Reviewerly/exHarmony huggingface dataset:
Research on Airport Aircraft Target Detection Algorithm Based on YOLOv4

Aiming at the complex airport environment and the large scale transformation of aircraft, a target detection algorithm based on YOLOv4 is designed. Based on the YOLOv4 algorithm structure, a feature fusion scale is added to the original 3 detection scales, which has the ability to obtain smaller target information. Simultaneously, the residual network structure is simplified, and a certain amount of calculation is reduced. In order to optimize the loss, the regression loss function CIoU is introduced, which accelerates the convergence of the network. The test results show that the algorithm of this article can reach a prediction accuracy of 93.31% for airplane target, a recall rate of 83.92%, a mean average accuracy of 91.02%, which is 2.49% higher than YOLOv4, which improves the recognition accuracy of the model in the airport environment.
Real-Time YOLO-based Heterogeneous Front Vehicles Detection

The perception of the complex road environment is a critical factor in autonomous driving, which has become the research focus in intelligent vehicles. In this paper, a real-time front vehicle detection system is proposed to ensure safe driving in a complex environment, particularly in congested megacities. This system is based on the YOLO model, which effectively detects and classifies various vehicles from both images and videos. It improves detection accuracy by modifying a feature extraction-based backbone. To the authors’ best knowledge, this is the first time that vehicle detection is implemented on the recently published DhakaAI dataset. Compared to the other available datasets for object detection, such as KITTI, the DhakaAI dataset has a complex environment with numerous vehicles (21 different types). Experimental results demonstrate that the proposed system outperforms the state-of-the-art object detectors. In this method, the mAP (mean average precision) and the FPS (frame per second) is increased by 2.97% and 1.47, 4.64% and 5.57, 4.75% and 3.02, compared to the RetinaNet, SSD, and Faster RCNN on this dataset, respectively.
Inside Commits: An Empirical Study on Commits in Open-Source Software

GitHub is currently the most popular open-source software hosting platform, containing about 20 million public repositories. Many studies have relied on data mined from GitHub repositories, especially commits. However, not knowing the characteristics of commits may introduce biases and threats in those studies. This work presents an empirical study to characterize commits in terms of three aspects: categories of activities performed in the commits; co-occurrences of activities in commits; and size of commits by category. We analyzed 1M commits from the 24 most popular and most active Java-based projects hosted in GitHub. The main findings of this work show that: reengineering is the most frequent activity; 30% of commits involve more than one type of activity; the most common co-occurrence of activities in commits is reengineering with forwarding and corrective reengineering, however in a low rate, only 8%. The results of this study should be considered by empirical works to avoid threats and biases when considering commits’ data.
Optimization of stereo calibration parameters for the binocular camera based on improved Beetle Antennae Search algorithm

Abstract For binocular cameras, aiming at the low precision of traditional stereo calibration methods for binocular cameras, a Beetle Antennae Search (BAS) algorithm based on binocular parallel polar line constraint was proposed to improve the calibration accuracy. The internal parameter matrix and distortion parameter matrix of the left and right cameras of the binocular camera were first calculated by using the MATLAB calibration toolbox. Then the stereo calibration parameter matrix of the binocular camera was calculated. Then, using the binocular parallel polar line constraint based on the BAS algorithm establishes the objective function by taking the difference and average values of the vertical coordinates of each checkerboard corner in the left and right images after stereo correction as the objective. The stereo calibration parameters of the binocular camera are further optimized. The results show that the BAS algorithm based on binocular parallel pole constraint is stable, reliable and fast in convergence, which can realize the optimization of stereo calibration parameters of the binocular camera.
Linear Threshold Secret-Sharing with Binary Reconstruction

Motivated in part by applications in lattice-based cryptography, we initiate the study of the size of linear threshold (`t-out-of-n') secret-sharing where the linear reconstruction function is restricted to coefficients in {0,1}. We also study the complexity of such schemes with the additional requirement that the joint distribution of the shares of any unauthorized set of parties is not only independent of the secret, but also uniformly distributed. We prove upper and lower bounds on the share size of such schemes, where the size is measured by the total number of field elements distributed to the parties. We prove our results by defining and investigating an equivalent variant of Karchmer and Wigderson’s Monotone Span Programs [CCC, 1993].One ramification of our results is that a natural variant of Shamir’s classic scheme [Comm. of ACM, 1979], where bit-decomposition is applied to each share, is optimal for when the underlying field has characteristic 2. Another ramification is that schemes obtained from monotone formulae are optimal for certain threshold values when the field’s characteristic is any constant. For schemes with the uniform distribution requirement, we show that they must use Ω(nlog n) field elements, for all thresholds 2 < t < n and regardless of the field. Moreover, this is tight up to constant factors for the special cases where any t = n-1 parties can reconstruct, as well as for any threshold when the field characteristic is 2.
Warping resilient scalable anomaly detection in time series

Time series data is ubiquitous in the real-world problems across various domains including healthcare, social media, and crime surveillance. Detecting anomalies, or irregular and rare events, in time series data, can enable us to find abnormal events in any natural phenomena, which may require special treatment. Moreover, labeled instances of anomaly are hard to get in time series data. On the other hand, time series data, due to its nature, often exhibits localized expansions and compressions in the time dimension which is called warping. These two challenges make it hard to detect anomalies in time series as often such warpings could get detected as anomalies erroneously. Our objective is to build an anomaly detection model that is robust to such warping variations. In this paper, we propose a novel unsupervised time series anomaly detection method, WaRTEm-AD, that operates in two stages. Within the key stage of representation learning, we employ data augmentation through bespoke time series operators which are passed through a twin autoencoder architecture to learn warping-robust representations for time series data. Second, adaptations of state-of-the-art anomaly detection methods are employed on the learnt representations to identify anomalies. We will illustrate that WaRTEm-AD is designed to detect two types of time series anomalies: point and sequence anomalies. We compare WaRTEm-AD with the state-of-the-art baselines and establish the effectiveness of our method both in terms of anomaly detection performance and computational efficiency.
Underwater Mixed Spatial Attention Network

High-quality underwater images play an important role in obtaining and understanding underwater information. However, raw underwater images have many problems, such as low contrast, chromatic aberration, blur and low light, which seriously restrict the progress of other underwater tasks. In this paper, we propose an Underwater Mixed Spatial Attention Network (UMSAN) for underwater image enhancement. The evaluation on the EUVP dataset and some other real underwater images demonstrates that our network is sufficient against several of the advanced models.
Pre-diagnosis Support System for Post Covid-19 Syndrome in Turkey

The objective of this research paper is to develop a fast pre-diagnosis support system to manage the post-acute covid-19 and to facilitate the diagnosis of a highly complicated and yet uncertain disease. The post-acute covid-19 also called “long covid” has been recently defined as a multisystem disease with ongoing signs and symptoms of covid-19 for more than four weeks after acute covid-19 infection. The recovery time for each patient is different but in most of the cases the symptoms disappear under twelve weeks. Even so, it has been registered lately that a significant number of patients have been suffering from ongoing symptoms for more than twelve weeks. There is a wide variety of symptoms and some patients may suffer from only one symptom while others may suffer from more than one at the same time. Besides, it has been discovered that post covid-19 syndrome is most probably not related to the severity of acute covid-19 infection period. Predicting post covid-19 syndrome according to some symptoms or generalizing a diagnosis for different patients is not recommended by specialists. Decision makers must incorporate many other aspects such as patient’s medical history and the nature and severity of symptoms. Decision making in such an uncertain environment is highly difficult. This study procures a Fuzzy Cognitive Maps (FCM) based model to help pre-diagnosis process of post covid-19 syndrome in Turkey. To improve the effectiveness and the robustness of FCM involving experts’ subjective judgements, Hebbian learning algor ithm has been used.
Deep Matrix Factorization for Learning Resources Recommendation

Learning should last all through people’s lives. With traditional learning, learners can meet face-to-face with their teachers or tutors. However, in some circumstances, learners cannot interact with their teachers. Learning resources (e.g., books, journals, slides, etc.) would be helpful for learners to get knowledge. With a large number of learning resources, how to select appropriate learning resources to learn is very important. In this work, a deep matrix factorization model extended from the standard matrix factorization is proposed for learning resources recommendation. We validate the proposed model on five published learning resources datasets and compare it with other well-known methods in recommender systems. The experimental results show that the proposed deep matrix factorization model works well, especially it can be a good choice for large-scale datasets.
Assertion, Rejection, and Semantic Universals

Natural language contains simple lexical items for some but not all Boolean operators. English, for example, contains conjunction and, disjunction or, negated disjunction nor, but no word to express negated conjunction *nand nor any other Boolean connective. Natural language grammar can be described by a logic that expresses what the lexicon can express by its primitives, and the rest compositionally. Such logic for propositional connectives is described here as a bilateral extension of update semantics. The basic intuition is that a context can be updated by assertion or by rejection, and by one or multiple propositions at once. These distinctions suffice to characterize the logic of the lexicon.
Web Security: Emerging Threats and Defense

Web applications have become a widely accepted method to support the internet for the past decade. Since they have been successfully installed in the business activities and there is a requirement of advanced functionalities, the configuration is growing and becoming more complicated. The growing demand and complexity also make these web applications a preferred target for intruders on the internet. Even with the support of security specialists, they remain highly problematic for the complexity of penetration and code reviewing methods. It requires considering different testing patterns in both codes reviewing and penetration testing. As a result, the number of hacked websites is increasing day by day. Most of these vulnerabilities also occur due to incorrect input validation and lack of result validation for lousy programming practices or coding errors. Vulnerability scanners for web applications can detect a few vulnerabilities in a dynamic approach. These are quite easy to use; however, these often miss out on some of the unique critical vulnerabilities in a different and static approach. Although these are time-consuming, they can find complex vulnerabilities and improve developer knowledge in coding and best practices. Many scanners choose both dynamic and static approaches, and the developers can select them based on their requirements and conditions. This research explores and provides details of SQL injection, operating system command injection, path traversal, and cross-site scripting vulnerabilities through dynamic and static approaches. It also examines various security measures in web applications and selected five tools based on their features for scanning PHP, and JAVA code focuses on SQL injection, cross-site scripting, Path Traversal, operating system command. Moreover, this research discusses the approach of a cyber-security tester or a security developer finding out vulnerabilities through dynamic and static approaches using manual and automated web vulnerability scanners.
CTR Prediction Model Using xDeepFM and Bayesian optimization

With the development of internet, increasing people tends to consume online, which is convenient and timesaving. Taobao is the largest online shopping site in China. In this paper, we use the data from Taobao to predict the click-through-rate (CTR), an important metrics that measures the number of clicks advertisers receive on their ads per number of impressions. We introduce xDeepFM model to predict CTR and use Bayesian optimization to optimize. The xDeepFM model is a combined model, composed by DNN and CIN, like Wide&Deep. Based on it, the xDeepFM model enables to catch features of different dimensions: implicit high-order interactions and explicit high-order interactions. In addition, we use the Bayesian optimization to get the optimal hyperparameters. The metrics we used is Auc Roc, and the higher Auc-Roc is, the better performance the model will gain. The Auc Roc of our modle is 0.651 higher 0.031 and 0.012 respectively than LightGBM and DeepFM.
Sentiment Analysis of Drug Reviews using Transfer Learning

With more and more people expressing their opinions on the web, Effective and Accurate Sentiment Analysis of the data is a burgeoning research area in the field of Machine Learning and Natural Language Processing. The proposed research study has built the multiple deep learning-based classification systems. Neural Network based Models with ELMO Word Embeddings as an input are built and compared with various Transformers-based classification such as BERT, XLNET and other variants of the BERT Model. Using the Drug Reviews Dataset which was collated by crawling through online pharmaceuticals review websites, where various pre-trained word embedding models are compared along with transformers and Deep Neural Network Architectures.
Auxiliary Information-Enhanced Recommendations

Sequential recommendations have attracted increasing attention from both academia and industry in recent years. They predict a given user’s next choice of items by mainly modeling the sequential relations over a sequence of the user’s interactions with the items. However, most of the existing sequential recommendation algorithms mainly focus on the sequential dependencies between item IDs within sequences, while ignoring the rich and complex relations embedded in the auxiliary information, such as items’ image information and textual information. Such complex relations can help us better understand users’ preferences towards items, and thus benefit from the recommendations. To bridge this gap, we propose an auxiliary information-enhanced sequential recommendation algorithm called memory fusion network for recommendation (MFN4Rec) to incorporate both items’ image and textual information for sequential recommendations. Accordingly, item IDs, item image information and item textual information are regarded as three modalities. By comprehensively modelling the sequential relations within modalities and interaction relations across modalities, MFN4Rec can learn a more informative representation of users’ preferences for more accurate recommendations. Extensive experiments on two real-world datasets demonstrate the superiority of MFN4Rec over state-of-the-art sequential recommendation algorithms.
Which Gameplay Aspects Impact the Immersion in Virtual Reality Games?

In this paper a comparison of two implementations of the same game is presented: a VR (virtual reality) version and a traditional one, meaning with no VR hardware used. The implementations’ design - made specially for this research - is the result of the analysis of many state-of-the VR games. The comparison’s goal is to find the aspects of gameplay that impact user immersion in a video game. Each extracted aspect is covered by a question in a post experiment questionnaire and discussed before final conclusions are drawn.
Self-training vs Pre-trained Embeddings for Automatic Essay Scoring

People usually believe that using pre-trained word vectors or pre-trained language models can effectively improve task performance. But that is not the case. A sufficient amount of annotated data is usually required to fine-tune the pre-trained language model and pre-trained word vectors for downstream tasks. In addition, the relevance of the training corpus and task corpus also affects task performance to a large extent. In this paper, we systematically compared the effects of different types of pre-trained embeddings and self-training embeddings on the performance of AES. At the same time, we propose an effective solution to the above problem, an automatic essay scoring method that includes pre-trained and self-training word embeddings. We conducted experiments on a public available dataset, including 8 subsets, and the experimental results show the effectiveness of this method.
Optimization of 16-Element Quantum Search on IBMQ

Unstructured searching, which is to find the marked element from a given unstructured data set, is a widely studied problem in computer science. It is well known that Grover algorithm provides a quadratic speedup to solve unstructured search problem compared with the classical algorithm. This algorithm has received a lot of attention due to the strong versatility. In this manuscript, we report experimental results of searching a unique target from 16 elements on five different quantum devices of IBM quantum Experience (IBMQ). We first implement the original Grover algorithm on these devices. However, the experiment probability of success of finding the correct target is almost the same as random choice. We then optimize the quantum circuit size of the search algorithm. The oracle operator and diffusion operator are two of the most costly operators in Grover algorithm. For the 16-element quantum search algorithm, both the oracle operator and diffusion operator consist of a triple controlled [Formula: see text] gate ([Formula: see text]) and some single-qubit gates. So we optimize the implementation of the [Formula: see text] gate according to the qubits layout of different quantum devices. On the ibmq_santiago, the experimental success rate of the 16-element quantum search algorithm is increased to [Formula: see text] by the optimization, which is better than all the published experiments implemented on IBMQ devices. For other IBMQ devices, the experimental success rate of 16-element quantum search also has been significantly improved. We then try to further reduce the size of the quantum circuit by modifying the Grover algorithm, with a tolerable loss of the theoretical success probability. On ibmq_quito, the experimental success rate is further improved from 25.23% to 27.56% after optimization. These experimental results show the importance of circuit optimization and algorithm optimization in the Noisy-Intermediate-Scale Quantum (NISQ) era.
Towards Certifying the Asymmetric Robustness for Neural Networks: Quantification and Applications

One intriguing property of deep neural networks (DNNs) is their vulnerability to adversarial examples – those maliciously crafted inputs that deceive target DNNs. While a plethora of defenses have been proposed to mitigate the threats of adversarial examples, they are often penetrated or circumvented by even stronger attacks. To end the constant arms race between attackers and defenders, significant efforts have been devoted to providing certifiable robustness bounds for DNNs, which ensures that for a given input its vicinity does not admit any adversarial instances. Yet, most prior works focus on the case of symmetric vicinities (e.g., a hyperrectangle centered at a given input), while ignoring the inherent heterogeneity of perturbation direction (e.g., the input is more vulnerable along a particular perturbation direction). To bridge the gap, in this article, we propose the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">asymmetric robustness</i> to account for the inherent heterogeneity of perturbation directions, and present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Amoeba</monospace> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , an efficient certification framework for asymmetric robustness. Through extensive empirical evaluation on state-of-the-art DNNs and benchmark datasets, we show that compared with its symmetric counterpart, the asymmetric robustness bound of a given input describes its local geometric properties in a more precise manner, which enables use cases including (i) modeling stronger adversarial threats, (ii) interpreting DNN predictions, and makes it a more practical definition of certifiable robustness for security-sensitive domains.
Cluster’s Number Free Bayes Prediction of General Framework on Mixture of Regression Models

Abstract Prediction based on a single linear regression model is one of the most common way in various field of studies. It enables us to understand the structure of data, but might not be suitable to express the data whose structure is complex. To express the structure of data more accurately, we make assumption that the data can be divided in clusters, and has a linear regression model in each cluster. In this case, we can assume that each explanatory variable has their own role; explaining the assignment to the clusters, explaining the regression to the target variable, or being both of them. Introducing probabilistic structure to the data generating process, we derive the optimal prediction under Bayes criterion and the algorithm which calculates it sub-optimally with variational inference method. One of the advantages of our algorithm is that it automatically weights the probabilities of being each number of clusters in the process of the algorithm, therefore it solves the concern about selection of the number of clusters. Some experiments are performed on both synthetic and real data to demonstrate the above advantages and to discover some behaviors and tendencies of the algorithm.