Reviewerly/exHarmony huggingface dataset:
Hybrid Recommender Sytem Combined Sentiment Analysis with Incremental Algorithm

Beside the problem how to improve hybrid system combine sentiment analysis, developing incremental algorithms become an interesting research in real-data environment. While improving the extension of Vietnamese language sentiment analysis is still difficult, stochastic gradient descent alogorithm (SGD) exposes the limitations about optimal process in incremental learning. Sterm from two issues, the study proposed model combine Long Short Term Memory with KSGD algorithms in matrix factorization to improve the time and accuracy of predict model. With experimental results, this work proves that proposed system achieves better results with accuracy and learning time.
A Facile Approach To Represent Neural Network With Less Storage Space

Deep neural networks have been applied to different applications, such as image recognition, data analysis, natural language processing, and so on. Some companies regularly release their mobile applications to app stores. Usually, app stores have a restriction that overweight applications can not be downloaded until the smartphone connects to Wi-Fi. Unfortunately, the size of the application that consists of deep neural networks may be too large. In order to reduce the size of application, an efficient compression approach is highly desired. An efficient encoding for compressed neural networks is a critical point for reducing storage complexity. Most of previous studies focus on the sparse matrix encoding instead of the dense one. The dense matrix encoding methods have not been studied well. Meanwhile, sparse matrix encoding can not attain a high compression rate when encoding a dense matrix. Toward this end, we proposed an alternative compression algorithm that linearizes the weights on each layer and then stores linearized weights with critical information. For a dense matrix, our algorithm can achieve a higher compression rate than one existing sparse matrix encoding. Experiment results indicate that our proposed piecewise linearization scheme can make VGG-16, Resnet152, and Densenet169 achieve at least two times of the compression rates. In other words, neural networks parameters can be stored in 40–50% of the original space.
Towards the full integration of Silicon Photonic Chip for Deep Neural Networks

Neural networks are taking a central role, while the integration of novel technologies has lacked. Here we present an integration of Silicon Photonic chip MVM into a stand-alone PCB, aiming at the photonic “black-box” integration.
Position and Angular control using Fuzzy Tuned PID Controller for Mobile Robot Path Tracking

Analyzing unstructured gain data has been a growing interest among researchers, resulting in valuable information in many fields such as path planning, and others. Furthermore, the Fuzzy Logic Controller (FLC) has simplified the managing unstructured gain data on the simulator such as Gazebo. FLC and PID controller can be used as a solution to recognize path planning entities in the collision-free environment based on heuristic solution. However, without proper selection of unstructured gain data of PID such as proportional, derivative and integral, the performance maybe compromised. In this paper, PID and FLC is designed in series for position and angular control to overcome the multi-representation and the problem of uncertainty contexts. It will be able to recognize the Fuzzy Tuned PID controller gains with limited supervised data accurately and take fast and precise intervention. The efficient and precise algorithms help the mobile robot to take immediate and appropriate intervention, thus helping to preserve the path planning. Finally, in line with the FLC and PID controller, this study will fulfill two elements in the Control Signal Distance (CSD) and Control Signal Angle (CSA) to produce precise and optimal mobile robot path tracking.
SiamDoGe: Domain Generalizable Semantic Segmentation Using Siamese Network

Deep learning-based approaches usually suffer from performance drop on out-of-distribution samples, therefore domain generalization is often introduced to improve the robustness of deep models. Domain randomization (DR) is a common strategy to improve the generalization capability of semantic segmentation networks, however, existing DR-based algorithms require collecting auxiliary domain images to stylize the training samples. In this paper, we propose a novel domain generalizable semantic segmentation method, "SiamDoGe", which builds upon a DR approach without using auxiliary domains and employs a Siamese architecture to learn domain-agnostic features from the training dataset. Particularly, the proposed method takes two augmented versions of each training sample as input and produces the corresponding predictions in parallel. Throughout this process, the features from each branch are randomized by those from the other to enhance the feature diversity of training samples. Then the predictions produced from the two branches are enforced to be consistent conditioned on feature sensitivity. Extensive experiment results demonstrate the proposed method exhibits better generalization ability than existing state-of-the-arts across various unseen target domains.
Improving the Semantic Consistency of Textual Adversarial Attacks via Prompt

Adversarial examples can expose the vulnerabilities of neural networks. State-of-the-art textual adversarial attacks have demonstrated their effectiveness in triggering errors in the output of natural language processing models. However, these attacks are limited to ensuring the semantic consistency between the adversarial example and the original input, increasing the possibility of the attacks being detected by human judges. In this paper, we propose a novel textual adversarial attack, Prompt-Attack, which aims to generate the adversarial examples having consistent semantics with the original input. Specifically, Prompt-Attack enhances the input's semantics with the prompts that represent the semantics of the different target segments extracted from the original input, to predict the substitutions having consistent semantics with the target segments. Then, it crafts the adversarial examples by replacing the important segments with their substitutions that can most affect the victim model's output. Besides, Prompt-Attack proposes a span-level segment identification strategy to extract more target segments from the input and a novel masking strategy to ensure the grammatical correctness of the generated adversarial examples. Extensive experiments on public datasets illustrate that Prompt-Attack significantly improves the semantic consistency score of the baseline attacks by an average of 48%. Further, Prompt-Attack achieves the best attack success rate of 0.906, showing an average improvement of 40% to the baselines. Moreover, the experimental results demonstrate that Prompt-Attack can achieve good performance in attacking different language models and Prompt-Attack is not sensitive to different settings.
Coordinated Underwater Dark Channel Prior for Alleviating Halos and Patch Artifacts of Challenging Image Enhancement

For the challenging underwater images, there are always some problems such as hazy object, low contrast, and serious noises, which often are caused by various changes in the light transmission pathway. The complex underwater environments possibly lead to scattered and refracted light, and thus some original images become blurry like a veil. It is a popular solution by applying the dark channel prior (DCP) to the dehazing process of underwater images. However, there still are some side effects such as halos and patch artifacts after DCP-based dehazing. According to the environmental dissimilarity between underwater imaging and land imaging, this paper proposes a coordinated underwater dark channel prior method so as to alleviate the side effects of the traditional DCP-based enhancement methods. By considering the differences of the light absorption in the deep sea or the shallow sea, the attenuation rates of the RGB channels are calculated and coordinated in different regions. Then, the color restoration is further implemented by a compensated gray world theory that coordinates the intensity of different RGB channels. Finally, the proposed method adopts a subpatch-wise mean filtering according to sub-patch classification. The experimental results show that compared with other enhancement methods, the proposed method can significantly alleviate the halos and patch artifacts of challenging image enhancement, and also effectively improve the objective quality of challenging underwater images.
Technological Tools for the Training of Mentor Teachers: Learnings from a Chilean Program

This study describes the participant perceptions and evaluations of a mentor teaching training course in b-learning modality, which sustains its virtual character with two technological platforms: Sibme and Canvas. A mixed methodological design was used to study the perceptions, based on the descriptive analysis of online surveys directed to two cohorts of course participants and the content analysis of interviews. The main findings revealed that the b-learning modality could promote the training of mentors, and the combination of theoretical and practical elements supported by the platforms was highly valued by teachers in training.
Silicon Photonics Neural Networks for Training and Inference

Deep learning hardware accelerators based on analog photonic networks are trained on standard digital electronics. We discuss on-chip training of neural networks enabled by a silicon photonic architecture for parallel, efficient, and fast data operations.
DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence

Tumour infiltrating lymphocytes (TIL) influence the prognosis of Ductal carcinoma in situ (DCIS). Currently, manual assessment of TIL by expert pathologists is considered a gold standard. However, there are issues with a shortage of expert pathologists and inter-observer variability. A reliable automated scoring method is yet to be developed due to the inherent complexity of DCIS duct morphology and the assessment strategy. We developed a new deep learning and spatial analysis pipeline to automatically score DCIS stromal TIL (AI-TIL) from 243 diagnostic haematoxylin and eosin-stained whole slide images from 127 patients. To automatically identify and segment DCIS ducts, we implemented a generative adversarial network. To identify lymphocytes, we used a pre-trained deep learning model. Our DCIS segmentation model achieved a dice overlap of 0.94 ( $${\pm }0.01$$ ) and the cell classifier model achieved 92% accuracy compared to pathologists’ annotations. Subsequently, we automatically delineated a stromal boundary and computed the percentage of the boundary area occupied by lymphocytes for each DCIS duct. Finally, we computed TIL score as the average of all duct level scores within the slide. We observe a higher correlation between AI-TIL and pathologists (average) score for wider stomal boundaries (r = 0.66, p = $$6.0 \times 10^{-7}$$ , W = 0.3 mm) compared with smaller boundary (r = 0.23, p = 0.12, W = 0.03 mm). Using multivariate analysis, a low AI-TIL score was associated with an increased risk of recurrence independent of age, grade, estrogen receptor (ER) status, progesterone receptor (PR) status, and necrosis (hazard ratio = 0.14, 95% CI 0.038–0.51, p = 0.003, W = 0.03 mm). These results suggest that our pipeline could be used to automatically quantify stromal TIL in DCIS and integrating AI-TIL with pathologists’ visual assessment may improve DCIS recurrence risk estimation.
Levelwise Data Disambiguation by Cautious Superset Classification

Drawing conclusions from set-valued data calls for a trade-off between caution and precision. In this paper, we propose a way to construct a hierarchical family of subsets within set-valued categorical observations. Each subset corresponds to a level of cautiousness, the smallest one as a singleton representing the most optimistic choice. To achieve this, we extend the framework of Optimistic Superset Learning (OSL), which disambiguates set-valued data by determining the singleton corresponding to the most predictive model. We utilize a variant of OSL for classification with 0/1 loss to find the instantiations whose corresponding empirical risks are below context-depending thresholds. Varying this threshold induces a hierarchy among those instantiations. In order to rule out ties corresponding to the same classification error, we utilize a hyperparameter of Support Vector Machines (SVM) that controls the model’s complexity. We twist the tuning of this hyperparameter to find instantiations whose optimal separations have the greatest generality. Finally, we apply our method on the prototypical example of yet undecided political voters as set-valued observations. To this end, we use both simulated data and pre-election polls by Civey including undecided voters for the 2021 German federal election.
Graph and Question Interaction Aware Graph2Seq Model for Knowledge Base Question Generation

The Knowledge Base Question Generation (KBQG) is an essential natural language processing task. Taking knowledge graph and answer entities as input, KBQG aims to generate corresponding natural language question. Recently Graph2Seq has been proposed to encode the knowledge graph and achieved remarkable results, while one important challenge still remains, i.e., the graph encoding lacks the interaction with the target question. To deal with the above challenge, we propose a graph and question interaction enhanced Graph2Seq model, in which we design an encoder-decoder parallel enhancement mechanism and apply the knowledge distillation for both inter-mediate representation and prediction distribution to employ the knowledge of the target question into the graph representation. Experiments have been conducted on KBQG benchmark dataset and experimental results have shown the promising potential of proposed method.
Cross Reality Authoring: A Mixed Reality Editor approach

The development of Mixed Reality (MR) applications with today's tools and approaches brings various challenges. A key problem is the difference and lack of connectivity between development plat-form and target platform. Not only does this make designing and testing of immersive experiences challenging, but it also puts exces-sive demands on the imagination, abstraction power, and patience of developers. This paper summarizes related research topics and high-lights a cross-device approach to MR development. The topics and research questions described form the basis for further investigations in the context of a dissertation.
Uncertainty-Driven Control for a Self-Supervised Lifelong Learning Drone

It has been a long standing goal of artificial intelligence to develop algorithms that support adaptive automation that allow unmanned vehicles to operate safely and independently in real-world environments. Here, we summarize experiments that demonstrate how a novel algorithm for measuring uncertainty during operation by a drone can support self-supervised learning. Our uncertainty-modulated learning algorithm is inspired by neuromodulatory mechanisms in the brain that control both the flow of information in neural circuits and the computational properties of those circuits. Our algorithm suggests how uncertainty can be used as an internal measure of performance that can trigger adaptation and the execution of information-seeking behaviors. This results in emergent behaviors that enable a drone to continually learn and adapt to support robust performance in real-world changing environments.
Research on Energy Allocation Strategy Based on Fault-Tolerant Control Theory

According to the actual situation of sports events, the physical strength distribution model and fault tolerant control model are established. According to the principle of multi-objective optimization, the improved distributed genetic algorithm was used for preliminary solving, and the corresponding fault-tolerant control law was developed to correct the strategies with errors, so as to maintain a high fault-tolerant degree while ensuring the overall score of the event as best as possible. Distributed genetic algorithm has the characteristics of strong search ability and fast search speed, and the fault-tolerant control theory can fully combine with the practice to ensure the overall fault tolerance of the strategy. Finally, the reliability and effectiveness of the algorithm are verified by simulation.
Reporting Framework and Responsibility Analysis of Chinese and American Media in Trending Events

In the era of mobile Internet, news client, as an important platform for the public to obtain news information, can not only provide users with completely open news information, but also meet the audience’s desire to understand hot events based on absolute timeliness. However, traditional media should correctly deal with the challenges brought by network media according to their authority and depth. Because of the hot issues and human daily life, social equity has a direct relationship, such as transmission range will be improved as the audience’s attention and growing, so in the new era of development, press the client to understand the user group characteristics, on the basis of fully show themselves in the release and guiding force in the influence of the social hot issues, only in this way can guarantee social stability development. Therefore, on the basis of understanding the reporting framework of hot events, this paper compares and analyzes the different characteristics of Chinese and American media during news reporting and summarizes the development direction of modern media news clients.
Self-Attention-Based Multivariate Anomaly Detection for CPS Time Series Data with Adversarial Autoencoders

Data-driven anomaly detection continues to be challenging due to the increased complexity of modern cyber physical systems (CPSs) and their temporal dependencies. Unsupervised detection techniques are widely used through VAE-based frame-works and RNN-based deep learning techniques. However, VAE and its variants impose too much constraint on extracted latent code, and RNN's autoregressive essence indicates the shortage of parallelism and long-term prediction. To tackle the above is-sues, we propose TransAAE (Transformer-augmented Adversarial Autoencoder), a novel unsupervised approach for multivariate time series anomaly detection. The use of the adversarial autoencoder (AAE) architecture loosens the regularization of latent code, and self-attention mechanism is utilized to extract temporal information. Extensive experiments show an average F1 score over 0.9 on three public datasets, which significantly outperforms among the baselines.
Research Papers Recommendation

The work is devoted to academic papers recommendation task considered as link prediction on a static citation network. We compare several graph embeddings, text-based and fusion models in the link prediction problem on academic papers citation dataset. We showed that fusion models of graph and text information outperform other approaches based on graph or text information alone. We prove this via an extensive set of experiments with different train/test splits that our fusion models are robust and retain superior performance even with a reduced train set.
Incidental Learning

Incidental learning is unintended learning, where learners can infer an action about the topic unrelated to what they learned. The prior research on this topic will analyze how people learn incidentally in their workplace routines. For many people, mobile devices have become an essential object in their everyday lives, allowing them to facilitate themselves with technology. This could trigger self-reflection and prove more consistent for long-term learning that encourages the learners to re-conceive isolated learning fragments that otherwise might have been isolated. This chapter introduces the concepts of Incidental learning and its impact on the learners and how to engage and implement it in the classroom. Further, this chapter attempts to differences and similarities between Incidental Learning, Intentional Learning, and Informal Learning. Finally, it presents a case study.
Deep learning based on byte sample entropy for VPN encrypted traffic identification

Network traffic identification is important for traffic engineering, resource allocation, network management, attack detection and improving network QoS. However, with the rapid development of computer network technology, various VPN technologies and applications have emerged, which use encryption and decryption technology, tunneling technology and authentication technology to obfuscate and hide traffic characteristics, making VPN traffic difficult to identify. The recent rise of V2Ray makes up for and completes the shortcomings of previous VPN technologies with a more complete protocol, more robust performance and richer functionality, using V2Ray’s customised VMess protocol, and the VMess protocol supports TLS-based implementations, making it a full-featured and powerful application. These undoubtedly pose a huge challenge for network traffic identification and auditing, as well as a huge risk for network security. Therefore, the identification of VPN traffic is of great importance. In this paper, we propose a VPN traffic identification method based on byte sample entropy and session interaction time difference. We use the byte sample entropy and session interaction time difference of some message sequences in network traffic as feature data, and use Random Forest RF (RF) algorithm to identify V2Ray VMess traffic, TLS-based VMess traffic and ISCX VPN-NonVPN public dataset, achieving 95.97%, 90.32% and 91.78% recognition accuracy, respectively. The experimental results show that the method can be used for the detection and identification of V2Ray traffic, and also supports the detection and identification of the rest of VPN traffic.
Spear Phishing Email Detection with Multiple Reputation Features and Sample Enhancement

Spear phishing is a complex targeted attack which has rapidly increased in recent years. The traditional email features based on the sender’s behavior portrait cannot accurately characterize the spear phishing email, and the detection is often hampered when the data set is small. In order to tackle these problems, in this paper, we present a new approach for detecting spear phishing attacks in the full help of the local and external reputation features. Our method extracts 8 local and 6 external reputation features derived from an analysis of spear phishing emails, combined with 4 forwarding features and 20 general features for more accurate detection. Synthetic Minority Oversampling Technique (SMOTE) algorithm and an improved KM-SMOTE are applied on enhancing samples.We evaluate features on a multi-source data set of over 41 thousand emails and achieve the recall of 86.89 $$\%$$ , the accuracy of 88.33 $$\%$$ in identifying spear phishing emails. With SMOTE, we improve the recall and precision to 91.80 $$\%$$ and 93.55 $$\%$$ , and the false positive rate is reduced by at least 22 $$\%$$ . With KM-SMOTE, we achieve better maximum recall of 95.08 $$\%$$ , precision of 93.55 $$\%$$ and F1-score of 94.31 $$\%$$ .
Research on Multi-Defect Model Recognition Based on Machine Learning

As an important part of the safe operation of the power grid, the air switchgear will cause inevitable economic losses when it fails. Aiming at the problems of low fault recognition rate and lack of credibility of test results in air switchgear, this paper proposes to combine machine learning to identify common fault defect types in switchgear, analyse PRPD maps of each defect model and statistical feature values as machine learning inputs. PNN probabilistic neural network, POS-SVM algorithm and SOM self-organizing neural network training were used to determine the optimal algorithm. Research shows that the PSO-SVM model is chosen as the most suitable model for the discharge mode judgment of the switchgear.