Reviewerly/exHarmony huggingface dataset:
Small-object detection based on YOLOv5 in autonomous driving systems

With the rapid advancements in the field of autonomous driving, the need for faster and more accurate object detection frameworks has become a necessity. Many recent deep learning-based object detectors have shown compelling performance for the detection of large objects in a variety of real-time driving applications. However, the detection of small objects such as traffic signs and traffic lights is a challenging task owing to the complex nature of such objects. Additionally, the complexity present in a few images due to the existence of foreground/background imbalance and perspective distortion caused by adverse weather and low-lighting conditions further makes it difficult to detect small objects accurately. In this letter, we investigate how an existing object detector can be adjusted to address specific tasks and how these modifications can impact the detection of small objects. To achieve this, we explore and introduce architectural changes to the popular YOLOv5 model to improve its performance in the detection of small objects without sacrificing the detection accuracy of large objects, particularly in autonomous driving. We will show that our modifications barely increase the computational complexity but significantly improve the detection accuracy and speed. Compared to the conventional YOLOv5, the proposed iS-YOLOv5 model increases the mean Average Precision (mAP) by 3.35% on the BDD100K dataset. Nevertheless, our proposed model improves the detection speed by 2.57 frames per second (FPS) compared to the YOLOv5 model.
ONYX: Assisting Users in Teaching Natural Language Interfaces Through Multi-Modal Interactive Task Learning

Users are increasingly empowered to personalize natural language interfaces (NLIs) by teaching how to handle new natural language (NL) inputs. However, our formative study found that when teaching new NL inputs, users require assistance in clarifying ambiguities that arise and want insight into which parts of the input the NLI understands. In this paper we introduce ONYX, an intelligent agent that interactively learns new NL inputs by combining NL programming and programming-by-demonstration, also known as multi-modal interactive task learning. To address the aforementioned challenges, ONYX provides suggestions on how ONYX could handle new NL inputs based on previously learned concepts or user-defined procedures, and poses follow-up questions to clarify ambiguities in user demonstrations, using visual and textual aids to clarify the connections. Our evaluation shows that users provided with ONYX's new features achieved significantly higher accuracy in teaching new NL inputs (median: 93.3%) in contrast to those without (median: 73.3%).
AI-Aided Teaching Model in Education 5.0

In the educational setting, artificial intelligence (AI) technology, notably chatbots, has made substantial improvements in English learning. This study aims to determine the effectiveness of using the Artificial Intelligence Virtual Dream Friend and John English Boot applications on learning English in the 5.0 revolution era in English courses for first-semester students at university. The assessment method used is a quantitative research method and research design (quasi-experiment design). Based on the results of the study, it can be concluded that the results of the comparison test showed that My Virtual Dream Friend and John English Bot were both effective for use as computer tutoring in English courses and also increased interest in learning English in the 5.0 revolution era compared to previous conventional methods. The outcomes of this study might be used to direct future research into utilizing chatbots outside of the classroom as learning companions, and educators could use them to adapt evaluation and feedback procedures.
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO’s development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.
Automating Intention Mining

Developers frequently discuss aspects of the systems they are developing online. The comments they post to discussions form a rich information source about the system. Intention mining, a process introduced by Di Sorbo et al., classifies sentences in developer discussions to enable further analysis. As one example of use, intention mining has been used to help build various recommenders for software developers. The technique introduced by Di Sorbo et al. to categorize sentences is based on linguistic patterns derived from two projects. The limited number of data sources used in this earlier work introduces questions about the comprehensiveness of intention categories and whether the linguistic patterns used to identify the categories are generalizable to developer discussion recorded in other kinds of software artifacts (e.g., issue reports). To assess the comprehensiveness of the previously identified intention categories and the generalizability of the linguistic patterns for category identification, we manually created a new dataset, categorizing 5,408 sentences from issue reports of four projects in GitHub. Based on this manual effort, we refined the previous categories. We assess Di Sorbo et al.'s patterns on this dataset, finding that the accuracy rate achieved is low (0.31). To address the deficiencies of Di Sorbo et al.'s patterns, we propose and investigate a convolution neural network (CNN)-based approach to automatically classify sentences into different categories of intentions. Our approach optimizes CNN by integrating batch normalization to accelerate the training speed, and an automatic hyperparameter tuning approach to tune appropriate hyperparameters of CNN. Our approach achieves an accuracy of 0.84 on the new dataset, improving Di Sorbo et al.'s approach by 171 percent. We also apply our approach to improve an automated software engineering task, in which we use our proposed approach to rectify misclassified issue reports, thus reducing the bias introduced by such data to other studies. A case study on four open source projects with 2,076 issue reports shows that our approach achieves an average AUC score of 0.687, which improves other baselines by at least 16 percent.
A Two-Stream Mutual Attention Network for Semi-Supervised Biomedical Segmentation with Noisy Labels

Learning-based methods suffer from a deficiency of clean annotations, especially in biomedical segmentation. Although many semi-supervised methods have been proposed to provide extra training data, automatically generated labels are usually too noisy to retrain models effectively. In this paper, we propose a Two-Stream Mutual Attention Network (TSMAN) that weakens the influence of back-propagated gradients caused by incorrect labels, thereby rendering the network robust to unclean data. The proposed TSMAN consists of two sub-networks that are connected by three types of attention models in different layers. The target of each attention model is to indicate potentially incorrect gradients in a certain layer for both sub-networks by analyzing their inferred features using the same input. In order to achieve this purpose, the attention models are designed based on the propagation analysis of noisy gradients at different layers. This allows the attention models to effectively discover incorrect labels and weaken their influence during parameter updating process. By exchanging multi-level features within two-stream architecture, the effects of noisy labels in each sub-network are reduced by decreasing the noisy gradients. Furthermore, a hierarchical distillation is developed to provide reliable pseudo labels for unlabelded data, which further boosts the performance of TSMAN. The experiments using both HVSMR 2016 and BRATS 2015 benchmarks demonstrate that our semi-supervised learning framework surpasses the state-of-the-art fully-supervised results.
Incremental Re-Identification by Cross-Direction and Cross-Ranking Adaption

Person re-identification is widely applied in video surveillance and criminal investigation applications. To achieve better performance, an additional re-ranking step is often exploited. Related methods attempt to optimize the result according to every single query independently. However, in a practical scene, as the investigation process goes on, the other queries, in particular, the gradually accumulated logs, can be used to guide or regularize the current query. In this paper, we propose to optimize the result according to not only the current query itself but also the other queries and historical logs. We respectively investigate the cross-direction and the cross-ranking constraints among different queries. Based on the investigations, we propose a reciprocal optimization method to refine multiple ranking lists reciprocally. Experiments on the VIPeR, new-protocol CUHK03, and Market-1501 datasets confirm the effectiveness of our method. In particular, on the Market-1501 dataset, with full utilization of the other queries, the method achieves an accuracy rate of 94.66% at rank-1 and a very high mAP of 75.12%, and significantly outperforms the state-of-the-art methods.
A Comparative Analysis of Nonlinear Machine Learning Algorithms for Breast Cancer Detection

Breast cancer is a form of invasive cancer and one of the most common health problems for women that is globally responsible for a large number of deaths.Accurately classifying and categorizing breast cancer subtype is an essential task.Automated techniques based on artificial intelligence can significantly save time and reduce error.In this paper, a performance comparison between five nonlinear machine learning algorithms viz Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Gaussian Nave Bayes (NB) and Support Vector Machines (SVM) on the Wisconsin Breast Cancer Diagnostic (WBCD) dataset is conducted.The primary objective is to evaluate the performance in classifying data with respect to efficiency and effectiveness of each algorithm in terms of classification test accuracy, precision, and recall.
Authentication Protocol for Cloud Databases Using Blockchain Mechanism

Cloud computing has made the software development process fast and flexible but on the other hand it has contributed to increasing security attacks. Employees who manage the data in cloud companies may face insider attack, affecting their reputation. They have the advantage of accessing the user data by interacting with the authentication mechanism. The primary aim of this research paper is to provide a novel secure authentication mechanism by using Blockchain technology for cloud databases. Blockchain makes it difficult to change user login credentials details in the user authentication process by an insider. The insider is not able to access the user authentication data due to the distributed ledger-based authentication scheme. Activity of insider can be traced and cannot be changed. Both insider and outsider user’s are authenticated using individual IDs and signatures. Furthermore, the user access control on the cloud database is also authenticated. The algorithm and theorem of the proposed mechanism have been given to demonstrate the applicability and correctness.The proposed mechanism is tested on the Scyther formal system tool against denial of service, impersonation, offline guessing, and no replay attacks. Scyther results show that the proposed methodology is secure cum robust.
The SFU Opinion and Comments Corpus: A Corpus for the Analysis of Online News Comments

Abstract We present the SFU Opinion and Comments Corpus (SOCC ), a collection of opinion articles and the comments posted in response to the articles. The articles include all the opinion pieces published in the Canadian newspaper The Globe and Mail in the 5-year period between 2012 and 2016, a total of 10,339 articles and 663,173 comments. SOCC is part of a project that investigates the linguistic characteristics of online comments. The corpus can be used to study a host of pragmatic phenomena. Among other aspects, researchers can explore: the connections between articles and comments; the connections of comments to each other; the types of topics discussed in comments; the nice (constructive) or mean (toxic) ways in which commenters respond to each other; how language is used to convey very specific types of evaluation; and how negation affects the interpretation of evaluative meaning in discourse. Our current focus is the study of constructiveness and evaluation in the comments. To that end, we have annotated a subset of the large corpus (1043 comments) with four layers of annotations: constructiveness, toxicity, negation and Appraisal (Martin and White, The language of evaluation, Palgrave, New York, 2005). This paper details our corpus, the data collection process, the characteristics of the corpus and describes the annotations. While our focus is comments posted in response to opinion news articles, the phenomena in this corpus are likely to be present in many commenting platforms: other news comments, comments and replies in fora such as Reddit, feedback on blogs, or YouTube comments.
A close neighbor mobility method using particle swarm optimizer for solving multimodal optimization problems

Niching is an important technique for multimodal optimization. Most existing niching methods require specification of certain niching parameters in order to perform well. But these parameters are usually difficult to set because they depend on the problem. The particle swarm optimization algorithm using the ring neighborhood topology does not require any niche parameters, but the determination of the particle neighborhood in this method is based on the subscript of the particle, and the result fails to achieve the best performance. For better performance, in this paper, a particle swarm optimization algorithm based on the ring neighborhood topology of Euclidean distance between particles is proposed, which is called the close neighbor mobility optimization algorithm. The algorithm mainly includes the following three strategies: elite selection mechanism, close neighbor mobility strategy and modified DE strategy. It mainly uses the Euclidean distance between particles. Each particle forms its own unique niche, evolves in a local scope, and finally locates multiple global optimal solutions with high precision. The algorithm greatly improves the accuracy of the particle. The experimental results show that the close neighbor mobility optimization algorithm has better performance than most single-objective multi-modal algorithms.
Reinforcement-Learning-Guided Source Code Summarization Using Hierarchical Attention

Code summarization (aka comment generation) provides a high-level natural language description of the function performed by code, which can benefit the software maintenance, code categorization and retrieval. To the best of our knowledge, the state-of-the-art approaches follow an encoder-decoder framework which encodes source code into a hidden space and later decodes it into a natural language space. Such approaches suffer from the following drawbacks: (a) they are mainly input by representing code as a sequence of tokens while ignoring code hierarchy; (b) most of the encoders only input simple features (e.g., tokens) while ignoring the features that can help capture the correlations between comments and code; (c) the decoders are typically trained to predict subsequent words by maximizing the likelihood of subsequent ground truth words, while in real world, they are excepted to generate the entire word sequence from scratch. As a result, such drawbacks lead to inferior and inconsistent comment generation accuracy. To address the above limitations, this paper presents a new code summarization approach using hierarchical attention network by incorporating multiple code features, including type-augmented abstract syntax trees and program control flows. Such features, along with plain code sequences, are injected into a deep reinforcement learning (DRL) framework (e.g., actor-critic network) for comment generation. Our approach assigns weights (pays “attention”) to tokens and statements when constructing the code representation to reflect the hierarchical code structure under different contexts regarding code features (e.g., control flows and abstract syntax trees). Our reinforcement learning mechanism further strengthens the prediction results through the actor network and the critic network, where the actor network provides the confidence of predicting subsequent words based on the current state, and the critic network computes the reward values of all the possible extensions of the current state to provide global guidance for explorations. Eventually, we employ an advantage reward to train both networks and conduct a set of experiments on a real-world dataset. The experimental results demonstrate that our approach outperforms the baselines by around 22 to 45 percent in BLEU-1 and outperforms the state-of-the-art approaches by around 5 to 60 percent in terms of S-BLEU and C-BLEU.
AI at the Edge: Blockchain-Empowered Secure Multiparty Learning With Heterogeneous Models

Edge computing, an emerging computing paradigm pushing data computing and storing to network edges, enables many applications that require high computing complexity, scalability, and security. In the big data era, one of the most critical applications is multiparty learning or federated learning, which allows different parties to collaborate with each other to obtain better learning models without sharing their own data. However, there are several main concerns about the current multiparty learning systems. First, most existing systems are distributed and need a central server to coordinate the learning process. However, such a central server can easily become a single point of failure and may not be trustworthy. Second, although quite a few schemes have been proposed to study Byzantine attacks, a very common and challenging kind of attack in distributed systems, they generally consider the scenario of learning a global model. However, in fact, all parties in multiparty learning usually have their own local models. The learning methods and security issues, in this case, are not fully explored. In this article, we propose a novel blockchain-empowered decentralized secure multiparty learning system with heterogeneous local models called BEMA. Particularly, we consider two types of Byzantine attacks, and carefully design “off-chain sample mining” and “on-chain mining ” schemes to protect the security of the proposed system. We theoretically prove the system performance bound and resilience under Byzantine attacks. The simulation results show that the proposed system obtains comparable performance with that of conventional distributed systems, and bounded performance in the case of Byzantine attacks.
Edgence: A blockchain-enabled edge-computing platform for intelligent IoT-based dApps

Nowadays scalable IoT management is a bottleneck of IoT development due to the geographically dispersed distribution, fragmented ownerships, and ever-growing population of IoT devices. To intelligently manage massive decentralized applications (dApps) in IoT usecases, Edgence (EDGe + intelligENCE) is proposed to use edge clouds to access IoT devices and users, and then use its in-built blockchain to realize self-governing and self-supervision of the edge clouds. Edgence proposes to use masternode technology to introduce IoT devices and users into a closed blockchain system, which can extend the range of blockchain to IoT-based dApps. Further, masternodes do good to scalability by raising the TPS (transactions per second) of the blockchain network. To support various dApps, a three-tier validation is proposed, namely script validation, smart contract validation, and masternode validation. To avoid energy consumption resulted by blockchain consensus, Edgence proposes a random but verifiable way to elect a masternode to generate each new block. The potential of the tailored Edgence is shown by examples of decentralized crowdsourcing and AI training.
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization

This paper considers stochastic optimization problems for a large class of objective functions, including convex and continuous submodular. Stochastic proximal gradient methods have been widely used to solve such problems; however, their applicability remains limited when the problem dimension is large and the projection onto a convex set is costly. Instead, stochastic conditional gradient methods are proposed as an alternative solution relying on (i) Approximating gradients via a simple averaging technique requiring a single stochastic gradient evaluation per iteration; (ii) Solving a linear program to compute the descent/ascent direction. The averaging technique reduces the noise of gradient approximations as time progresses, and replacing projection step in proximal methods by a linear program lowers the computational complexity of each iteration. We show that under convexity and smoothness assumptions, our proposed method converges to the optimal objective function value at a sublinear rate of $O(1/t^{1/3})$. Further, for a monotone and continuous DR-submodular function and subject to a general convex body constraint, we prove that our proposed method achieves a $((1-1/e)OPT-\eps)$ guarantee with $O(1/\eps^3)$ stochastic gradient computations. This guarantee matches the known hardness results and closes the gap between deterministic and stochastic continuous submodular maximization. Additionally, we obtain $((1/e)OPT -\eps)$ guarantee after using $O(1/\eps^3)$ stochastic gradients for the case that the objective function is continuous DR-submodular but non-monotone and the constraint set is down-closed. By using stochastic continuous optimization as an interface, we provide the first $(1-1/e)$ tight approximation guarantee for maximizing a monotone but stochastic submodular set function subject to a matroid constraint and $(1/e)$ approximation guarantee for the non-monotone case.
Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning

The goal of few-shot learning is to learn a classifier that can recognize unseen classes from limited support data with labels. A common practice for this task is to train a model on the base set first and then transfer to novel classes through fine-tuning or meta-learning. However, as the base classes have no overlap to the novel set, simply transferring whole knowledge from base data is not an optimal solution since some knowledge in the base model may be biased or even harmful to the novel class. In this paper, we propose to transfer partial knowledge by freezing or fine-tuning particular layer(s) in the base model. Specifically, layers will be imposed different learning rates if they are chosen to be fine-tuned, to control the extent of preserved transferability. To determine which layers to be recast and what values of learning rates for them, we introduce an evolutionary search based method that is efficient to simultaneously locate the target layers and determine their individual learning rates. We conduct extensive experiments on CUB and mini-ImageNet to demonstrate the effectiveness of our proposed method. It achieves the state-of-the-art performance on both meta-learning and non-meta based frameworks. Furthermore, we extend our method to the conventional pre-training + fine-tuning paradigm and obtain consistent improvement.