Reviewerly/exHarmony huggingface dataset:
OpenCBS: An Open-Source COBOL Defects Benchmark Suite

As the current COBOL workforce retires, entry-level developers are left to keep complex legacy systems maintained and operational. This creates a massive gap in knowledge and ability as companies are having their veteran developers replaced with a new, inexperienced workforce. Additionally, the lack of COBOL and mainframe technology in the current academic curriculum further increases the learning curve for this new generation of developers. These issues are becoming even more pressing due to the business-critical nature of these systems, which makes migrating or replacing the mainframe and COBOL unlikely anytime soon. As a result, there is now a huge need for tools and resources to increase new developers’ code comprehension and ability to perform routine tasks such as debugging and defect location. Extensive work has been done in the software engineering field on the creation of such resources. However, the proprietary nature of COBOL and mainframe systems has restricted the amount of work and the number of open-source tools available for this domain. To address this issue, our work leverages the publicly available technical forum data to build an open-source collection of COBOL programs embodying issues/defects faced by COBOL developers. These programs were reconstructed and organized in a benchmark suite to facilitate the testing of developer tools. Our goal is to provide an open-source COBOL benchmark and testing suite that encourage community contribution and serve as a resource for researchers and tool-smiths in this domain.
SoftCollage: A Differentiable Probabilistic Tree Generator for Image Collage

Image collage task aims to create an informative and visual-aesthetic visual summarization for an image collection. While several recent works exploit tree-based algorithm to preserve image content better, all of them resort to hand-crafted adjustment rules to optimize the collage tree structure, leading to the failure of fully exploring the structure space of collage tree. Our key idea is to soften the discrete tree structure space into a continuous probability space. We propose SoftCollage, a novel method that employs a neural-based differentiable probabilistic tree generator to produce the probability distribution of correlation-preserving collage tree conditioned on deep image feature, aspect ratio and canvas size. The differentiable characteristic allows us to formulate the tree-based collage generation as a differentiable process and directly exploit gradient to optimize the collage layout in the level of probability space in an end-to-end manner. To facilitate image collage research, we propose AIC, a large-scale public-available annotated dataset for image collage evaluation. Extensive experiments on the introduced dataset demonstrate the superior performance of the proposed method. Data and codes are available at https://github.com/ChineseYjh/SoftCollage.
AnnoteREI! A Tool for Transcribing and Annotating Requirements Elicitation Interviews

Requirements elicitation interviews are the most commonly used technique for requirements elicitation. Through interviewing subject matter experts, facilitating deep discussions, and documenting findings, requirement analysts can uncover the true needs of the system to satisfy stakeholders’ needs. However, during these interviews, the analysts often have to divide their attention to asking the right questions, react appropriately to the stakeholder’s responses, and keep track of the information flow throughout the interview. This is a complicated process, during which analysts could make mistakes and miss important information. For these reasons, it could be useful for them to review their interviews to understand what information has been elicited and what has been missed, what they would need to ask as follow up, and what mistakes they have done during the process. To support these processes, we propose AnnoteREI, a tool that allows analysts to upload the recording of their interviews and create an editable transcript with speaker diarization. The tool offers many functionalities for different purposes: (1) students and novices can use it to identify and tag their mistakes, thus improving their skills by learning from their experience; (2) analysts can annotate any part of the transcript and tag it for later review. For example, they can label a section as a requirement or as a topic that requires more investigation, or a name mentioned by the interviewee as another stakeholder; and (3) researchers can use the tagging system to analyze and collect new data.
Non-linear Optimization Methods for Learning Regular Distributions

Probabilistic finite automata (PFA) are recognizers of regular distributions over finite strings, a model that is widely applied in speech recognition and biological systems, for example. While the underlying structure of a PFA is just that of a normal automaton, it is well known that PFA with a non-deterministic underlying structure is more powerful than deterministic one. In this paper, we concentrate on passive learning non-deterministic PFA from examples and counterexamples using a two steps procedure: first we learn the underlying structure using an algorithm for learning the underlying residual finite state automaton, then we learn the probabilities of states and transitions using three different optimization methods. We experimentally show with a set of random probabilistic finite automata that the ones learned using RFSA combined with genetic algorithm for optimizing the weight outperforms other existing methods greatly improving the distance to the automaton to be learned. We also apply our algorithm to model the behavior of an agent in a maze. Also here RFSA algorithms have better performance than existing automata learning methods and can model both positive and negative samples well.
Sentiment Analysis of Bengali Textual Comments in Field of Sports Using Deep Learning Approach

In recent days, people are expressing their emotions, feelings or opinions on various social platforms. In those opinions some are real and some are fake. There are a lot of discussions about sports. When their team wins a match, they celebrate this highly but when a match loses, they criticize, bullying them. And then they express them angrily to different sites, like Facebook pages, Facebook groups etc. This issue may be resolved by using natural language processing (NLP) to analyze the sentiment of the relevant comments. Here we analyze sentiment in various sports related Bangla comments. We collected almost 4061 data from various Facebook pages and groups. After collecting those data, we classified them into five different categories: neutral, happy, sad, positive and negative. We use some preprocessing techniques like removing punctuation, data cleaning, manual validation to prepare our data. In this study, we used three different familiar deep learning models to predict sentiment of our dataset. Here our models are CNN, LSTM and BiLSTM. In these three models CNN with the glove word embedding performed better than other two models, and it is 94.57%. Finally, the CNN model outperforms other models in a way that captures the sentiment of the fans' remarks.
End-to-end 3D Human Pose Estimation with Transformer

Transformer based architectures have become the common choice in natural language processing and are now achieving SOTA performance in computer vision tasks such as image classification, object detection. However, the convolutional method still keeps SOTA performance in many approaches of 3D human pose estimation. Inspired by recent development in vision transformers, we design a heatmap-free structure using standard transformer architecture and learnable object queries to model the human joint relation within each frame and then output accurate joint positions and types, we also present a transformer based pose recognition architecture without any greedy algorithm to post-processing predicted bones during runtime. In the experiments, we achieve the best performance among methods that directly regress 3D joint position from a single RGB image, and report competitive results with many 2D to 3D Lifting approaches.
Adversarial Self-Training Unsupervised Domain Adaptation for Remote Sensing Scene Classification

In recent years, deep learning have achieved outstanding performance in field of remote sensing scenes classification. At the same time, most neural network models also exposed many shortcomings when dealing with different imaging conditions, insufficient sample size, etc. By analyzing the characteristics of source domain and target domain, reducing the gap between these domains is the main key for better recognition performance. In this paper, a novel adversarial self-training unsupervised domain adaptation (AST) framework is proposed to deal with the domain migration issues. To enforcing the gap reducing and the alignment of feature distribution of domains, we first implement the domain-adversarial learning mechanism. And then the self-training modules is also applied for better decision boundary generation. The experimental results show that our proposed method outperforms state of art in remote sensing scene classification.
Keyword-guided Topic-oriented Conversational Recommender System

Conversational recommender system (CRS) allows agent to understand the conversation with user and give recommendations after multi-turn dialogues. However, there are still two limitations in existing CRS: (1) improper words or items may be chosen for the given topic in the generation, and (2) the contextual information of items in the recommendation is not rationally explored. To solve these issues, we proposed a Keyword-guided Topic-oriented CRS model (KGTO), which captures more accurate topic by extracting keywords through the hierarchical attention mechanism, and enriches the contextual information of items by fusing the co-occurrence graph with the knowledge graph. Moreover, a generative module can select words or items supplemented topic information to generate proper responses. Extensive experiments on the task-oriented dialogue dataset prove that our model performs well in recommendation effectiveness and dialogue informativeness.
An End-to-End Contrastive Self-Supervised Learning Framework for Language Understanding

Abstract Self-supervised learning (SSL) methods such as Word2vec, BERT, and GPT have shown great effectiveness in language understanding. Contrastive learning, as a recent SSL approach, has attracted increasing attention in NLP. Contrastive learning learns data representations by predicting whether two augmented data instances are generated from the same original data example. Previous contrastive learning methods perform data augmentation and contrastive learning separately. As a result, the augmented data may not be optimal for contrastive learning. To address this problem, we propose a four-level optimization framework that performs data augmentation and contrastive learning end-to-end, to enable the augmented data to be tailored to the contrastive learning task. This framework consists of four learning stages, including training machine translation models for sentence augmentation, pretraining a text encoder using contrastive learning, finetuning a text classification model, and updating weights of translation data by minimizing the validation loss of the classification model, which are performed in a unified way. Experiments on datasets in the GLUE benchmark (Wang et al., 2018a) and on datasets used in Gururangan et al. (2020) demonstrate the effectiveness of our method.
A novel review of potential assessment tools for Para Dressage Classification

The key aim of classification in Para sport is to try and ensure that competitors are grouped so that they compete against others with impairments that cause a similar level of activity limitation within a specific sport.This synthesis aimed to identify existing, valid, and reliable, impairment assessment tools to measure eligible impairments that influence an individual's ability to compete at Para dressage.A multi-stage approach was employed, where a systematic search of professional databases of impairment assessment tools first identified 35 tools for Para dressage.Then, a search strategy was developed, based on these 35 tools, and 305 scientific articles were identified from academic databases up to September 2021.From here, impairment assessment tools were evaluated and refined in a twostage process using known performance determinants for Para dressage and then an assessment of their reliability, validity and practical usability.This resulted in the selection of impairment assessment tools, which included measures of muscle tone, strength, coordination, sitting balance, and trunk function.From this synthesis, we present a novel process by which impairment assessment tools were selected, refined, and critically examined using knowledge of performance determinants for dressage, the views and experiences of stakeholders, and reliability and validity of tools.The processes described here could be applied to the development of evidence-based classification systems across a range of Para sports.
Optimality of Huffman Code in the Class of 1-Bit Delay Decodable Codes

For a given independent and identically distributed (i.i.d.) source, Huffman code achieves the optimal average codeword length in the class of instantaneous codes with a single code table. However, it is known that there exist time-variant encoders, which achieve a shorter average codeword length than the Huffman code, using multiple code tables and allowing at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -bit decoding delay for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k = 2, 3, 4, \ldots $ </tex-math></inline-formula> . On the other hand, it is not known whether there exists a 1-bit delay decodable code, which achieves a shorter average length than the Huffman code. This paper proves that for a given i.i.d. source, a Huffman code achieves the optimal average codeword length in the class of 1-bit delay decodable codes with a finite number of code tables.
Spectral Bounds for Graph Echo State Network Stability

Graph echo state networks (GESN) are a class of reservoir computing models for the efficient and effective processing of graphs. They compute graph embeddings by the convergence to a fixed point of a dynamical system, randomly initialized according to a generalization of the echo state property, called the graph embedding stability (GES) property. In this paper, we prove new and more accurate bounds for necessary and sufficient GES conditions. Experiments demonstrate how these bounds allow an easier parameter selection and better quality reservoirs.
Reusable Contracts for Safe Integration of Reinforcement Learning in Hybrid Systems

Deductive verification is a powerful approach for establishing crucial safety properties of intelligent hybrid systems. However, deductive verification requires abstract formal descriptions, e.g. properties, contracts, and invariants. Defining these requires a high level of expertise and an enormous amount of manual effort, in particular if the system contains intelligent components such as reinforcement learning agents. In this paper, we propose reusable contract patterns for the safe integration of reinforcement learning in hybrid systems. Our key ideas are threefold: First, we identify recurring verification problems for intelligent hybrid systems that contain reinforcement learning agents. Second, we provide a set of contract patterns that ease the definition of contracts for the safe integration of reinforcement learning agents. Third, we indicate how to derive invariants from the contract patterns. Our contract patterns together with the invariant derivation enable systematic reuse of manually defined hybrid contracts and invariants and reduce the manual effort of the deductive verification process for intelligent hybrid systems.
DLB: A Dynamic Load Balance Strategy for Distributed Training of Deep Neural Networks

Synchronous strategies with data parallelism are widely utilized in distributed training of Deep Neural Networks (DNNs), largely owing to their easy implementation yet promising performance. In these strategies, the workers with different computational capabilities need to wait for each other because of the essential gradient or weight synchronization. This will inevitably cause the high-performance workers to waste time waiting for the weak computational workers, which in turn results in the inefficiency of the cluster. In this paper, we propose a Dynamic Load Balance (DLB) strategy for the distributed training of DNNs to tackle this issue. Specifically, the performance of each worker is evaluated first based on the performance demonstration during the previous training epochs, and then the batch size and dataset partition are adaptively adjusted in consideration of the current performance of the workers. As a result, the waiting cost among the workers will be eliminated, thereby the utilization of the clusters is highly improved. Furthermore, the essential theoretical analysis has also been provided to justify the convergence of the proposed algorithm. Extensive experiments have been conducted on the CIFAR10 and CIFAR100 benchmark datasets with four state-of-the-art DNN models. The experimental results indicate that the proposed algorithm can significantly improve the utilization of the distributed cluster. In addition, the proposed algorithm can also prevent the load imbalance of the distributed DNN training from being affected by the disturbance and can be employed flexibly in conjunction with the other synchronous distributed DNN training methods.
Selecting Optimal Trace Clustering Pipelines with Meta-learning

Trace clustering has been extensively used to discover aspects of the data from event logs. Process Mining techniques guide the identification of sub-logs by grouping traces with similar behaviors, producing more understandable models and improving conformance indicators. Nevertheless, little attention has been posed to the relationship among event log properties, the pipeline of encoding and clustering algorithms, and the quality of the obtained outcome. The present study contributes to the understanding of the aforementioned relationships and provides an automatic selection of a proper combination of algorithms for clustering a given event log. We propose a Meta-Learning framework to recommend the most suitable pipeline for trace clustering, which encompasses the encoding method, clustering algorithm, and its hyperparameters. Our experiments were conducted using a thousand event logs, four encoding techniques, and three clustering methods. Results indicate that our framework sheds light on the trace clustering problem and can assist users in choosing the best pipeline considering their environment.
Anime Character Recognition using Intermediate Features Aggregation

In this work we study the problem of anime character recognition. Anime, refers to animation produced within Japan and work derived or inspired from it. We propose a novel Intermediate Features Aggregation classification head, which helps smooth the optimization landscape of Vision Transformers (ViTs) by adding skip connections between intermediate layers and the classification head, thereby improving relative classification accuracy by up to 28%. The proposed model, named as Animesion, is the first end-to-end framework for large-scale anime character recognition. We conduct extensive experiments using a variety of classification models, including CNNs and self-attention based ViTs. We also adapt its multimodal variation Vision-Language Transformer (ViLT), to incorporate external tag data for classification, without additional multimodal pre-training. Through our results we obtain new insights into the effects of how hyperparameters such as input sequence length, mini-batch size, and variations on the architecture, affect the transfer learning performance of Vi(L)Ts.
A Deep Retinex Framework for Light Field Restoration under Low-light Conditions

Light field (LF) images can record the scene from multiple directions and have many applications, such as refocusing and depth estimation. However, these applications can be heavily influenced by poor light condition and noise. This work aims to recover the high-quality LF images from their lowlight detection. First, a decomposition network is employed to decompose each LF image into its reflectance and illumination with the Retinex theory. Then, two enhancement networks are designed to denoise the reflectance and enhance the illumination, respectively. They adopt alternate spatial-angular feature extractions and process all the views synchronously with high efficiency. A parallel dual attention mechanism is integrated to both the spatial and angular feature extractions, to encode more important information. Moreover, a discriminator is introduced during the training to generate more realistic LF images by making judgment according to both the spatial and angular characteristics. Experimental results have demonstrated the superior performance of our method, which can restore the content, luminance, color and geometric structures of LF images effectively.
Traffic Police Gestures Recognition Based on Graph Convolution with Height Layering Partitioning Strategy

Aiming at the needs for auto-driving vehicles to recognize continuous traffic police gestures, a graph convolutional traffic police gesture recognizer with the height layering partitioning strategy is proposed. Firstly, according to the natural, assist, self-connection spatial relationships and temporal associations between human parts, a spatial-temporal traffic gesture model is established. Secondly, the spatial-temporal convolutional network is introduced, and the height layering partitioning strategy is proposed, which uses the relative height differences of parts as labels and breaks the limitations of existing partitioning strategies on graph structure. Finally, the spatial mean layer output structure which retains the length of the temporal dimension is designed to adapt the many-to-many prediction pattern for recognizing continuous traffic police gestures. The experiments show that the proposed method significantly improves the recognizing performance, the confusion rate between gestures is 0.1% and the Jaccard index surpasses comparison methods.
Detection of data drift in a two-dimensional stream using the Kolmogorov-Smirnov test

In recent years, there has been an increasing amount of streaming information coming from time series. Learning from data appearing in real time is quite a call, due in part to the speed at which new data appears. Hidden data changes that are not previously known to learning algorithms are referred to in the literature as data or concept drift. In classical machine learning, a classifier analyzes new data using past training instances of the data stream. However, the accuracy of the classifier deteriorates due to data drift, which occurs in non-stationary data. In such situations, the classifier must detect a significant change in the data adapt its prediction over time. The motivation of this paper is to show a method for drift detection without knowledge of instance labels. Labels are sometimes not available or periodically missing, making it difficult to apply methods where knowledge of them is required.