Reviewerly/exHarmony huggingface dataset:
CODEP: Grammatical Seq2Seq Model for General-Purpose Code Generation

General-purpose code generation aims to automatically convert the natural language description to code snippets in a general-purpose programming language (GPL) such as Python. In the process of code generation, it is essential to guarantee the generated code satisfies grammar constraints of GPL. However, existing sequence-to-sequence (Seq2Seq) approaches neglect grammar rules when generating GPL code. In this paper, we devise a pushdown automaton (PDA)-based methodology to make the first attempt to consider grammatical Seq2Seq models for general-purpose code generation, exploiting the principle that PL is a subset of PDA recognizable language and code accepted by PDA is grammatical. Specifically, we construct a PDA module and design an algorithm to constrain the generation of Seq2Seq models to ensure grammatical correctness. Guided by this methodology, we further propose CODEP, a code generation framework equipped with a PDA module, to integrate the deduction of PDA into deep learning. This framework leverages the state of PDA deduction (including state representation, state prediction task, and joint prediction with state) to assist models in learning PDA deduction. To comprehensively evaluate CODEP, we construct a PDA for Python and conduct extensive experiments on four public benchmark datasets. CODEP can employ existing sequence-based models as base models, and we show that it achieves 100% grammatical correctness percentage on these benchmark datasets. Consequently, CODEP relatively improves 17% CodeBLEU on CONALA, 8% EM on DJANGO, and 15% CodeBLEU on JUICE-10K compared to base models. Moreover, PDA module also achieves significant improvements on the pre-trained models.
DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization

The deep learning (DL) compiler serves as a vital infrastructure component to enable the deployment of deep neural networks on diverse hardware platforms such as mobile devices and Raspberry Pi. DL compiler's primary function is to translate DNN programs written in high-level DL frameworks such as PyTorch and TensorFlow into portable executables. These executables can then be flexibly executed by the deployed host programs. However, existing DL compilers rely on a tracing mechanism, which involves feeding a runtime input to a neural network program and tracing the program execution paths to generate the computational graph necessary for compilation. Unfortunately, this mechanism falls short when dealing with modern dynamic neural networks (DyNNs) that possess varying computational graphs depending on the inputs. Consequently, conventional DL compilers struggle to accurately compile DyNNs into executable code. To address this limitation, we propose DyCL, a general approach that enables any existing DL compiler to successfully compile DyNNs. DyCL tackles the dynamic nature of DyNNs by introducing a compilation mechanism that redistributes the control and data flow of the original DNN programs during the compilation process. Specifically, DyCL develops program analysis and program transformation techniques to convert a dynamic neural network into multiple sub-neural networks. Each sub-neural network is devoid of conditional statements and is compiled independently. Furthermore, DyCL synthesizes a host module that models the control flow of the DyNNs and facilitates the invocation of the sub-neural networks. Our evaluation demonstrates the effectiveness of DyCL, achieving a 100% success rate in compiling all dynamic neural networks. Moreover, the compiled executables generated by DyCL exhibit significantly improved performance, running between 1.12× and 20.21× faster than the original DyNNs executed on general-purpose DL frameworks.
PhysCov: Physical Test Coverage for Autonomous Vehicles

Adequately exercising the behaviors of autonomous vehicles is fundamental to their validation. However, quantifying an autonomous vehicle's testing adequacy is challenging as the system's behavior is influenced both by its state as well as its physical environment. To address this challenge, our work builds on two insights. First, data sensed by an autonomous vehicle provides a unique spatial signature of the physical environment inputs. Second, given the vehicle's current state, inputs residing outside the autonomous vehicle's physically reachable regions are less relevant to its behavior. Building on those insights, we introduce an abstraction that enables the computation of a physical environment-state coverage metric, PhysCov. The abstraction combines the sensor readings with a physical reachability analysis based on the vehicle's state and dynamics to determine the region of the environment that may affect the autonomous vehicle. It then characterizes that region through a parameterizable geometric approximation that can trade quality for cost. Tests with the same characterizations are deemed to have had similar internal states and exposed to similar environments and thus likely to exercise the same set of behaviors, while tests with distinct characterizations will increase PhysCov. A study on two simulated and one real system's dataset examines PhysCovs's ability to quantify an autonomous vehicle's test suite, showcases its characterization cost and precision, investigates its correlation with failures found and potential for test selection, and assesses its ability to distinguish among real-world scenarios.
RINX: A system for information and knowledge extraction from resumes

A resume is a detailed source of information about the candidate which summarizes the personal details, education, career history, project experience, certifications, trainings, awards, and any other achievements. For large organizations or job portals which receive thousands of resumes for recruitment or profile creation, it is not possible to manually go through each resume and identify the important information. Hence, there is a need for a system that automatically extracts the information of interest from the resumes. Such automatic extraction of information from resumes is very challenging because resumes are unstructured documents with a wide range of variations in terms of format, style, and contents. In this paper, we describe RINX (Resume INformation eXtraction) which is an end-to-end system for automatic extraction of information from resumes. RINX heavily utilizes traditional approaches like linguistic patterns and gazettes for information extraction. RINX also complements these traditional approaches with state-of-the-art machine learning and deep learning based techniques. We further describe a few knowledge extraction techniques as well as several real-life use-cases based on the information extracted from a large repository of resumes.
A Patient’s Wish List for Imaging Artificial Intelligence (and Radiology)

As a patient, I would like any radiologic procedure to include several elements: •selection and appropriate use of the optimal imaging for the clinical issue; •an accurate reading and analysis of the results by a radiologist, who communicates the results of the reading clearly and promptly to the referring physician, including any recommendations for further imaging or analysis; and •a clear communication of the results and any recommendations for further imaging, tests, and treatments to the patient in a form that the patient can understand and thus play an active role in all decisions about any next steps.
Multimedia analysis of robustly optimized multimodal transformer based on vision and language co-learning

Recently, research on multimodal learning using all modality information has been conducted to detect disinformation on multimedia. Existing multimodal learning methods include score-level fusion approaches combining different models, and feature-level fusion methods combining embedding vectors to integrate data of different dimensions. Because a late-level fusion method is combined after the modalities are individually operated, there is a limit in that the recognition performance of a unimodal determines the performance. In addition, a fusion method has constraints in that the data among the modalities must be matched. In this study, we propose a classification system using a RoBERTa-based multimodal fusion transformer (RoBERTaMFT) that applies a co-learning method to solve the limitations of the recognition performance of multimodal learning as well as the data imbalance among the modalities. RoBERTaMFT consists of image feature extraction, co-learning using the reconstruction of image features with text embedding, and a late-level fusion step applied to the final classification. As experiment results using the CrisisMMD dataset indicate, RoBERTaMFT achieved an accuracy 21.2% and an f1-score 0.414 higher than those of unimodal learning, and an accuracy 11.7% and an f1-score 0.268 higher than those of existing multimodal learning.
Empirical Analysis with Component Decomposition Methods for Cervical Cancer Risk Assessment

Cervical cancer is one of the most common female diseases. This happens when a woman’s cervix changes. To make matters worse, these cancer cells have the potential to spread to other organs like the liver, bladder, rectum, and even the lungs.Cervical cancer has a high mortality rate, especially in low and middle-income countries, and is the fourth most frequent cancer in women worldwide, according to the World Health Organisation. Medical datasets, including those for diabetes, cervical cancer, and liver illness, are becoming increasingly important as the field of computer science and information technology develops. Cervical cancer risk assessment makes use of a wide variety of classification methods, including but not limited to few ML algorithms. Due to the imbalance and high number of missing values, the dataset underwent a rigorous data pre-processing phase. To further improve classification accuracy, we have proposed component decomposition techniques in our research. Our proposed classifiers, the Support Vector Classifier and the Logistic Regression, have an accuracy of 99.40%.
Sentiment Analysis with Novel GRU based Deep Learning Networks

Consumers make decisions online on diverse websites based on recommendations from people they have never met. This shared online input provides thus insight into the way they perceive things such as products, services, or events, can be beneficial to some degree to the users who are looking to make decisions. Sentiment identification is an ongoing research topic that has successfully shown results for movie and product reviews, blogs, social media post. As users, we shop for the best fit. Text classification, a subcategory of natural language processing, is the task of categorizing text to represent the word and its use in context. Neural networks have been widely used to implement text classification mechanisms for sentiment analysis. In this research we aim to use movie reviews from the internet movie database (IMDb) dataset [1] to perform neural network sentiment analysis. Two novel multi-branch models are explored: CNN-GRU and CNN-bidirectional GRU. The results show that while CNN-bidirectional GRU has slightly higher accuracy, the CNN-GRU has a comparable accuracy and did so with less training time.
MFCC as Features for Speaker Classification using Machine Learning

Speech recognition is an important area in modern-day systems for security and communications. In this preliminary study, a speaker recognition system using MFCC (Mel Frequency Cepstral Coefficient) and machine learning has been designed, developed, and evaluated for male and female identification and speaker identification applications. In this study, pitch frequency and MFCC is used for gender and speaker identification application. Male and female speech signals are recorded in a .wav file at a sampling rate of 16kHz using an application on the phone in a quiet place and then transferred to a computer. This speech signal data is processed using MATLAB to calculate and analyze in the time and frequency domain. The MFCC features in the speech signal are extracted by a small window of 20 to 40 milliseconds in the classifier. This preliminary study extracts the features over 32ms, which shows that N is 512 due to the 16kHz sampling frequency. MFCC features are used to train and predict using Tree, SVM and KNN models with the highest prediction accuracy of 99.6%. Therefore, the study shows MFCC as an excellent feature for speaker recognition classification tasks.
A Generalized Semantic Communication System: From Sources to Channels

Semantic communication is regarded as the breakthrough beyond the Shannon paradigm, which transmits the semantic information only to improve the communication efficiency significantly. This article first introduces a framework for the generalized semantic communication system, which exploits the semantic information in both the multimodal source and the wireless channel environment. Subsequently, the deep learning enabled end-to-end semantic communications and the environment semantics aided wireless communications are demonstrated through two user cases. The article is concluded with several research challenges to boost the development of such a generalized semantic communication system.
Cross-View Human Intention Recognition for Human-Robot Collaboration

Benefiting from the promise of sixth generation (6G) wireless networks, multimodal machine learning based on exploiting complementarity among video, audio, and haptic signals, becomes a key enabler for human intention recognition, which is critical to realize effective human-robot collaboration in Industry 4.0 scenarios. However, as multimodal human intention recognition is limited by expensive equipment and a demanding environment, it is hard to strike an efficient trade-off between inference accuracy and system overhead. Naturally, how to induce more intention semantics from readily available videos emerges as a fundamental issue for human intention recognition. In this article, we use cross-view human intention recognition to solve the above issue and demonstrate the effectiveness of our method with well-designed evaluation metrics. Specifically, we first compensate for the scarcity of intention semantics in the body view by adding a face view. Second, we deploy the cross-view generative model to capture intention semantics induced by the mutual generation of two views. Finally, in the human-robot collaboration experiments, our method gets closer to human performance regarding response time and inference accuracy.
Improved GraphSVX for GNN Explanations Based on Cross Entropy

Graph neural networks (GNNs) are a type of neural networks that can operate on graph data structures. And GNNs are difficult to explain. This lack of interpretability is a significant challenge in domains where transparency, accountability, and fairness are essential. This paper proposes an improved version of the GraphSVX method for explaining graph neural networks (GNNs) based cross-entropy. The proposed method combines the strengths of the existing explanation techniques and outperforms the original methods in terms of accuracy. It was evaluated on several benchmark datasets and a self-collected circuit dataset. The improved GraphSVX method provides a promising approach to explain GNNs for both graph classification tasks and node classification tasks, which can increase trust in the model’s decisions and facilitate the development of more transparent and accountable AI systems.
Naturalness in Source Code Summarization. How Significant is it?

Research in source code summarization, that is the description of the functionality of a program with short sentences expressed in natural language, is a topic of great interest in the software engineering community, since it can help in automatically generating software documentation, and in general can ease the effort of the developers in understanding the code they are working on. In this work, which is conceived as a negative results paper, we study the existing neural models designed for this purpose, pointing out their high sensitivity to the natural elements present in the source code (i.e. comments and identifiers) and the related drop in performance when such elements are ablated or masked. We then propose a novel source code summarization approach based on the aid of an intermediate pseudo-language, through which we are able to fine-tune the BRIO model for natural language on source code summarization, and to achieve results comparable to that obtained by the state-of-the-art source code competitors (e.g. PLBART and CodeBERT). We finally discuss about the limitations of these NLP-based approaches when transferred in the domain of source code processing, and we provide some insights for further research directions.
Face Patches Designed Through Neuroevolution for Face Recognition With Large Pose Variation

Face Recognition (FR) has been a widely used biometric technique for identity authentication in various domains. Despite the remarkable progress in the field of FR during the past few years, there are still challenges that need to be addressed, including recognition of faces with large pose variation. Following the idea of organic face patches found in and used by human and macaque monkey brains, in this paper we report a set of convolutional neural networks (CNNs) that we have designed to represent face patches specialized in a particular face pose orientation range. We use neuroevolution, employing genetic algorithms (GAs), to define the structure of three CNNs. Each CNN is evolved through a genetic algorithm, to a particular face pose orientation range for small, medium, and large face rotations. Each CNN was evolved to improve FR accuracy using the training partition of the VGGFace2 dataset, split into three sets for small, medium, and large face rotations. The GAs generated new specialized CNNs tuned to the three face orientation ranges: small, medium, and large face rotations. Following standard procedures, using extended training for a longer number of epochs, the best CNN individuals were trained with VGGFace2 (training set), and MS1M datasets. The performance of our proposed method was assessed on several datasets that contain a significant number of faces with large pose variation: VGGFace2 (test set), CPFLW, and CFP_FP. The results reached accuracies greater than those of the state-of-the-art of 95.91%, 95.73%, 94.60% and 99.18% on VGGFace2(test), VGGFace2_FP, CPFLW and CFP_FP, respectively.
A Novel Resource-Saving and Traceable Tea Production and Supply Chain Based on Blockchain and IoT

To realize efficient cooperation among tea plantations, manufacturers, logistics, and sellers, the tea production and supply chain (PSC) benefits participating enterprises by coordinating tea planting, product processing, transportation, and product sales links. However, the current tea PSC lacks resource-saving automatic control and suffers from the effect of counterfeit tea products and inefficient supervision. To address these problems, in this work, blockchain and Internet of Things (IoT) technologies are applied to turn the tea PSC around with efficient automation and decentralized supervision. Specifically, we propose DeTea, a blockchain-IoT-empowered decentralized framework, for chain-wide tea counterfeiting supervision and automatic environment management. On the one hand, for the benefit of tea customers, an incentive scheme is designed based on blockchain technology to attract tea PSC participants and decentralized detectors to report dishonest participants, which can motivate the good behaviors of the participating enterprises for a healthier tea market. On the other hand, for the benefit of practitioners of tea products, we realize the automation of environment monitoring and equipment control via IoT technology. Moreover, we design an improved adaptive weighted data fusion algorithm for accurate IoT data and efficient resource allocation, and present an optimal irrigation strategy for automatic plantation environment adjustment. To evaluate the proposed solution, a prototype is implemented by designing a blockchain infrastructure with ten main smart contracts and constructing an IoT-based system sandbox for automatic environment monitoring and resource allocation. Experimental results demonstrate the high throughput of DeTea and the efficiency of resource-saving automation. This work introduces blockchain and IoT technologies to tea PSC for full-process traceability and automatic environment control, which is expected to extend to other agricultural products to ensure the safety, reliability, and efficiency of agricultural traceability systems.
Dual disentanglement of user–item interaction for recommendation with causal embedding

To achieve personalized recommendations, the recommender system selects the items that users may like by learning the collected user–item interaction data. However, the acquisition and use of data usually form a feedback loop, which leads to recommender systems suffering from popularity bias. To solve this problem, we propose a novel dual disentanglement of user–item interaction for recommendation with causal embedding (DDCE). Different from the existing work, our innovation is we take into account double-end popularity bias from the user-side and the item-side. Firstly, we perform a causal analysis of the reasons for user–item interaction and obtain the causal embedding representation of each part according to the analysis results. Secondly, on the item-side, we consider the influence of item attributes on popularity to improve the reliability of the item popularity. Then, on the user-side, we consider the effect of the time series when obtaining users’ interest. We model the contrastive learning task to disentangle users’ long–short-term interests, which avoids the bias of long–short-term interests overlapping, and use the attention mechanism to realize the dynamic integration of users’ long–short-term interests. Finally, we realize the disentanglement of user–item interaction reasons by decoupling user interest and item popularity. We experiment on two real-world datasets (Douban Movie and KuaiRec) to verify the significance of DDCE, the average improvement of DDCE in three evaluation metrics (NDCG, HR, and Recall) compared to the state-of-the-art model are 5.1106% and 4.1277% (MF as the backbone), 3.8256% and 3.2790% (LightGCN as the backbone), respectively.
Investigating Unsupervised Neural Machine Translation for Low-resource Language Pair English-Mizo via Lexically Enhanced Pre-trained Language Models

The vast majority of languages in the world at present are considered to be low-resource languages. Since the availability of large parallel data is crucial for the success of most modern machine translation approaches, improving machine translation for low-resource languages is a key challenge. Most unsupervised techniques for translation benefit closely related languages with monolingual data of substantial quantity. To facilitate research in this direction for the extremely low resource language pair English ( en ) and Mizo ( lus ), we have developed a parallel and monolingual corpus for the Mizo language from various news websites. We explore Unsupervised Neural Machine Translation (UNMT) based on the developed monolingual data. We observe that cross-lingual embedding (CLWE) initializations on subword segmented data during pre-training, based on both masked language modelling and sequence-to-sequence generation tasks, improve translation performance. We experiment with cross-lingual alignment and combined alignment and joint training for learning the cross-lingual embedding representations. We also report baseline performances and the impact of CLWE initialization using semi-supervised and supervised neural machine translation. Empirical results show that both CLWE initializations work well for the distant pair English-Mizo compared to the baselines.