Reviewerly/exHarmony huggingface dataset:
Graph Neural Network Based Collaborative Filtering for API Usage Recommendation

Developers often face the need to find out how to use different APIs suitable for their purposes. API usage recommendation has been shown very useful to facilitate the process of software reuse and daily development. Previous approaches mainly use statistical models and collaborative filtering(CF) techniques to improve the accuracy of recommendation. However, they fail to exploit the high-order connectivity of the interaction of API calls and the structural information of software projects. In this paper, we formulate this problem in terms of the graph-based collaborative filtering recommendation. We propose a novel approach for API usage recommendation, named GAPI, which uses graph neural networks (GNNs) to capture the high-order collaborative signals from API calls. Besides, GAPI integrates project structures into the graph and incorporates text attributes in the network, which are helpful to represent the program semantics. We evaluate our approach on large-scale open-source repositories collected from Github and Maven Central. The experimental results demonstrate that our approach is effective and outperforms the state-of-the-art approaches in terms of success rate and accuracy.
Dynamic Low-Light Image Enhancement for Object Detection via End-to-End Training

Object detection based on convolutional neural networks is a hot research topic in computer vision. The illumination component in the image has a great impact on object detection, and it will cause a sharp decline in detection performance under low-light conditions. Using low-light image enhancement technique as a pre-processing mechanism can improve image quality and obtain better detection results. However, due to the complexity of low-light environments, the existing enhancement methods may have negative effects on some samples. Therefore, it is difficult to improve the overall detection performance in low-light conditions. In this paper, our goal is to use image enhancement to improve object detection performance rather than perceptual quality for humans. We propose a novel framework that combines low-light enhancement and object detection for end-to-end training. The framework can dynamically select different enhancement subnetworks for each sample to improve the performance of the detector. Our proposed method consists of two stage: the enhancement stage and the detection stage. The enhancement stage dynamically enhances the low-light images under the supervision of several enhancement methods and output corresponding weights. During the detection stage, the weights offers information on object classification to generate high-quality region proposals and in turn result in accurate detection. Our experiments present promising results, which show that the proposed method can significantly improve the detection performance in low-light environment.
Human Activity Recognition Algorithm in Video Sequences Based on Integration of Magnitude and Orientation Information of Optical Flow

Human activity recognition from video sequences has emerged recently as pivotal research area due to its importance in a large number of applications such as real-time surveillance monitoring, healthcare, smart homes, security, behavior analysis, and many more. However, lots of challenges also exist such as intra-class variations, object occlusion, varying illumination condition, complex background, camera motion, etc. In this work, we introduce a novel feature descriptor based on the integration of magnitude and orientation information of optical flow and histogram of oriented gradients which gives an efficient and robust feature vector for the recognition of human activities for real-world environment. In the proposed approach first we computed magnitude and orientation of the optical flow separately then a local-oriented histogram of magnitude and orientation of motion flow vectors are computed using histogram of oriented gradients followed by linear combination feature fusion strategy. The resultant features are then processed by a multiclass Support Vector Machine (SVM) classifier for activity recognition. The experimental results are performed over different publically available benchmark video datasets such as UT interaction, CASIA, and HMDB51 datasets. The effectiveness of the proposed approach is evaluated in terms of six different performance parameters such as accuracy, precision, recall, specificity, [Formula: see text]-measure, and Matthew’s correlation coefficient (MCC). To show the significance of the proposed method, it is compared with the other state-of-the-art methods. The experimental result shows that the proposed method performs well in comparison to other state-of-the-art methods.
A low-query black-box adversarial attack based on transferability

Artificial intelligence systems suffer from black-box adversarial attacks recently. To prevent this kind of attack, a large amount of researches that reveal the nature of this attack has emerged. However, the query count, success rate, and distortion in the existing works cannot fully satisfy the practical purposes. In this paper, we propose a low-query black-box adversarial attack based on transferability by combining the optimization-based method and the transfer-based method. Our approach aims to improve the black-box attack with a lower number of queries, higher success rate, and lower distortion. In addition, we make full use of surrogate models and optimize the objective function to further improve the performance of our algorithm. We verified our method on MNIST (Lecun and Bottou, 1998) [1], CIFAR-10 (Krizhevsky et al., 2009) [2], and ImageNet (Deng et al. 2009) [3], respectively. Experimental results demonstrate that our method can implement a black-box attack with more than 98.5% success rate and achieve specific distortion with less than 5% queries comparing with other state-of-the-art methods.
Detection Thresholds with Joint Horizontal and Vertical Gains in Redirected Jumping

Redirected jumping (RDJ) is a locomotion technique that allows users to explore a virtual space that is larger than the available physical space by imperceptibly manipulating users' virtual viewpoints according to different gains. In previous redirected jumping work, different types of gains were imposed separately, without considering the possible interaction effects of horizontal and vertical gains on the jumping distance perception. To figure out how humans perceive distance manipulation when more than one gain is used, in this paper, we explored joint horizontal and vertical gains that manipulate horizontal and vertical distances at the same time during two-legged takeoff jumping in the virtual space. We estimated and analyzed horizontal and vertical detection thresholds by conducting a user study, fitting the data to two-dimensional psychometric functions, and visualizing the fitted 3D plots. We provided quantitative insights into the effects of joint gains on detection thresholds, where the imperceptible range for one gain can be affected by the variation of the other gain. Finally, we designed redirected jumping-based games as applications with joint horizontal and vertical gains and demonstrated the effectiveness of the redirected jumping technique.
Synthesis of Covert Actuator and Sensor Attackers as Supervisor Synthesis

In this work, we investigate the problem of covert attacker synthesis for supervisory control of discrete-event systems. In particular, we consider attackers that exercise both actuator attacks and sensor attacks, where the (partial-observation) attackers may or may not eavesdrop the control commands issued by the supervisor. We develop a reduction from the covert attacker synthesis problem to the Ramadge-Wonham supervisor synthesis problem, which generalizes our previous work on a reduction based approach for covert actuator attacker synthesis.
Self-Guided Adaptation: Progressive Representation Alignment for Domain Adaptive Object Detection

Unsupervised domain adaptation (UDA) has achieved unprecedented success in improving the cross-domain robustness of object detection models. However, existing UDA methods largely ignore the instantaneous data distribution and the sampling strategy during model learning, which could deteriorate the feature representation given large domain shift. In this work, we propose a Self-Guided Adaptation (SGA) model, targeting at aligning feature representation and transferring object detection models across domains while considering the instantaneous alignment difficulty. The core of SGA is to calculate “hardness” factors for sample pairs indicating domain distance in a kernel space. With the hardness factor, the proposed SGA adaptively indicates the importance of samples and assigns them different constrains. Indicated by these hardness factors, Self-Guided Progressive Sampling (SPS) is implemented in an “easy-to-hard” way during model adaptation. Using multi-stage convolutional features, SGA is further aggregated to fully align hierarchical representations of detection models. Extensive experiments on commonly-used benchmarks show that SGA improves the state-of-the-art methods with significant margins especially on large domain shift cases.
An ensemble classification-based approach to detect attack level of SQL injections

Sensitive data including identity information, passwords, financial and business processes belonging to the user are kept in the databases. These data can be obtained by attackers with malicious code added to SQL queries. The malicious and clean SQL queries are taken from OWASP dataset to ensure that the proposed approach effective and practical. The middleware application which is developed in this study analyzes these SQL queries instantly to prevent attackers from accessing sensitive data in databases. In order to provide protection, an ensemble classification algorithm is trained with 22 features which are obtained from queries containing malicious codes. The trained ensemble algorithm classifies queries as clean and malicious. For the first time in this study, malicious SQL injections are detected as simple, unified or lateral to determine the level of the cyber-attack. If the query is clean, the request is provided in the flow forwarding scheme, otherwise the query is blocked. If SQL injection is detected as simple, the SQL request is blocked. In other cases source IP address is blocked at different time intervals. The accuracy of the model maintains over 98% to detect SQL injections, and 92% to classify as simple, unified or lateral these attacks. This result demonstrates that the developed middleware application has an active role against simple, unified and lateral SQL Injection attacks which are so hard to detect and provides flexible decisions against the attacks.
Mixed-pooling-dropout for convolutional neural network regularization

Deep neural networks are the most used machine learning systems in the literature, for they are able to train huge amounts of data with a large number of parameters in a very effective way. However, one of the problems that such networks face is overfitting. There are many ways to address the overfitting issue, one of which is regularization using the dropout function. The use of dropout has the benefit of using a combination of different networks in one architecture and preventing units from co-adapting in an excessive way. The dropout function is known to work well in fully-connected layers as well as in pooling layers. In this work, we propose a novel method called Mixed-Pooling-Dropout that adapts the dropout function with a mixed-pooling strategy. The dropout operation is represented by a binary mask with each element drawn independently from a Bernoulli distribution. Experimental results show that our proposed method outperforms conventional pooling methods as well as the max-pooling-dropout method with an interesting margin (0.926 vs 0.868) regardless of the retaining probability.
Concept drift detection and adaptation for federated and continual learning

Abstract Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and therefore, the user experience. Federated learning is a young and popular framework that allows multiple distributed devices to train deep learning models collaboratively while preserving data privacy. Nevertheless, this approach may not be optimal for scenarios where data distribution is non-identical among the participants or changes over time, causing what is known as concept drift . Little research has yet been done in this field, but this kind of situation is quite frequent in real life and poses new challenges to both continual and federated learning. Therefore, in this work, we present a new method, called Concept-Drift-Aware Federated Averaging (CDA-FedAvg). Our proposal is an extension of the most popular federated algorithm, Federated Averaging (FedAvg), enhancing it for continual adaptation under concept drift. We empirically demonstrate the weaknesses of regular FedAvg and prove that CDA-FedAvg outperforms it in this type of scenario.
An efficient alpha seeding method for optimized extreme learning machine-based feature selection algorithm

Embedded feature selection algorithms, such as support vector machine based recursive feature elimination (SVM-RFE), have proven to be effective for many real applications. However, due to the model selection problem, SVM-RFE naturally suffers from a heavy computational burden as well as high computational complexity. To solve these issues, this paper proposes using an optimized extreme learning machine (OELM) model instead of SVM. This model, referred to as OELM-RFE provides an efficient active set solver for training the OELM algorithm. We also present an effective alpha seeding algorithm to efficiently solve successive quadratic programming (QP) problems inherent in OELM. One of the salient characteristics of OELM-RFE is that it has only one tuning parameter: the penalty constant C. Experimental results from work on benchmark datasets show that OELM-RFE tends to have higher prediction accuracy than SVM-RFE, and requires fewer model selection efforts. In addition, the alpha seeding method works better on more datasets.
OpenNEEDS: A Dataset of Gaze, Head, Hand, and Scene Signals During Exploration in Open-Ended VR Environments

We present OpenNEEDS, the first large-scale, high frame rate, comprehensive, and open-source dataset of Non-Eye (head, hand, and scene) and Eye (3D gaze vectors) data captured for 44 participants as they freely explored two virtual environments with many potential tasks (i.e., reading, drawing, shooting, object manipulation, etc.). With this dataset, we aim to enable research on the relationship between head, hand, scene, and gaze spatiotemporal statistics and its applications to gaze estimation. To demonstrate the power of OpenNEEDS, we show that gaze estimation models using individual non-eye sensors and an early fusion model combining all non-eye sensors outperform all baseline gaze estimation models considered, suggesting the possibility of considering non-eye sensors in the design of robust eye trackers. We anticipate that this dataset will support research progress in many areas and applications such as gaze estimation and prediction, sensor fusion, human-computer interaction, intent prediction, perceptuo-motor control, and machine learning.
Top-k Partial Label Machine

To deal with ambiguities in partial label learning (PLL), the existing PLL methods implement disambiguations, by either identifying the ground-truth label or averaging the candidate labels. However, these methods can be easily misled by the false-positive labels in the candidate label set. We find that these ambiguities often originate from the noise caused by highly correlated or overlapping candidate labels, which leads to the difficulty in identifying the ground-truth label on the first attempt. To give the trained models more tolerance, we first propose the top-k partial loss and convex top-k partial hinge loss. Based on the losses, we present a novel top-k partial label machine (TPLM) for partial label classification. An efficient optimization algorithm is proposed based on accelerated proximal stochastic dual coordinate ascent (Prox-SDCA) and linear programming (LP). Moreover, we present a theoretical analysis of the generalization error for TPLM. Comprehensive experiments on both controlled UCI datasets and real-world partial label datasets demonstrate that the proposed method is superior to the state-of-the-art approaches.
State of machine and deep learning in histopathological applications in digestive diseases

Machine learning (ML)- and deep learning (DL)-based imaging modalities have exhibited the capacity to handle extremely high dimensional data for a number of computer vision tasks. While these approaches have been applied to numerous data types, this capacity can be especially leveraged by application on histopathological images, which capture cellular and structural features with their high-resolution, microscopic perspectives. Already, these methodologies have demonstrated promising performance in a variety of applications like disease classification, cancer grading, structure and cellular localizations, and prognostic predictions. A wide range of pathologies requiring histopathological evaluation exist in gastroenterology and hepatology, indicating these as disciplines highly targetable for integration of these technologies. Gastroenterologists have also already been primed to consider the impact of these algorithms, as development of real-time endoscopic video analysis software has been an active and popular field of research. This heightened clinical awareness will likely be important for future integration of these methods and to drive interdisciplinary collaborations on emerging studies. To provide an overview on the application of these methodologies for gastrointestinal and hepatological histopathological slides, this review will discuss general ML and DL concepts, introduce recent and emerging literature using these methods, and cover challenges moving forward to further advance the field.
An Enhanced Gated Recurrent Unit with Auto-Encoder for Solving Text Classification Problems

Classification has become an important task for automatically categorizing documents based on their respective group. The purpose of classification is to assign the pre-specified group or class to an instance based on the observed features related to that instance. For accurate text classification, feature selection techniques are normally used to identify important features and to remove irrelevant, undesired and noisy features for minimizing the dimensionality of feature space. Therefore, in this research, a new model namely Encoder Simplified GRU (ES-GRU) is proposed to reduce dimension of data using an auto-encoder (AE). Gated Recurrent Unit (GRU) is a deep learning algorithm that contains update gate and reset gate, which is considered as one of the most efficient text classification technique, specifically on sequential datasets. Accordingly, the reset gate is replaced with an update gate in order to reduce the redundancy and complexity in the standard GRU. The proposed model has been evaluated on five benchmark text datasets and compared with six baseline well-known text classification approaches, which includes standard GRU, AE, Long Short-Term Memory, Convolutional Neural Network, Support Vector Machine, and Naïve Bayes. Based on various types of performance evaluation parameters, a considerable amount of improvement has been observed in the performance of the proposed model as compared to state-of-the-art approaches.
Improvement of Traveling Salesman Problem Solution Using Hybrid Algorithm Based on Best-Worst Ant System and Particle Swarm Optimization

This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature. However, additional potential improvement in the arrangement way decrease is yet possible in this approach. In this paper BWAS based incorporated arrangement as a high level type of ACO to upgrade the exhibition of the TSP arrangement is proposed. In addition, a novel approach, based on hybrid Particle Swarm Optimization (PSO) and ACO (BWAS) has also been introduced in this work. The presentation measurements of arrangement quality and assembly time have been utilized in this work and proposed algorithm is tried against various standard test sets to examine the upgrade in search capacity. The outcomes for TSP arrangement show that initial trail setup for the best particle can result in shortening the accumulated process of the optimization by a considerable amount. The exhibition of the mathematical test shows the viability of the proposed calculation over regular ACO and PSO-ACO based strategies.
Twisted linearized Reed-Solomon codes: A skew polynomial framework

We provide an algebraic description for sum-rank metric codes, as quotient space of a skew polynomial ring. This approach generalizes at the same time the skew group algebra setting for rank-metric codes and the polynomial setting for codes in the Hamming metric. This allows to construct twisted linearized Reed-Solomon codes, a new family of maximum sum-rank distance codes extending at the same time Sheekey's twisted Gabidulin codes in the rank metric and twisted Reed-Solomon codes in the Hamming metric. Furthermore, we provide an analogue in the sum-rank metric of Trombetti-Zhou construction, which also provides a family of maximum sum-rank distance codes. As a byproduct, in the extremal case of the Hamming metric, we obtain a new family of additive MDS codes over quadratic fields.
Few-shot Learning for Named Entity Recognition Based on BERT and Two-level Model Fusion

Currently, as a basic task of military document information extraction, Named Entity Recognition (NER) for military documents has received great attention. In 2020, China Conference on Knowledge Graph and Semantic Computing (CCKS) and System Engineering Research Institute of Academy of Military Sciences (AMS) issued the NER task for test evaluation, which requires the recognition of four types of entities including Test Elements (TE), Performance Indicators (PI), System Components (SC) and Task Scenarios (TS). Due to the particularity and confidentiality of the military field, only 400 items of annotated data are provided by the organizer. In this paper, the task is regarded as a few-shot learning problem for NER, and a method based on BERT and two-level model fusion is proposed. Firstly, the proposed method is based on several basic models fine tuned by BERT on the training data. Then, a two-level fusion strategy applied to the prediction results of multiple basic models is proposed to alleviate the over-fitting problem. Finally, the labeling errors are eliminated by post-processing. This method achieves F1 score of 0.7203 on the test set of the evaluation task.