Reviewerly/exHarmony huggingface dataset:
A Novel Teacher-Assistance-Based Method to Detect and Handle Bad Training Demonstrations in Learning From Demonstration

Learning from demonstration (LfD) assists robots to derive a policy from a training set to execute a task. The training set consists of training demonstrations collected from a human who executes the same task. However, due to the human’s varied skill level, the quality of the training set may be bad, which will affect the accuracy of the derived policy. To solve this problem, this article proposes a novel method to improve the quality of the training set. This method includes two steps, namely detecting and handling bad training demonstrations in the training set. In the detecting step, a reference set containing reference demonstrations is provided by a human teacher. Based on the reference set, we calculate the influence of each training demonstration on the policy derivation. If the influence is negative, the corresponding training demonstration is bad. Afterward, in the handling step, we calculate the proportion of the negative influence with respect to the overall influence and reduce the proportion by iteratively removing bad training demonstrations until it is less than a threshold. The results show that the accuracy of a policy derived from the improved training set increases with up to 19.30%, which verifies the effectiveness of our method.
High Efficiency Spam Filtering: A Manifold Learning-Based Approach

Spam filtering, which refers to detecting unsolicited, unwanted, and virus-infested emails, is a significant problem because spam emails lead to unnecessary costs of Internet resources, waste of people’s time, and even loss of property. Support vector machine (SVM) is the state-of-the-art method for high accuracy spam filtering. However, SVM incurs high time complexity because of the high dimensionality of the emails. In this study, we propose a manifold learning-based approach for time-efficient spam filtering. From the experiments that most of the features are not decisive, we can obtain the viewpoint that only a minor part of the spam emails can be detected using the nondecisive features. Based on the insight, we propose to employ the Laplace feature map algorithm to obtain the geometrical information from the email text datasets and extract the decisive features. Then, the extracted features are used as the input of SVM to spam filtering. We conduct extensive experiments on three datasets, and the evaluation results indicate the high accuracy time efficiency of our proposed algorithm.
DeepFH segmentations for superpixel-based object proposal refinement

Class-agnostic object proposal generation is an important first step in many object detection pipelines. However, object proposals of modern systems are rather inaccurate in terms of segmentation and only roughly adhere to object boundaries. Since typical refinement steps are usually not applicable to thousands of proposals, we propose a superpixel-based refinement system for object proposal generation systems. Utilizing precise superpixels and superpixel pooling on deep features, we refine initial coarse proposals in an end-to-end learned system. Furthermore, we propose a novel DeepFH segmentation, which enriches the classic Felzenszwalb and Huttenlocher (FH) segmentation with deep features leading to improved segmentation results and better object proposal refinements. On the COCO dataset with LVIS annotations, we show that our refinement based on DeepFH superpixels outperforms state-of-the-art methods and leads to more precise object proposals.
TiAcc: Triangle-inequality based Hardware Accelerator for K-means on FPGAs

K-means is one of the most important unsuper-vised learning algorithms. In this paper, we present TiAcc, a triangle-inequality based K-means hardware accelerator on FPGAs. TiAcc highlights itself with an algorithm-hardware co-design strategy tailored for K-means clustering. Specifically, TiAcc leverages a novel triangle-inequality based filtering to eliminate unnecessary distance computations without changing the final clustering results. Meanwhile, it employs a pipeline decoupling approach to mitigate the irregularity of the remaining computations, and an efficient hardware architecture design to fully exploit the pipeline and parallel processing capability of FPGAs. Moreover, TiAcc provides parameterized configuration knobs that can minimize the manual efforts in the arduous hardware design process and provides flexibility to optimize hardware designs for a variety of datasets with different sizes and dimensionalities. Intensive experiments show that TiAcc achieves an average 4.94× speedup and significant energy efficiency (average 74.22 ×) compared with an optimized K-means running on a server-grade Xeon CPU.
Blockchain-Based Continuous Auditing for Dynamic Data Sharing in Autonomous Vehicular Networks

With the rapid development of intelligent transportation, massive data are generated by autonomous vehicle systems and shared among vehicles through cloud servers to improve the driving experience and service quality. However, cloud servers cannot be fully trusted and may lead to serious data security challenges.
Real-Time Rolling Door Detection and Classification for Security Application

Rolling Doors or shutters play an important role as a means of security and privacy of street shops in many countries. These shutters have flimsy locking mechanisms which makes them vulnerable and can be broken in easily. In this paper, we have implemented the detection and classification of rolling doors, intending to improve the surveillance system by detecting whether a particular rolling door is open during off times, leading to a possible break-in. The tool used for detection in this system is the Google Tensorflow Object Detection (GTOD) API [3]. This API enables us to train custom datasets on various latest cutting-edge models. The OpenCV library [2] provides real-time optimized computer vision applications which are used in various aspects of image processing for this system such as data augmentation, image grading, and application of the flood fill algorithm [1], which is being utilized as the main technique for classification in this system. In this system, OpenCV is used as the primary interface for transporting input frames to the detection model and displaying the final output frame.
FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation

Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in many application areas, due to the capability of automatically extracting and aggregating spatial and channel-wise features from images. Most recent studies have concentrated on modifying convolutional kernel size to achieve multi-scale spatial information. In this paper, we introduce a novel fuzzy integral module to the CNNs for fusing the information across feature channels. The fuzzy integral is a mathematical aggregation operator and is widely used in decision level fusion. Herein, we utilize a special case of fuzzy integrals namely ordered weight averaging (OWA) to merge information at feature level. Three publicly available datasets were used to evaluate the proposed fuzzy CNN model for image segmentation. The results show that the proposed fuzzy module helps in reducing the baseline model parameters by 58.54% while producing higher segmentation accuracy (measured by Dice) than the baseline method and a similar method reported in the literature.
Quantum analysis of polarization entanglement degradation induced by multiple-photon-pair generation

The formation of multiple photon pairs in spontaneous parametric down-conversion leads to a degradation of polarization entanglement quality. The authors characterize this quality and perform all-quantum calculations that agree with known results for two different Bell-state-generation methods.
HDR4CV: High Dynamic Range Dataset with Adversarial Illumination for Testing Computer Vision Methods

Benchmark datasets used for testing computer vision (CV) methods often contain little variation in illumination. The methods that perform well on these datasets have been observed to fail under challenging illumination conditions encountered in the real world, in particular, when the dynamic range of a scene is high. The authors present a new dataset for evaluating CV methods in challenging illumination conditions such as low light, high dynamic range, and glare. The main feature of the dataset is that each scene has been captured in all the adversarial illuminations. Moreover, each scene includes an additional reference condition with uniform illumination, which can be used to automatically generate labels for the tested CV methods. We demonstrate the usefulness of the dataset in a preliminary study by evaluating the performance of popular face detection, optical flow, and object detection methods under adversarial illumination conditions. We further assess whether the performance of these applications can be improved if a different transfer function is used.
Group Activity Recognition Using Joint Learning of Individual Action Recognition and People Grouping

This paper proposes joint learning of individual action recognition and people grouping for improving group activity recognition. By sharing the information between two similar tasks (i.e., individual action recognition and people grouping) through joint learning, errors of these two tasks are mutually corrected. This joint learning also improves the accuracy of group activity recognition. Our proposed method is designed to consist of any individual action recognition methods as a component. The effectiveness is validated with various IAR methods. By employing existing group activity recognition methods for ensembling with the proposed method, we achieved the best performance compared to the similar SOTA group activity recognition methods.
Effects of Print Exposure on an Online Lexical Decision Task: A Direct Replication Using a Web-Based Experimental Procedure

Due to the global pandemic, behavioral sciences including psychology that have traditionally relied on face-to-face data collection methods are facing a crisis. Given these circumstances, the present study was designed as a web-based replication of the findings reported in Lee et al. ( 2019 ) on the relationship between print exposure measured by the Korean Author Recognition Test (KART) and online measures of word processing using the lexical decision task and offline measures of language ability. We used the PsychoPy3 and Pavlovia platform in which participants were presented with a series of tasks in an entirely web-based environment. We found that scores on the KART were correlated with scores on a measure of language skills as well as self-reported reading habits. In addition, KART scores modulated the word frequency effect in the lexical decision task such that participants with higher KART scores tended to have smaller frequency effects. These results were highly consistent with previous lab-based studies including Lee et al. indicating that web-based experimental procedures are a viable alternative to lab-based face-to-face experiments.
Model-based clinical note entity recognition for rheumatoid arthritis using bidirectional encoder representation from transformers

Rheumatoid arthritis (RA) is a disease of the immune system with a high rate of disability and there are a large amount of valuable disease diagnosis and treatment information in the clinical note of the electronic medical record. Artificial intelligence methods can be used to mine useful information in clinical notes effectively. This study aimed to develop an effective method to identify and classify medical entities in the clinical notes relating to RA and use the entity identification results in subsequent studies.In this paper, we introduced the bidirectional encoder representation from transformers (BERT) pre-training model to enhance the semantic representation of word vectors. The generated word vectors were then inputted into the model, which is composed of traditional bidirectional long short-term memory neural networks and conditional random field machine learning algorithms for the named entity recognition of clinical notes to improve the model's effectiveness. The BERT method takes the combination of token embeddings, segment embeddings, and position embeddings as the model input and fine-tunes the model during training.Compared with the traditional Word2vec word vector model, the performance of the BERT pre-training model to obtain a word vector as model input was significantly improved. The best F1-score of the named entity recognition task after training using many rheumatoid arthritis clinical notes was 0.936.This paper confirms the effectiveness of using an advanced artificial intelligence method to carry out named entity recognition tasks on a corpus of a large number of clinical notes; this application is promising in the medical setting. Moreover, the extraction of results in this study provides a lot of basic data for subsequent tasks, including relation extraction, medical knowledge graph construction, and disease reasoning.
The Knowledge Graph as an Ontological Framework

The knowledge Graph has emerged as a potential way of representing an ontology for dynamic systems. It may also serve as a governance mechanism for future complexity driven applications.
Selecting test inputs for DNNs using differential testing with subspecialized model instances

Testing of Deep Learning (DL) models is difficult due to the lack of automated test oracle and the high cost of human labelling. Differential testing has been used as a surrogate oracle, but there is no systematic guide on how to choose the reference model to use for differential testing. We propose a novel differential testing approach based on subspecialized models, i.e., models that are trained on sliced training data only (hence specialized for the slice). A preliminary evaluation of our approach with an CNN-based EMNIST image classifier shows that it can achieve higher error detection rate with selected inputs compared to using more advanced ResNet and LeNet as the reference model for differential testing. Our approach also outperforms N-version testing, i.e., the use of the same DL model architecture trained separately but using the same data.
Improving Mispronunciation Detection of Mandarin for Tibetan Students Based on the End-To-End Speech Recognition Model

Mispronunciation detection is an important part of the Computer-Assisted Pronunciation Training System (CAPT). In this paper, we build a CNN-GRU-CTC acoustic model with Gated Recurrent Unit (GRU), convolutional neural network (CNN), and Connectionist Temporal Classification (CTC) technologies to study the pronunciation error detection of Tibetan students in Mandarin. This approach is end-to-end models, while phonemic or graphemic information, or forced alignment between different linguistic units, are not required. The experimental results show that the method proposed in this paper has achieved better experimental results and can effectively detect mispronunciations. The false rejection rate is 7.26%, the detection accuracy rate is 88.35%, and the combined error rate is 14.91%.
Do current user testing practices meet the needs of the new interactive paradigms?

Human-Computer Interaction (HCI) practitioners count with more than 30-40 years of work already done evaluating the usability of interactive systems. For along time, the evaluation of user interfaces by user testing techniques, has been one of the most successful ways to get feedback from the users. HCI experts usually perform their experimental evaluations sessions in usability labs, indoor facilities with equipment like eye-tracking devices or multi-rooms with specialised software. Although expensive and not available for everybody, this model has worked for a long time. On the other hand, during the last decade the interactive context has completely changed. The rise of new user interactions like voice, virtual reality and augmented reality has conceived new interactive paradigms. The well-known Industry 4.0 and the Internet of Things (IoT) are not just something that might happen in the future, but an unstoppable reality that favours these changes and, because of that, we need new HCI knowledge, methods, and techniques to deal with the challenges brought by them. In this paper we show and discuss about this new reality in order to defend the need for a complete change in the way that we evaluate the Quality in Use of current and future interactive scenarios. We will finish our arguments by proposing a line of research to find new methodologies to improve the Quality in Use laboratories for the present/future.
Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss

Abstract We study learning named entity recognizers in the presence of missing entity annotations. We approach this setting as tagging with latent variables and propose a novel loss, the Expected Entity Ratio, to learn models in the presence of systematically missing tags. We show that our approach is both theoretically sound and empirically useful. Experimentally, we find that it meets or exceeds performance of strong and state-of-the-art baselines across a variety of languages, annotation scenarios, and amounts of labeled data. In particular, we find that it significantly outperforms the previous state-of-the-art methods from Mayhew et al. (2019) and Li et al. (2021) by +12.7 and +2.3 F1 score in a challenging setting with only 1,000 biased annotations, averaged across 7 datasets. We also show that, when combined with our approach, a novel sparse annotation scheme outperforms exhaustive annotation for modest annotation budgets.1
A Study on College English Teaching Reform Based on The Integration of Computer Network and Foreign Language Courses

Abstract Since stepping into the 21st century, the innovation and development of computer network technology have been eye-catching, which has brought a great impact on the traditional models of all walks of life. In the field of education teaching, the introduction of computer network technology, application and innovation not only upgrade the teaching equipment and facilities, enrich the teaching of various types of network resources, but also changed the traditional English teaching strategy, this paper is to vocabulary learning in college English curriculum as the breakthrough point, through empirical method to research network multimedia form, the relationship between college students and the electronic media, presenting methods of college English teaching content, different mode of teaching and learning of vocabulary learning effect.
An Automatic Evaluation Approach for Binary Software Vulnerabilities with Address Space Layout Randomization Enabled

ASLR is currently an effective mean to defend against exploits based on known addresses, but in recent years, exploit codes have begun to effectively bypass the ASLR technology. Since the ASLR mechanism bypass methods are different and closely related to the target program, the current vulnerability assessments with ASLR enabled are manually completed based on the experience of manual vulnerability exploitation. Therefore, how to quickly assess software vulnerabilities, especially with the ASLR enabled, is a problem that needs to be solved in current software security. In this paper, we propose an automatic evaluation approach for binary software vulnerabilities with ASLR enabled, that can obtain key address information from the program to identify the possible ASLR bypasspaths. The approach uses an information leakage method based on a recessive output function by imitating the ASLR bypass technology commonly used among human vulnerability exploitation experts. By inputting a valid proof of concept, it first obtains the vulnerability triggering constraints of the target program, then searches for the hidden output function to leak information, and generates a final exploit that can be used to bypass ASLR. We argue that this automatic evaluation approach can reduce the reliance on manual work of ASLR-related exploits, and improves the vulnerability assessment mechanism significantly. We evaluated our approach against three CTF binary programs, and the results show that it can assess the exploitability of the vulnerability with the ASLR enabled in seconds.
Successful New-entry Prediction for Multi-Party Online Conversations via Latent Topics and Discourse Modeling

With the increasing popularity of social media, online interpersonal communication now plays an essential role in people’s everyday information exchange. Whether and how a newcomer can better engage in the community has attracted great interest due to its application in many scenarios. Although some prior works that explore early socialization have obtained salient achievements, they are focusing on sociological surveys based on the small group. To help individuals get through the early socialization period and engage well in online conversations, we study a novel task to foresee whether a newcomer’s message will be responded to by other participants in a multi-party conversation (henceforth Successful New-entry Prediction)1. The task would be an important part of the research in online assistants and social media. To further investigate the key factors indicating such engagement success, we employ an unsupervised neural network, Variational Auto-Encoder (VAE), to examine the topic content and discourse behavior from newcomer’s chatting history and conversation’s ongoing context. Furthermore, two large-scale datasets, from Reddit and Twitter, are collected to support further research on new-entries. Extensive experiments on both Twitter and Reddit datasets show that our model significantly outperforms all the baselines and popular neural models. Additional explainable and visual analyses on new-entry behavior shed light on how to better join in others’ discussions.
Liver Tumor Detection Via A Multi-Scale Intermediate Multi-Modal Fusion Network on MRI Images

Automatic liver tumor detection can assist doctors to make effective treatments. However, how to utilize multi-modal images to improve detection performance is still challenging. Common solutions for using multi-modal images consist of early, inter-layer, and late fusion. They either do not fully consider the intermediate multi-modal feature interaction or have not put their focus on tumor detection. In this paper, we propose a novel multi-scale intermediate multi-modal fusion detection framework to achieve multi-modal liver tumor detection. Unlike early or late fusion, it maintains two branches of different modal information and introduces cross-modal feature interaction progressively, thus better leveraging the complementary information contained in multi-modalities. To further enhance the multi-modal context at all scales, we design a multi-modal enhanced feature pyramid. Extensive experiments on the collected liver tumor magnetic resonance imaging (MRI) dataset show that our framework outperforms other state-of-the-art detection approaches in the case of using multi-modal images.