Reviewerly/exHarmony huggingface dataset:
MWSR-YLCA: Improved YOLOv7 Embedded with Attention Mechanism for Nasopharyngeal Carcinoma Detection from MR Images

Nasopharyngeal carcinoma (NPC) is a malignant tumor, and early diagnosis and timely treatment are important for NPC patients. Accurate and reliable detection of NPC lesions in magnetic resonance (MR) images is very helpful for the disease diagnosis. However, recent deep learning methods need to be improved for NPC detection in MR images. Because NPC tumors are invasive and usually small in size, it is difficult to distinguish NPC tumors from the closely connected surrounding tissues in a huge and complex background. In this paper, we propose an automatic detection method, named MWSR-YLCA, to accurately detect NPC lesions in MR images. Specifically, we design two modules, the multi-window settings resampling (MWSR) module and an improved YOLOv7 embedded with a coordinate attention mechanism (YLCA) module, to detect NPC lesions more accurately. First, the MWSR generates a pseudo-color version of MR images based on a multi-window resampling method, which preserves richer information. Subsequently, the YLCA detects the NPC lesion areas more accurately by constructing a novel network based on an improved YOLOv7 framework embedded with the coordinate attention mechanism. The proposed method was validated on an MR image set of 800 NPC patients and obtained 80.1% mAP detection performance with only 4694 data samples. The experimental results show that the proposed MWSR-YLCA method can perform high-accuracy detection of NPC lesions and has superior performance.
Modeling Noncommutative Composition of Relations for Knowledge Graph Embedding

Knowledge Graph Embedding (KGE) is a powerful way to express Knowledge Graphs (KGs), which can help machines learn patterns hidden in the KGs. Relation patterns are useful hidden patterns, and they usually assist machines to predict unseen facts. Many existing KGE approaches can model some common relation patterns like symmetry/antisymmetry, inversion, and commutative composition patterns. However, most of them are weak in modeling noncommutative composition patterns. It means these approaches can not distinguish a lot of composite relations like “father’s mother” and “mother’s father”. In this work, we propose a new KGE method called QuatRotatScalE (QRSE) to overcome this weakness, since it utilizes rotation and scaling transformations of quaternions to design the relation embedding. Specifically, we embed the relations and entities into a quaternion vector space under the difference norm KGE framework. Since the multiplication of quaternions does not satisfy the commutative law, QRSE can model noncommutative composition patterns naturally. The experimental results on the synthetic dataset also support that QRSE has this ability. In addition, the experimental results on real-world datasets show that QRSE reaches state-of-the-art in link prediction problem.
Image Adversarial Example Generation Method Based on Adaptive Parameter Adjustable Differential Evolution

Adversarial example generation techniques for neural network models have exploded in recent years. In the adversarial attack scheme for image recognition models, it is challenging to achieve a high attack success rate with very few pixel modifications. To address this issue, this paper proposes an adversarial example generation method based on adaptive parameter adjustable differential evolution. The method realizes the dynamic adjustment of the algorithm performance by adjusting the control parameters and operation strategies of the adaptive differential evolution algorithm, while searching for the optimal perturbation. Finally, the method generates adversarial examples with a high success rate, modifying just a very few pixels. The attack effectiveness of the method is confirmed in CIFAR10 and MNIST datasets. The experimental results show that our method has a greater attack success rate than the One Pixel Attack based on the conventional differential evolution. In addition, it requires significantly less perturbation to be successful compared to global or local perturbation attacks, and is more resistant to perception and detection.
Domain Word Extension Using Curriculum Learning

Self-supervised learning models, such as BERT, have improved the performance of various tasks in natural language processing. Although the effect is reduced in the out-of-domain field and not the the trained domain thus representing a limitation, it is difficult to train a new language model for a specific domain since it is both time-consuming and requires large amounts of data. We propose a method to quickly and effectively apply the pre-trained language models trained in the general domain to a specific domain's vocabulary without re-training. An extended vocabulary list is obtained by extracting a meaningful wordpiece from the training data of the downstream task. We introduce curriculum learning, training the models with two successive updates, to adapt the embedding value of the new vocabulary. It is convenient to apply because all training of the models for downstream tasks are performed in one run. To confirm the effectiveness of the proposed method, we conducted experiments on AIDA-SC, AIDA-FC, and KLUE-TC, which are Korean classification tasks, and subsequently achieved stable performance improvement.
Task Scheduling Based on Adaptive Priority Experience Replay on Cloud Platforms

Task scheduling algorithms based on reinforce learning (RL) have been important methods with which to improve the performance of cloud platforms; however, due to the dynamics and complexity of the cloud environment, the action space has a very high dimension. This not only makes agent training difficult but also affects scheduling performance. In order to guide an agent’s behavior and reduce the number of episodes by using historical records, a task scheduling algorithm based on adaptive priority experience replay (APER) is proposed. APER uses performance metrics as scheduling and sampling optimization objectives with which to improve network accuracy. Combined with prioritized experience replay (PER), an agent can decide how to use experiences. Moreover, this algorithm also considers whether a subtask is executed in a workflow to improve scheduling efficiency. Experimental results on Tpc-h, Alibaba cluster data, and scientific workflows show that a model with APER has significant benefits in terms of convergence and performance.
Attentional Interactive Encoder Network Focused on Aspect for Sentiment Classification

Aspect-based sentiment analysis (ABSA) plays a significant role in the field of big data and aims to distinguish the sentiment polarity of specific aspects in given sentences; however, the previous works on ABSA had two limitations. They mainly considered semantic features, rather than syntactic dependency features, and paid too much attention to the context words, while ignoring the high-level interaction of multiple representations of aspects themselves. To cope with these limitations, we propose a new method based on the graph convolutional network (GCN) and the multi-head attention mechanism, called the attention interactive encoder network (AIEN). The GCN was used to obtain the syntactic information that has the greatest syntactic impact on the aspect based on the syntax dependency tree. The multi-head attention mechanism can not only obtain the context-aware information of the given aspects, but also the interaction information between multiple representations of the aspect itself. The high-level information generated by the interaction of multi-dimensional features can produce a stronger representation ability for the aspect. Our experiments with the proposed model on five benchmark datasets showed that our model outperformed other works significantly. The experimental results further demonstrated the feasibility and applicability of our proposed model in the ABSA task.
A Blind Image Quality Index for Synthetic and Authentic Distortions with Hierarchical Feature Fusion

Blind Image Quality Assessment (BIQA) for synthetic and authentic distortions has attracted much attention in the community, and it is still a great challenge. The existing quality metrics are mildly consistent with subjective perception. Traditional handcrafted quality metrics can easily and directly extract low-level features, which mainly account for the outline, edge, color, texture, and shape features, while ignoring the important deep semantics of the distorted image. In the field of popular deep learning, multilevel features can be acquired easily. However, most of them either use only high-level features, ignoring the shallow features, or they simply combine features at different levels, resulting in limited prediction performance. Motivated by these, this paper presents a novel BIQA for synthetic and authentic distortions with hierarchical feature fusion in a flexible vision-Transformer framework. First, multiscale features are extracted from a strong vision-Transformer backbone. Second, an effective hierarchical feature fusion module is proposed to incorporate the features at different levels progressively. To eliminate redundant information, a simple but effective attention mechanism is employed after each fusion. Third, inspired by the human visual system, local and global features from the fusion features are extracted to represent different granularity distortions. Finally, these local and global features are mapped to the final quality score. Extensive experiments on three authentic image databases and two synthetic image datasets show that the proposed method is superior to the state-of-the-art quality metrics for both single-database testing and cross-database testing.
Word Game Modeling Using Character-Level N-Gram and Statistics

Word games are one of the most essential factors of vocabulary learning and matching letters to form words for children aged 5–12. These games help children to improve letter and word recognition, memory-building, and vocabulary retention skills. Since Uzbek is a low-resource language, there has not been enough research into designing word games for the Uzbek language. In this paper, we develop two models for designing the cubic-letter game, also known as the matching-letter game, in the Uzbek language, consisting of a predefined number of cubes, with a letter on each side of each six-sided cube, and word cards to form words using a combination of the cubes. More precisely, we provide the opportunity to form as many words as possible from the dataset, while minimizing the number of cubes. The proposed methods were created using a combination of a character-level n-gram model and letter position frequency in words at the level of vowels and consonants. To perform the experiments, a novel dataset, consisting of 4.5 k 3–5 letter words, was created by filtering based on child age groups for the Uzbek language, and three more datasets were generated, based on the support of experts for the Russian, English, and Slovenian languages. Experimental evaluations showed that both models achieved good results in terms of average coverage. In particular, the Vowel Priority (VL) approach obtained reasonably high coverage with 95.9% in Uzbek, 96.8% in English, and 94.2% in the Slovenian language in the case of eight cubes, based on the five-fold cross-validation method. Both models covered around 85% of five letter words in Uzbek, English, and Slovenian datasets, while this coverage was even higher (99%) in three letter words in the case of eight cubes.
Indoor Signs Detection for Visually Impaired People: Navigation Assistance Based on a Lightweight Anchor-Free Object Detector

Facilitating the navigation of visually impaired people in indoor environments requires detecting indicating signs and informing them. In this paper, we proposed an indoor sign detection based on a lightweight anchor-free object detection model called FAM-centerNet. The baseline model of this work is the centerNet, which is an anchor-free object detection model with high performance and low computation complexity. A Foreground Attention Module (FAM) was introduced to extract target objects in real scenes with complex backgrounds. This module segments the foreground to extract relevant features of the target object using midground proposal and boxes-induced segmentation. In addition, the foreground module provides scale information to improve the regression performance. Extensive experiments on two datasets prove the efficiency of the proposed model for detecting general objects and custom indoor signs. The Pascal VOC dataset was used to test the performance of the proposed model for detecting general objects, and a custom dataset was used for evaluating the performance in detecting indoor signs. The reported results have proved the efficiency of the proposed FAM in enhancing the performance of the baseline model.
Too civil to care? How online hate speech against different social groups affects bystander intervention

A large share of online users has already witnessed online hate speech. Because targets tend to interpret such bystanders’ lack of reaction as agreement with the hate speech, bystander intervention in online hate speech is crucial as it can help alleviate negative consequences. Despite evidence regarding online bystander intervention, however, whether bystanders evaluate online hate speech targeting different social groups as equally uncivil and, thereby, equally worthy of intervention remains largely unclear. Thus, we conducted an online experiment systematically varying the type of online hate speech as homophobia, racism, and misogyny. The results demonstrate that, although all three forms were perceived as uncivil, homophobic hate speech was perceived to be less uncivil than hate speech against women. Consequently, misogynist hate speech, compared to homophobic hate speech, increased feelings of personal responsibility and, in turn, boosted willingness to confront.
A few-shot semantic segmentation method based on adaptively mining correlation network

Abstract The goal of few-shot semantic segmentation is to learn a segmentation model that can segment novel classes in queries when only a few annotated support examples are available. Due to large intra-class variations, the building of accurate semantic correlation remains a challenging job. Current methods typically use 4D kernels to learn the semantic correlation of feature maps. However, they still face the challenge of reducing the consumption of computation and memory while keeping the availability of correlations mined by their methods. In this paper, we propose the adaptively mining correlation network (AMCNet) to alleviate the aforementioned issues. The key points of AMCNet are the proposed adaptive separable 4D kernel and the learnable pyramid correlation module, which form the basic block for correlation encoder and provide a learnable concatenation operation over pyramid correlation tensors, respectively. Experiments on the PASCAL VOC 2012 dataset show that our AMCNet surpasses the state-of-the-art method by $0.7\%$ and $2.2\%$ on 1-shot and 5-shot segmentation scenarios, respectively.
Rotational abstractions for verification of quantum Fourier transform circuits

With the race to build large-scale quantum computers and efforts to exploit quantum algorithms for efficient problem solving in science and engineering disciplines, the requirement to have efficient and scalable verification methods are of vital importance. We propose a novel formal verification method that is targeted at Quantum Fourier Transform (QFT) circuits. QFT is a fundamental quantum algorithm that forms the basis of many quantum computing applications. The verification method employs abstractions of quantum gates used in QFT that leads to a reduction of the verification problem from Hilbert space to the quantifier free logic of bit-vectors. Very efficient decision procedures are available to reason about bit-vectors. Therefore, our method is able to scale up to the verification of QFT circuits with 10,000 qubits and 50 million quantum gates, providing a meteoric advance in the size of QFT circuits thus far verified using formal verification methods.
A Statistical Model for Estimating the Amount of Monthly Global Radiation in the Horizontal Plane

In addition to dynamic methods, purely statistical models, i.e., findings from the statistical analysis of the existing measured database, also play an important role in predicting the different characteristics of climate elements. In our article, we try to estimate the monthly amount of global radiation in each day of the month. In our previous articles, we presented the sliding-average model developed for estimating the average or amount of a climatic element, measured over a time interval, from within the interval. A version of this model for estimating the end-of-interval sums, the sliding-sum model, was used to estimate the amount of monthly global radiation. After generating the characteristics required for the estimation and analyzing their properties, we examined the errors of the performed estimation. Our model can also help solar energy users create the schedule.
Adversarial Attacks on Network Intrusion Detection Systems Using Flow Containers

Abstract This paper studies adversarial attacks on network intrusion detection systems (IDSs) based on deep or machine learning algorithms. Adversarial attacks on network IDSs must maintain the functional logic of the attack flow. To prevent the produced adversarial examples from violating the attack behavior, most solutions define some limited modification actions. The result limits the production of adversarial examples, and the produced adversarial examples are not guaranteed to find the attack packets. This paper proposes the concept of flow containers to model packets in a flow. Then, we propose a generative adversarial network framework with dual adversarial training to train the generator to produce adversarial flow containers. Flow containers can correlate attack packets and feature vectors of attack flows. We test the evasion rate of the produced adversarial examples using 12 deep and machine learning algorithms. For experiments on the CTU42 data set, the proposed adversarial examples have the highest evasion rates among all 12 classifiers, with the highest evasion rate as high as 1.00. For experiments on the CIC-IDS2017 data set, the proposed adversarial examples have the highest evasion rate among the five classifiers, and the highest evasion rate is also up to 1.00.
A New Fuzzy Smoothing Term Model For Stereo Matching

Abstract In this study, we construct a smoothing term structure, which is an essential part of the energy function in binocular matching. However, the existing energy models are mainly deterministic, which cannot adapt to processing low-quality images, especially when there exists a large proportion of vague areas. In order to perform better in processing these low-quality images, in this paper, we construct the smoothing term based on a fuzzy model, which includes fuzzy segmentation, the fuzzy network between the superpixels and the fuzzy relationship between the pixels. These can be compatible with the uncertainty in the image. In addition, to explain the rationality of the calculation of the degree of correlation between superpixels and further elaborate on the property of these degrees between each superpixel, we propose five corresponding theorems with proofs. After we solve the energy model combined with our proposed smoothing term, we compare our disparity results with the corresponding deterministic model and several state-of-the-art algorithms in the experiment. The results verify the effectiveness of the proposed algorithm.
Social Interventions to Encourage Co-Located Collaborative Learning in a Tangible User Interface

Abstract Technologies that support co-located group activity should not only provide a shared workspace, but they should also encourage collaboration. For this, it is necessary to know the structure of the group and how the members of the group work in order to achieve a common goal. In this paper, an observational study of groups of students interacting in a tangible user interface was performed. This study allowed us to identify social interactions and phenomena that can affect the collaboration and performance of the group. These phenomena can be for instance that some members of the group may contribute to the activity, while others may not. In order to minimize these phenomena in a co-located group activity, we propose interactions, which we call social interventions, that might be used by a coaching system to encourage group collaboration in co-located settings.
Git command recommendations using crowd-sourced knowledge

Git is a fast, scalable, distributed version control system with a rich command set that provides high-level operations and full access to the internals. It has been widely used by millions of developers worldwide. However, due to the flexibility of the usage of Git commands and the scarcity of Git documentation, many developers have experienced difficulties when using Git commands. This paper aims to propose an automatic approach to recommending Git commands for developers given a query described by natural language. Our approach makes recommendations by mining the crowd-sourced knowledge related to Git on Stack Overflow. It first constructs a keyword-command mapping database from Git-related posts, then analyzes the similarity between the query given by the developer and the keywords in the database to retrieve the candidate commands, and proposes an algorithm to rank the candidate commands. Our approach’s recommendation results significantly outperform the baseline approaches in several metrics (e.g., Top-K accuracy). Meanwhile, the experimental results have shown that the favorable efficiency of our approach can promise its use by developers in real-world scenarios. The Git command recommendation approach proposed in this paper is effective and can be helpful for developers to use Git commands for more efficient development.
A systematic literature review on Android-specific smells

Code smells are well-known concepts in Object-Oriented (OO) programs as symptoms that negatively impact software quality and cause long-term issues. However, the domain-specific smells in Android have not yet been investigated well. Android smells often refer to the misuse of mobile SDK and causes of performance, accessibility, and efficiency issues that end-users can perceive. This study aims to provide a clear overview of state-of-the-art techniques for addressing Android-specific code smells to understand existing methods and open challenges, which help the community understand the significance of Android smells and the current status of research. We conducted a Systematic Literature Review of 4,820 distinct papers published until 2021, following a consolidated methodology applied in software engineering. 35 primary studies were selected. The known Android smells cannot be treated equally in the proposed approaches, as they mainly focus on detecting performance-related smells. The proposed approaches capture various features to detect smell instances using different analysis techniques in Android applications. In addition, the Android community continuously identifies new types of smells to improve apps' quality. The research community still encounters several challenges. Thus, this paper outlines various directions for the necessary investigation as future work.