Reviewerly/exHarmony huggingface dataset:
Efficient Algorithms for Data Processing under Type-3 (and Higher) Fuzzy Uncertainty

It is known that, to more adequately describe expert knowledge, it is necessary to go from the traditional (type-1) fuzzy techniques to higher-order ones: type-2, probably type-3 and even higher. Until recently, only type-1 and type-2 fuzzy sets were used in practical applications. However, lately, it turned out that type-3 fuzzy sets are also useful in some applications. Because of this practical importance, it is necessary to design efficient algorithms for data processing under such type-3 (and higher-order) fuzzy uncertainty. In this paper, we show how we can combine known efficient algorithms for processing type-1 and type-2 uncertainty to come up with a new algorithm for the type-3 case.
Construction of quaternary Hermitian LCD codes

We introduce a general construction of many Hermitian LCD [n, k] codes from a given Hermitian LCD [n, k] code. Furthermore, we present some results on punctured codes and shortened codes of quaternary Hermitian LCD codes. As an application, we improve some of the previously known lower bounds on the largest minimum weights of quaternary Hermitian LCD codes of length $$26 \le n \le 30$$ .
Designing an Interactive Communication Assistance System for Hearing-Impaired College Students Based on Gesture Recognition and Representation

Developing a smart classroom can make the modern classroom more efficient and intelligent. Much research has been conducted pertaining to smart classrooms for hearing-impaired college students. However, there have been few significant breakthroughs in mobilizing students’ learning efficiency as measured by information transmission, communication, and interaction in class. This research collects data through nonparticipatory observation and in-depth interviews and analyzes available data on classroom interaction needs of these students. We found that diversified explanations, recordable interactive contents, and interaction between teachers and students could improve the learning effects in the classroom. We also propose a tracking-processing method based on gesture recognition and representation and present a design for a processing system based on AT89C52 microcontroller and Kinect. In this way, sign language can be translated into text and all students can receive the information and participate in the interaction, which greatly improves students’ autonomy and enthusiasm of learning. This design enables deaf students to fully use classroom learning resources, reduces learning time costs, and improves learning efficiency. It can assist teachers in teaching and tutoring students to enhance their experience.
Saliency-Regularized Deep Multi-Task Learning

Multitask learning is a framework that enforces multiple learning tasks to share knowledge to improve their generalization abilities. While shallow multitask learning can learn task relations, it can only handle predefined features. Modern deep multitask learning can jointly learn latent features and task sharing, but they are obscure in task relation. Also, they predefine which layers and neurons should share across tasks and cannot learn adaptively. To address these challenges, this paper proposes a new multitask learning framework that jointly learns latent features and explicit task relations by complementing the strength of existing shallow and deep multitask learning scenarios. Specifically, we propose to model the task relation as the similarity between task input gradients, with a theoretical analysis of their equivalency. In addition, we innovatively propose a multitask learning objective that explicitly learns task relations by a new regularizer. Theoretical analysis shows that the generalizability error has been reduced thanks to the proposed regularizer. Extensive experiments on several multitask learning and image classification benchmarks demonstrate the proposed method effectiveness, efficiency as well as reasonableness in the learned task relation patterns.
Gaussian Kernel-Based Cross Modal Network for Spatio-Temporal Video Grounding

Spatial-Temporal Video Grounding (STVG) is a challenging task which aims to localize the spatio-temporal tube of the interested object semantically according to a natural language query. Most previous works not only severely rely on the anchor boxes extracted by Faster R-CNN, but also simply regard the video as a series of individual frames, thus lacking their temporal modeling. Instead, in this paper, we are the first to propose an anchor-free framework for STVG, called Gaussian Kernel-based Cross Modal Network (GKCMN). Specifically, we utilize the learned Gaussian Kernel-based heatmaps of each video frame to locate the query-related object. A mixed serial and parallel connection network is further developed to leverage both spatial and temporal relations among frames for better grounding. Experimental results on VidSTG dataset demonstrate the effectiveness of our proposed GKCMN.
Optical Flow-Aware-Based Multi-Modal Fusion Network for Violence Detection

Violence detection aims to locate violent content in video frames. Improving the accuracy of violence detection is of great importance for security. However, the current methods do not make full use of the multi-modal vision and audio information, which affects the accuracy of violence detection. We found that the violence detection accuracy of different kinds of videos is related to the change of optical flow. With this in mind, we propose an optical flow-aware-based multi-modal fusion network (OAMFN) for violence detection. Specifically, we use three different fusion strategies to fully integrate multi-modal features. First, the main branch concatenates RGB features and audio features and the optical flow branch concatenates optical flow features with RGB features and audio features, respectively. Then, the cross-modal information fusion module integrates the features of different combinations and applies weights to them to capture cross-modal information in audio and video. After that, the channel attention module extracts valuable information by weighting the integration features. Furthermore, an optical flow-aware-based score fusion strategy is introduced to fuse features of different modalities from two branches. Compared with methods on the XD-Violence dataset, our multi-modal fusion network yields APs that are 83.09% and 1.4% higher than those of the state-of-the-art methods in offline detection, and 78.09% and 4.42% higher than those of the state-of-the-art methods in online detection.
SMANet: multi-region ensemble of convolutional neural network model for skeletal maturity assessment

Bone age assessment (BAA) is a crucial research topic in pediatric radiology. Interest in the development of automated methods for BAA is increasing. The current BAA algorithms based on deep learning have displayed the following deficiencies: (I) most methods involve end-to-end prediction, lacking integration with clinically interpretable methods; (II) BAA methods exhibit racial and geographical differences.A novel, automatic skeletal maturity assessment (SMA) method with clinically interpretable methods was proposed based on a multi-region ensemble of convolutional neural networks (CNNs). This method predicted skeletal maturity scores and thus assessed bone age by utilizing left-hand radiographs and key regional patches of clinical concern.Experiments included 4,861 left-hand radiographs from the database of Beijing Jishuitan Hospital and revealed that the mean absolute error (MAE) was 31.4±0.19 points (skeletal maturity scores) and 0.45±0.13 years (bone age) for the carpal bones-series and 29.9±0.21 points and 0.43±0.17 years, respectively, for the radius, ulna, and short (RUS) bones series based on the Tanner-Whitehouse 3 (TW3) method.The proposed automatic SMA method, which was without racial and geographical influence, is a novel, automatic method for assessing childhood bone development by utilizing skeletal maturity. Furthermore, it provides a comparable performance to endocrinologists, with greater stability and efficiency.
Flow Sequence-Based Anonymity Network Traffic Identification with Residual Graph Convolutional Networks

Identifying anonymity services from network traffic is a crucial task for network management and security. Currently, some works based on deep learning have achieved excellent performance for traffic analysis, especially those based on flow sequence (FS), which utilizes information and features of the traffic flow. However, these models still face a serious challenge because of lacking a mechanism to take into account relationships between flows, resulting in mistakenly recognizing irrelevant flows in FS as clues for identifying traffic. In this paper, we propose a novel FS-based anonymity network traffic identification framework to tackle this problem, which leverages Residual Graph Convolutional Network (ResGCN) to exploit relationships between flows for FS feature extraction. Moreover, we design a practical scheme to preprocess the raw data of real-world traffic, which further improves identification performance and efficiency. Experimental results on two real-world traffic datasets demonstrate that our method outperforms state-of-the-art methods by a large margin.
Evaluating Surprise Adequacy for Deep Learning System Testing

The rapid adoption of Deep Learning (DL) systems in safety critical domains such as medical imaging and autonomous driving urgently calls for ways to test their correctness and robustness. Borrowing from the concept of test adequacy in traditional software testing, existing work on testing of DL systems initially investigated DL systems from structural point of view, leading to a number of coverage metrics. Our lack of understanding of the internal mechanism of Deep Neural Networks (DNNs), however, means that coverage metrics defined on the Boolean dichotomy of coverage are hard to intuitively interpret and understand. We propose the degree of out-of-distribution-ness of a given input as its adequacy for testing: the more surprising a given input is to the DNN under test, the more likely the system will show unexpected behavior for the input. We develop the concept of surprise into a test adequacy criterion, called Surprise Adequacy (SA). Intuitively, SA measures the difference in the behavior of the DNN for the given input and its behavior for the training data. We posit that a good test input should be sufficiently, but not overtly, surprising compared to the training dataset. This article evaluates SA using a range of DL systems from simple image classifiers to autonomous driving car platforms, as well as both small and large data benchmarks ranging from MNIST to ImageNet. The results show that the SA value of an input can be a reliable predictor of the correctness of the mode behavior. We also show that SA can be used to detect adversarial examples, and also be efficiently computed against large training dataset such as ImageNet using sampling.
A Two-Stage Framework for Directed Hypergraph Link Prediction

Hypergraphs, as a special type of graph, can be leveraged to better model relationships among multiple entities. In this article, we focus on the task of hyperlink prediction in directed hypergraphs, which finds a wide spectrum of applications in knowledge graphs, chem-informatics, bio-informatics, etc. Existing methods handling the task overlook the order constraints of the hyperlink’s direction and fail to exploit features of all entities covered by a hyperlink. To make up for the deficiency, we present a performant pipelined model, i.e., a two-stage framework for directed hyperlink prediction method (TF-DHP), which equally considers the entity’s contribution to the form of hyperlinks, and emphasizes not only the fixed order between two parts but also the randomness inside each part. The TF-DHP incorporates two tailored modules: a Tucker decomposition-based module for hyperlink prediction, and a BiLSTM-based module for direction inference. Extensive experiments on benchmarks—WikiPeople, JF17K, and ReVerb15K—demonstrate the effectiveness and universality of our TF-DHP model, leading to state-of-the-art performance.
Online Entanglement Routing in Quantum Networks

Quantum Data Networks (QDNs) typically leverage teleportation to reliably send data quantum bits (called qubits) to their destinations. To teleport a data qubit from Alice to Bob, one entanglement connection between Alice and Bob needs to be established. Accordingly, we have to establish as many entanglement connections as possible with limited quantum resources in order to maximize the network throughput. Conventional methods assume a known traffic matrix and calculate the paths to establish entanglement connections in one batch using a centralized algorithm. However, these methods are not scalable in large scale QDNs since it is time consuming to optimize the entanglement paths for a batch of requests, which may result in a long time slot duration and significantly reduce the network throughput. To address this issue, we propose an Online Entanglement Routing (OER) scheme which determines the entanglement paths for each request when it arrives. In addition, OER pursues work conservation and fairness among all requests in the QDNs. Through extensive simulations, we demonstrate that OER not only outperforms two representative heuristics by up to 61.27% and 52.79%, respectively, in terms of average request completion time, but also achieves a better fairness performance than these two counterparts.
Augmented Reality Enhanced Thunderstorm Learning Experiences for General Aviation

Reviews of general aviation weather training curricula indicate gaps in how students experience different weather patterns and learn to correlate weather knowledge in weather-related situations. Augmented reality learning experiences immerse students in three-dimensional, animated, and interactive weather content. Students completing these augmented reality enhanced learning experiences have the opportunity to apply their knowledge in weather-related situations. In this study, augmented reality learning experiences were designed and developed to enhance how students learn about thunderstorms, the impacts of thunderstorms on flight, and thunderstorm avoidance. A user evaluation was conducted to assess students' learning outcomes and experiences. The study results found that student participants significantly improved their factual knowledge and visual knowledge, reported high motivation levels, and rated the learning experiences as appropriate and usable when trained in an augmented reality environment.
FedSyL: Computation-Efficient Federated Synergy Learning on Heterogeneous IoT Devices

As a popular privacy-preserving model training technique, Federated Learning (FL) enables multiple end-devices to collaboratively train Deep Neural Network (DNN) models without exposing local privately-owned data. According to the FL paradigm, resource-constrained end-devices in IoT should perform model training which is computation-intensive, whereas the edge server occupied with powerful computation capability only performs model aggregation. Due to the above unbalanced computation pattern, IoT-oriented FL is time-consuming and inefficient. In order to alleviate the computation burden of end-devices, recent countermeasures introduce the edge server to assist end-devices in model training. However, existing works neither efficiently address the computation heterogeneity across end-devices nor reduce the leakage risk of data privacy. To this end, we propose a Federated Synergy Learning (FedSyL) paradigm which innovatively strikes a balance between training efficiency and data leakage risk. We explore the complicated relationship between the local training latency and multi-dimensional training configurations, and design a uniform training latency prediction method by applying the polynomial quadratic regression analysis. Additionally, we design the optimal model offloading strategy with the consideration of resource limitation and computation heterogeneity of end-devices, so as to accurately assign capability=matched device-side sub-models for heterogeneous end-devices. We implement FedSyL on a real test-bed comprising multiple heterogeneous end-devices. Experimental results demonstrate the superiority of FedSyL on training efficiency and privacy protection.
Optimization of Software Quality Attributes using Evolutionary Algorithm

Software quality is a multidimensional concept. Single attribute can’t define the overall quality of the software. Software developer aims to develop software that possesses maximum software quality which depends upon various software quality attributes such as understand ability, flexibility, reusability, effectiveness, extendibility, functionality, and many more. All these software quality attributes are linked with each other and conflicting in nature. Further, these quality attributes depend upon the design properties of the software. During the designing phase of software, developers must optimize the design properties to develop good software quality. To obtain the appropriate value optimization is done. This paper implemented two multi-objective evolutionary algorithms (NSGA-2 and MOEA/D) to optimize software design properties to enhance software quality. While comparing NSGA-2 algorithm with original values it is found that there is a 1.73% improvement in the software quality on the other hand MOEA/D shows a 3.58% improvement in the software quality.
Blockchain in healthcare : Moving towards a methodological framework for protecting Biomedical Databases

Biomedical databases or repositories have scientific information that is evidence based and protecting such documents from tampering or non-repudiation is very significant. The traditional techniques for the same have limitations in the distributed environments. Scientific contributions are to be safeguarded and it is one of the challenging problems. Blockchain is the promising technology that can support distributed ledger of transactions and thus it is found suitable for protecting biomedical repositories. As blockchain is a proven technology associated with crypto-currency known as Bitcoin in finance domain, it has plenty of opportunities in other domains. In this paper, a framework that is based on blockchain technology (BCT) for protection of biomedical databases with integrity and non-repudiation is presented. The framework will have underlying mechanisms to exploit blockchain to have a protection service and smart contracts to be more flexible and dynamic to adapt new requirements from time to time. The framework is domain specific but can pave way for motivation for adapting it to new domains as well.
Enhanced Visual Analytics Technique for Content-Based Medical Image Retrieval

Content-based image retrieval (CBIR) is a method for searching that finds related images in a medical database. Furthermore, a clinical adaptation of CBIR is hampered in part by a contextual gap that is the disparity among the person characterization of the picture and the framework characterization of the image. This technique makes it tough for the user to validate the fetched images that are similar to the query image in addition to that it only fetches the images of top-ranked and ignores the low-ranking ones. Visual Analytics for Medical Image Retrieval is a novel procedure for medicinal CBIR proposed in this research (VAMIR). By integrating human and machine analysis, Visual Analytics provides the potential to address the above-mentioned significant challenges. The texture properties are retrieved using the shape features extraction and Gray Level Co-occurrence Matrix (GLCM) is performed by contour-based shape descriptor. Using the Euclidean distance correlation metric, related medical pictures will be fetched by distinguishing the query image's attribute vector with the database images' respective attribute vectors. A vector of multiple features outperforms a vector of a single feature in terms of quality. The VAMIR implementation demonstrates that the search outcome for the user is acquired with 90% of recall and precision.
Quantum Water Strider Algorithm with Hybrid-Deep-Learning-Based Activity Recognition for Human–Computer Interaction

Human action and activity recognition are clues that alleviate human behavior analysis. Human action recognition (HAR) becomes a significant challenge in various applications involving human computer interaction (HCI) and intellectual video surveillance for enhancing security in distinct fields. Precise action recognition is highly challenging because of the variations in clutter, backgrounds, and viewpoint. The evaluation method depends on the proper extraction and learning of data. The achievement of deep learning (DL) models results in effectual performance in several image-related tasks. In this view, this paper presents a new quantum water strider algorithm with hybrid-deep-learning-based activity recognition (QWSA-HDLAR) model for HCI. The proposed QWSA-HDLAR technique mainly aims to recognize the different types of activities. To recognize activities, the QWSA-HDLAR model employs a deep-transfer-learning-based, neural-architectural-search-network (NASNet)-based feature extractor to generate feature vectors. In addition, the presented QWSA-HDLAR model exploits a QWSA-based hyperparameter tuning process to choose the hyperparameter values of the NASNet model optimally. Finally, the classification of human activities is carried out by the use of a hybrid convolutional neural network with a bidirectional recurrent neural network (HCNN-BiRNN) model. The experimental validation of the QWSA-HDLAR model is tested using two datasets, namely KTH and UCF Sports datasets. The experimental values reported the supremacy of the QWSA-HDLAR model over recent DL approaches.
Performance Analysis of Quantum Classifier on Benchmarking Datasets

Quantum machine learning (QML) is an evolving field which is capable of surpassing the classical machine learning in solving classification and clustering problems. The enormous growth in data size started creating barrier for classical machine learning techniques. QML stand out as a best solution to handle big and complex data. In this paper quantum support vector machine (QSVM) based models for the classification of three benchmarking datasets namely, Iris species, Pumpkin seed and Raisin has been constructed. These QSVM based classification models are implemented on real-time superconducting quantum computers/simulators. The performance of these classification models is evaluated in the context of execution time and accuracy and compared with the classical support vector machine (SVM) based models. The kernel based QSVM models for the classification of datasets when run on IBMQ_QASM_simulator appeared to be 232, 207 and 186 times faster than the SVM based classification model. The results indicate that quantum computers/algorithms deliver quantum speed-up.
Why Don't You Click

Web search heavily relies on click-through behavior as an essential feedback signal for performance evaluation and improvement. Traditionally, click is usually treated as a positive implicit feedback signal of relevance or usefulness, while non-click is regarded as a signal of irrelevance or uselessness. However, there are many cases where users satisfy their information need with the contents shown on the Search Engine Result Page (SERP). This raises the problem of measuring the usefulness of non-click results and modeling user satisfaction in such circumstances.