Reviewerly/exHarmony huggingface dataset:
Online Batch Selection for Enhanced Generalization in Imbalanced Datasets

Importance sampling, a variant of online sampling, is often used in neural network training to improve the learning process, and, in particular, the convergence speed of the model. We study, here, the performance of a set of batch selection algorithms, namely, online sampling algorithms that process small parts of the dataset at each iteration. Convergence is accelerated through the creation of a bias towards the learning of hard samples. We first consider the baseline algorithm and investigate its performance in terms of convergence speed and generalization efficiency. The latter, however, is limited in case of poor balancing of data sets. To alleviate this shortcoming, we propose two variations of the algorithm that achieve better generalization and also manage to not undermine the convergence speed boost offered by the original algorithm. Various data transformation techniques were tested in conjunction with the proposed scheme to develop an overall training method of the model and to ensure robustness in different training environments. An experimental framework was constructed using three naturally imbalanced datasets and one artificially imbalanced one. The results assess the advantage in convergence of the extended algorithm over the vanilla one, but, mostly, show better generalization performance in imbalanced data environments.
Analysis of Real-Time Face-Verification Methods for Surveillance Applications

In the last decade, face-recognition and -verification methods based on deep learning have increasingly used deeper and more complex architectures to obtain state-of-the-art (SOTA) accuracy. Hence, these architectures are limited to powerful devices that can handle heavy computational resources. Conversely, lightweight and efficient methods have recently been proposed to achieve real-time performance on limited devices and embedded systems. However, real-time face-verification methods struggle with problems usually solved by their heavy counterparts-for example, illumination changes, occlusions, face rotation, and distance to the subject. These challenges are strongly related to surveillance applications that deal with low-resolution face images under unconstrained conditions. Therefore, this paper compares three SOTA real-time face-verification methods for coping with specific problems in surveillance applications. To this end, we created an evaluation subset from two available datasets consisting of 3000 face images presenting face rotation and low-resolution problems. We defined five groups of face rotation with five levels of resolutions that can appear in common surveillance scenarios. With our evaluation subset, we methodically evaluated the face-verification accuracy of MobileFaceNet, EfficientNet-B0, and GhostNet. Furthermore, we also evaluated them with conventional datasets, such as Cross-Pose LFW and QMUL-SurvFace. When examining the experimental results of the three mentioned datasets, we found that EfficientNet-B0 could deal with both surveillance problems, but MobileFaceNet was better at handling extreme face rotation over 80 degrees.
Fake review identification and utility evaluation model using machine learning

Due to the structural growth of e-commerce platforms, the frequency of exchange of opinions and the number of online reviews of platform participants related to products are increasing. However, given the growth of fake reviews, the corresponding growth in the quality of online reviews seems to be slow, at best. The number of cases of harm to retailers and customers caused by malicious false reviews is steadily increasing every year. In this context, it is becoming difficult for users to determine useful reviews amid a flood of information. As a result, the intrinsic value of online reviews that reduce uncertainty in pre-purchase decisions is blurred, and e-commerce platforms are on the verge of losing credibility and traffic. Through this study, we intend to present solutions related to review filtering and classification by constructing a model for judging the authenticity and usefulness of online reviews using machine learning.
El-Rec: Enhanced User and News Interaction for News Recommendation

With the development of the Internet, the amount of information data on the Web is growing exponentially. Recommender systems are widely used in online news services. The existing methods usually extract features from news content and historical user behavior and predict news of interest to users. However, these methods only use dot-product to interact news features with user features, making the recommendation system tend to recommend news that is similar to historical news. In fact, this dot-product approach is based only on interest and ignores some biases of the candidate news itself, such as the fact that breaking news is always more eye-catching. To resolve the problem, in this paper we proposed an Enhanced User and News Interaction modeling for News Recommendation. In our method, the click probability is predicted by a gated aggregator which aggregates the news and user representation adaptively. We evaluate El-Rec on two standard news datasets, i.e., MIND-large and MIND-small. The resulting model significantly outperforms previous methods and achieves new state-of-the-art results.
Object Detection Algorithm Based on Channel-Spatial Fusion Cascade Attention

To solve the problem of low accuracy and high leakage rate of small object detection in a one-stage object detection model, a cascade attention mechanism (CAtt) is proposed. Cascading attention integrates channel attention and spital attention mechanisms that can be flexibly embedded into any object detection model, such as YoloV4, to improve the performance of the object detection model. The attention mechanism is perfected from two aspects: one is to integrate channel attention and spatial attention respectively so that the two are more complementary. Secondly, by introducing the skipping connection mechanism, the shallow detail information is connected with the deep semantic information, to solve the problem of information loss caused by the multiple convolutions of the model. Four typical attention mechanisms were selected to compare public datasets such as PASCAL VOC2007 and PASCAL VOC2012. A large number of experimental results show that this method significantly improves the performance of the object detection model, especially for the PASCAL VOC2007. Embedding the method in YoloV4 resulted in a 4.77% increase in the mAP index of the original model.
Scientific Paper Classification Based on Graph Neural Network with Hypergraph Self-attention Mechanism

The number of scientific papers has increased rapidly in recent years. How to make good use of scientific papers for research is very important. Through the high-quality classification of scientific papers, researchers can quickly find the resource content they need from the massive scientific resources. The classification of scientific papers will effectively help researchers filter redundant information, obtain search results quickly and accurately, and improve the search quality, which is necessary for scientific resource management. This paper proposed a science-technique paper classification method based on hypergraph neural network(SPHNN). In the heterogeneous information network of scientific papers, the repeated high-order subgraphs are modeled as hyperedges composed of multiple related nodes. Then the whole heterogeneous information network is transformed into a hypergraph composed of different hyperedges. The graph convolution operation is carried out on the hypergraph structure, and the hyperedges self-attention mechanism is introduced to aggregate different types of nodes in the hypergraph, so that the final node representation can effectively maintain high-order nearest neighbor relationships and complex semantic information. Finally, by comparing with other methods, we proved that the model proposed in this paper has improved its performance.
Remolding Semantic Focus with Dual Attention Mechanism for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is an NLP task that classify fine-grained sentiment towards one specific aspect from the same text. While attention mechanism has achieved great success, attaching aspects to abstract sentiment remains challenging. In this paper, we propose dual attention mechanism, a novel method to re-weight the distribution of attention between stack BERT layers, in prompt learning way with pretrained language model BERT. Specifically, after obtaining the most attractive words, the method raises weight of other possible corresponding words and makes model consider more comprehensively. To introduce more aspect information, we classify the sentiment in improved prompt learning way. Note that the overfitting using BERT on ABSA, we utilize the approach of staged loss that restrict the training not too small. Finally, the experiment results demonstrate the effectiveness and the stability of dual attention and provide a good insight of attention mechanism.
Deep Learning Techniques for Breast Cancer Mitotic Cell Detection

Breast cancer is one of the highest incidence in women's cancer, The pathological diagnosis of breast cancer can be used to evaluate the invasion of tumors and provide important information for accurate diagnosis and treatment. Statistics the number of mitosis cells in breast cancer is one of the important indicators of breast cancer division. In this paper, we summarized the current mainstream methods of mitosis cells detection. These methods are mainly implemented based on deep learning, and discussing the results of some of the methods, comparison and evaluation. At last, through the review of the research methods in this field, the existing breast cancer research methods have been summarized, and the future developments are prospected.
Cascaded ASPP and Attention Mechanism-based Deeplabv3+ Semantic Segmentation Model

Deeplabv3+ is a standard semantic segmentation model, which adds decoding structure to recover spatial information of the image and uses the Atrous Spatial Pyramid Pooling (ASPP) module to solve the multi-scale problem of the image. However, the Deeplabv3+ model has some drawbacks regarding restoring details. Therefore, we propose the CB_Deeplabv3+ model. In the encoding structure of the CB_Deeplabv3+ model, we use ASPP modules cascaded in parallel to extend the network structure and enable the model to capture richer context information by increasing the information interaction between channels. At the same time, CB_Deeplabv3+ introduced the Convolutional Block Attention Module(CBAM) to solve the long-distance dependence problem in the encoding-decoding structure. Experimental evaluation results on the Part_VOC dataset show that CB_Deeplabv3+ achieves excellent performance for semantic segmentation.
Post-Pandemic Follow-Up Audit of Security Checkpoints

This paper provides a follow-up audit of security checkpoints (or simply checkpoints) for mass transportation hubs such as airports and seaports aiming at the post-pandemic R&D adjustments. The goal of our study is to determine biometric-enabled resources of checkpoints for a counter-epidemic response. To achieve the follow-up audit goals, we embedded the checkpoint into the Emergency Management Cycle (EMC) – the core of any doctrine that challenges disaster. This embedding helps to identify the technology-societal gaps between contemporary and post-pandemic checkpoints. Our study advocates a conceptual exploration of the problem using EMC profiling and formulates new tasks for checkpoints based on the COVID-19 pandemic lessons learned. In order to increase practical value, we chose a case study of face biometrics for an experimental post-pandemic follow-up audit.
Path Planning Considering Time-Varying and Uncertain Movement Speed in Multi-Robot Automatic Warehouses: Problem Formulation and Algorithm

Path planning in the multi-robot system refers to calculating a set of actions for each robot, which will move each robot to its goal without conflicting with other robots. Lately, the research topic has received significant attention for its extensive applications, such as airport ground, drone swarms, and automatic warehouses. Despite these available research results, most of the existing investigations are concerned with the cases of robots with a fixed movement speed without considering uncertainty. Therefore, in this work, we study the problem of path-planning in the multi-robot automatic warehouse context, which considers the time-varying and uncertain robots’ movement speed. Specifically, the path-planning module searches a path with as few conflicts as possible for a single agent by calculating traffic cost based on customarily distributed conflict probability and combining it with the classic $\mathrm{A}^{*}$ algorithm. However, this probability-based method cannot eliminate all conflicts, and speed’s uncertainty will constantly cause new conflicts. As a supplement, we propose the other two modules. The conflict detection and re-planning module chooses objects requiring re-planning paths from the agents involved in different types of conflicts periodically by our designed rules. Also, at each step, the scheduling module fills up the agent’s preserved queue and decides who has a higher priority when the same element is assigned to two agents simultaneously. Finally, we compare the proposed algorithm with other algorithms from academia and industry, and the results show that the proposed method is validated as the best performance.
Supramodal Representation of the Sense of Body Ownership in the Human Parieto-Premotor and Extrastriate Cortices

The sense of body ownership, defined as the sensation that one's body belongs to oneself, is a fundamental component of bodily self-consciousness. Several studies have shown the importance of multisensory integration for the emergence of the sense of body ownership, together with the involvement of the parieto-premotor and extrastriate cortices in bodily awareness. However, whether the sense of body ownership elicited by different sources of signal, especially visuotactile and visuomotor inputs, is represented by common neural patterns remains to be elucidated. We used functional magnetic resonance imaging (fMRI) to investigate the existence of neural correlates of the sense of body ownership independent of the sensory modalities. Participants received tactile stimulation or executed finger movements while given synchronous and asynchronous visual feedback of their hand. We used multivoxel patterns analysis (MVPA) to decode the synchronous and asynchronous conditions with cross-classification between two modalities: the classifier was first trained in the visuotactile sessions and then tested in the visuomotor sessions, and vice versa. Regions of interest (ROIs)-based and searchlight analyses revealed significant above-chance cross-classification accuracies in the bilateral intraparietal sulcus (IPS), the bilateral ventral premotor cortex (PMv), and the left extrastriate body area (EBA). Moreover, we observed a significant positive correlation between the cross-classification accuracy in the left PMv and the difference in subjective ratings of the sense of body ownership between the synchronous and asynchronous conditions. Our findings revealed the neural representations of the sense of body ownership in the IPS, PMv, and EBA that is invariant to the sensory modalities.
Domain-Invariant Feature Learning for Domain Adaptation

Unsupervised domain adaptation (UDA) mainly explores how to learn domain-invariant features from the source domain when the target domain label is unknown. To learn domain-invariant features requires aligning the distribution of samples from two domains in the feature space, which can be achieved by minimizing the maximum mean discrepancy (MMD) of samples from the two domains. However, there is still no effective way to find the best parameter values of MMD. Such a problem is addressed in the MMD with deep kernels (MMD-D), whose optimal parameters can be obtained through training. This study proposes a method of domain-invariant feature learning for UDA, whose architecture, named MMDDCDA, comprises an MMD-D module and a cross domain adaptation (CDA) module. MMDDCDA performs alternating training similar to adversarial training to alternately boost the power of the two modules. To our knowledge, this is the first UDA method that performs such alternating training on a UDA architecture using MMD with deep kernels. Experimental validation showed that the proposed method yields state-of-the-art results among UDA methods using other MMD variants and some UDA benchmarks.
A Model of a Parallel Design Environment for the Development of Decision-Making IoT Systems

The aim of the article is to present a model of building a parallel design environment using containerization processes for Internet of Things (IoT) systems supported by local environments and their orchestration. According to the currently dominant approach in IT, which is the maximum automation of the software development process, special emphasis is placed on the processes of Continuous Integration and Continuous Delivery (CI / CD). They are supported by such solutions as Docker Compose, Swarm and Pipeline. CI / CD is a response to such common problems related to software development as slow product growth, lack of transparency in the cooperation of the development team, lack of predictability and the possibility of estimating project duration, late implementation of changes, repeatable errors caused by the human factor and many others. These challenges translate into the final product and customer satisfaction, and thus the success of the project or even the financial result of the enterprise. Although Continuous Integration and Delivery are not new concepts (the first mention of Continuous Integration dates back to 1991), thanks to the continuous improvement of these methods, many tools and new approaches are created. Therefore, in this article, the authors consider the research problems of building a decision-making system using Continuous Integration and Continuous Delivery processes. For this purpose, a model of the decision-making process was built using both of the above-mentioned processes and then it was verified in a parallel design environment.
DRGS: Low-Precision Full Quantization of Deep Neural Network with Dynamic Rounding and Gradient Scaling for Object Detection

To improve the inference accuracy of neural networks, their size and complexity are growing rapidly, making the deployment of complex task models on mobile devices with efficient inference a major challenge for industry today. Low-precision quantization is one of the key methods to achieve efficient inference on complex networks, but previous works often quantize partial layers because severe accuracy degradation occurs when quantizing is applied to the entire network. In order to improve the stability and accuracy of low-precision quantization-fine-tuning, we propose a hardware-friendly low-precision full quantization method, called DRGS, which dynamically selects rounding mode for weights according to the direction of weight updates during the training forward and scales the corresponding gradient, finally completing the quantization of all layers of the complex network to achieve floating-free-inference. To validate the effectiveness of DRGS, we apply it to RetinaNet with full 4-bit quantization, and the result of the MS-COCO dataset shows that DRGS has a 2.1% improvement in mAP or at least 2X less quantization loss compared to the state of art implementation. This improvement is also significant even on the YOLO, an object detection model family known for run-time low latency and efficiency. In the latest version of YOLO-v5s, the 4-bit fully quantized network reaches mAP 33.4 which to our knowledge is the best mAP achieved at this category.
Pseudorandom Correlation Functions from Variable-Density LPN, Revisited

Pseudorandom correlation functions (PCF), introduced in the work of (Boyle et al., FOCS 2020), allow two parties to locally generate, from short correlated keys, a near-unbounded amount of pseudorandom samples from a target correlation. PCF is an extremely appealing primitive in secure computation, where they allow to confine all preprocessing phases of all future computations two parties could want to execute to a single short interaction with low communication and computation, followed solely by offline computations. Beyond introducing the notion, Boyle et al. gave a candidate construction, using a new variable-density variant of the learning parity with noise (LPN) assumption. Then, to provide support for this new assumption, the authors showed that it provably resists a large class of linear attacks, which captures in particular all known attacks on LPN. In this work, we revisit the analysis of the VDLPN assumption. We make two key contributions: Our parameters set leads to PCFs with keys around 3 MB allowing $$\sim 500$$ evaluations per second on one core of a standard laptop for 110 bits of security; these numbers can be improved to 350 kB keys and $$\sim 3950$$ evaluations/s using a more aggressive all-prefix variant. All numbers are quite tight: only within a factor 3 of the best bounds one could heuristically hope for.
Ranking coherence in topic models using statistically validated networks

Probabilistic topic models have become one of the most widespread machine learning techniques in textual analysis. Topic discovering is an unsupervised process that does not guarantee the interpretability of its output. Hence, the automatic evaluation of topic coherence has attracted the interest of many researchers over the last decade, and it is an open research area. This article offers a new quality evaluation method based on statistically validated networks (SVNs). The proposed probabilistic approach consists of representing each topic as a weighted network of its most probable words. The presence of a link between each pair of words is assessed by statistically validating their co-occurrence in sentences against the null hypothesis of random co-occurrence. The proposed method allows one to distinguish between high-quality and low-quality topics, by making use of a battery of statistical tests. The statistically significant pairwise associations of words represented by the links in the SVN might reasonably be expected to be strictly related to the semantic coherence and interpretability of a topic. Therefore, the more connected the network, the more coherent the topic in question. We demonstrate the effectiveness of the method through an analysis of a real text corpus, which shows that the proposed measure is more correlated with human judgement than the state-of-the-art coherence measures.
Handling support cycles and collective interactions in the logical encoding of higher-order bipolar argumentation frameworks

Abstract In our paper [17], we have proposed a logical encoding of argumentation frameworks with higher-order interactions (i.e. attacks or supports whose targets are arguments or other attacks or supports) with an evidential interpretation for supports, such frameworks are called REBAF. With this encoding, we are able to characterize the semantics of REBAF under the form of specific logical models. Nevertheless, this encoding has two important drawbacks: first, the handling of support cycles has some weaknesses, and second, the collective interactions (i.e. attacks or supports whose source is a set of arguments and not only one argument) are not taken into account. The present paper proposes an improvement of this encoding for solving these drawbacks. Moreover, we also extend this proposal to another kind of Higher-Order Bipolar Argumentation Framework, the RAFN, in which supports are interpreted as necessity.