simulations demonstrate that our algorithm can recognize RS(15, 11, 2, 19) and RS(31, 15, 8, 37) with over 95% correct rate when SNR is above 0.8dB and 2.5dB, achieving at least 7dB of signal-to-noise (SNR) defined gain over the existing algorithms. Moreover, the recognition time of our method is reduced by 74.8% compared to that of the GFFT-based method for RS(31, 15, 8, 37) when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N = 1 \times 10^{5}$ </tex-math></inline-formula> .
Graph Neural Network Meets Sparse Representation: Graph Sparse Neural Networks Via Exclusive Group Lasso

Existing GNNs usually conduct the layer-wise message propagation via the 'full' aggregation of all neighborhood information which are usually sensitive to the structural noises existed in the graphs, such as incorrect or undesired redundant edge connections. To overcome this issue, we propose to exploit Sparse Representation (SR) theory into GNNs and propose Graph Sparse Neural Networks (GSNNs) which conduct sparse aggregation to select reliable neighbors for message aggregation. GSNNs problem contains discrete/sparse constraint which is difficult to be optimized. Thus, we then develop a tight continuous relaxation model Exclusive Group Lasso GNNs (EGLassoGNNs) for GSNNs. An effective algorithm is derived to optimize the proposed EGLassoGNNs model. Experimental results on several benchmark datasets demonstrate the better performance and robustness of the proposed EGLassoGNNs model.
Genomic Research in Reproduction and Biobanking: An Analysis of International Legal Approaches

The article examines the regulatory issues of genomic research in human reproduction and biobanking. The approaches to legal regulation at the level of international law, integration organizations, and individual states are analyzed. Based on the analysis, proposals for legal regulation are formulated. Regarding the legal regulation of genomic research in the field of biobanking, the article discusses the issue of the legal standing of biobanks, approaches to the legal regulation of biobanks in various countries, and formulates proposals for the commercial use of the results of genomic research in biobanking. The article also provides an overview of some judicial decisions that had a certain impact on forming legal regulation of genomic research in the field of biobanking and human reproduction.
Process Optimization in Small Software Firms

Process optimization is of paramount importance for small software firms to achieve enhanced efficiency, productivity, and competitiveness. This abstract provides a concise overview of the concept of process optimization in small software firms. It highlights the challenges faced by these organizations, such as limited resources, lack of standardized processes, time constraints, and resistance to change. The abstract also presents strategies for process optimization, including process mapping, automation and technology adoption, agile methodologies, and training. Best practices, such as clear process documentation, establishing key performance indicators (KPIs), cross-functional collaboration, and continuous monitoring, are discussed. By implementing these strategies and best practices, small software firms can optimize their processes and drive successful outcomes. Process optimization empowers small software firms to streamline their workflows, deliver high-quality software solutions, and achieve sustainable growth in a competitive industry.
One-way multimodal image-to-image translation for heterogeneous face recognition

Cross-domain face matching is known as heterogeneous face recognition (HFR), which is more valuable than traditional face recognition and plays a key role in public safety. It remains a challenging problem due to insufficient heterogeneous data and large domain discrepancies. Recent work has focused on learning common features from different visual domains and synthesizing face images from other domains into the visible domain to reduce domain discrepancy. The former performs domain transformation during face recognition, which requires additional runtime. The latter has poorer performance limited by insufficient heterogeneous data. We propose a one-way multimodal image-to-image translation (OMIT) to tackle the problem of insufficient heterogeneous data. Specifically, we learn the generation of heterogeneous data from small-scale paired heterogeneous training data, and an identity-preserving loss is imposed on the generated heterogeneous images to ensure their identity consistency. Then, we transform large-scale visible data of abundant identity information into paired heterogeneous data, which addresses the lack of identity diversity due to small-scale heterogeneous data. The generated pairwise heterogeneous data can be directly used to train the HFR network to improve recognition accuracy and precision. Finally, our method exhibits generated quality improvements on BUAA, CASIA NIR–VIS 2.0, Oulu NIR–VIS, and Tufts face database. Heterogeneous datasets generated by OMIT improve the performance of HFR networks.
Image Clustering and Feature Extraction by Utilizing an Improvised Unsupervised Learning Approach

Abstract The need for information is gradually shifting from text to images due to the technology’s growth and increase in digital images. It is quite challenging for people to find similar color images. To obtain similarity matching, the color of the image needs to be identified. This paper aims at various clustering techniques to identify the color of the digital image. Though many clustering techniques exist, this paper focuses on Fuzzy c-Means, Mean-Shift, and a hybrid technique that amalgamates the agglomerative hierarchies and k-Means, known as hKmeans to cluster the intensity of the image. Applying evaluation metrics of Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Homogeneity, Completeness, V-Score, and Peak signal-to-noise ratio it is proven that the results obtained demonstrate the good performance of the proposed technique. Then the color histogram is applied to identify the color and differentiate the color distribution on the original and clustered image.
Learned Disentangled Latent Representations for Scalable Image Coding for Humans and Machines

As an increasing amount of image and video content will be analyzed by machines, there is demand for a new codec paradigm that is capable of compressing visual input primarily for the purpose of computer vision inference, while secondarily supporting input reconstruction. In this work, we propose a learned compression architecture that can be used to build such a codec. We introduce a novel variational formulation that explicitly takes feature data relevant to the desired inference task as input at the encoder side. As such, our learned scalable image codec encodes and transmits two disentangled latent representations for object detection and input reconstruction. We note that compared to relevant benchmarks, our proposed scheme yields a more compact latent representation that is specialized for the inference task. Our experiments show that our proposed system achieves a bit rate savings of 40.6% on the primary object detection task compared to the current state-of-the-art, albeit with some degradation in performance for the secondary input reconstruction task.
The Use of Machine Learning in Libraries

This chapter focuses on machine learning algorithms used in real-world applications and the possibilities of using them in libraries. Recommender systems are considered to be very valuable tools as they can be personalized to each user's preferences and needs. The main goal of the recommendation system is to aid users in finding necessary items from an overwhelming number of options. In this chapter, the authors examine two book recommender systems, explain the logic behind the used algorithms, and illustrate the way to adapt the same technologies in other libraries.