Reviewerly/exHarmony huggingface dataset:
Triple Factorization-Like Symmetric NLF Models With Latent Item–Item Relationship

Undirected, high-dimension and sparse (SHiDS) networks which are often encountered in industrial applications usually contain numerous useful information; thus, it is practically important to develop effective collaborative filtering methods to rigidly extract the latent information. In order to improve the performance of nonnegative latent factor (NLF) models based on the double factorization while reducing the computation burden of NLF models in terms of the triple factorization (TF) technique, a novel symmetric NLF (SNLF) model, i.e., TF-like SNLF model, is proposed in this article. A latent factor (LF) matrix that reflects the relationship of items is introduced into the objective function so as to offer more freedom for solutions. Then, a fixed weighting matrix designed by sensitivity experiments is used to replace the aforementioned LF matrix dependent on the update, which greatly helps save the time cost without sacrificing any accuracy. Furthermore, the alteration direction (ADI) method is adopted to design the algorithm, which effectively helps reduce the occurrence of the local minimum while guaranteeing convergence. Finally, experimental results on four real industrial data sets demonstrate that the proposed TF-like SNLF model makes a good compromise between the model performance and the time efficiency.
Gaze Controlled Safe HRI for Users with SSMI

Robots are already used for rehabilitation of people with disabilities mainly in the form of prosthetics or exoskeleton. This paper proposes a Markov Decision Process based safe distance maintenance algorithm to make a small robotic manipulator accessible to users with severe speech and motor impairment (SSMI). We have developed a video see through eye gaze controlled graphical user interface for operating the robotic arm while an intelligent agent monitors presence of users' hand in the vicinity of the robot and plans a navigation path using value iteration algorithm. A user study involving 5 users with SSMI and 7 able-bodied users found the system safe and usable by all users undertaking tasks within 3 minutes and significant decrease in task completion time with practice.
Design, Reachability Analysis, and Constrained Motion Planning for a Quadcopter Manipulator System

This paper presents a detailed workspace analysis of an aerial robot and a motion planning algorithm to generate the robotic arm's feasible motion in the reachable workspace. An aerial robot consists of a quadcopter and a three degree of freedom manipulator attached to the bottom of the aerial vehicle. The quadcopter-manipulator system can reach otherwise hard-to-reach locations, and the manipulator can perform different tasks. Serial link manipulators have a larger workspace and relatively uncomplicated kinematics. In the proposed system, the manipulator's workspace is constrained due to various factors like the downwash of the drone, torque limits, singular configurations, and mechanical structure. The effect of these factors on the reachable workspace is analytically studied in this work. Based on the constraints, a reachable workspace is derived. Finally, a motion planning algorithm that considers these constraints and generates feasible arm configurations is developed. A minimum acceleration trajectory is then generated to reach a given target location. The proposed motion planning algorithm is implemented on the quadcopter manipulator system's unified dynamics, simulated, and validated in MATLAB and ROS/Gazebo platform.
RAIDU-Net: Image Inpainting via Residual Attention Fusion and Gated Information Distillation

Most existing deep learning image inpainting methods cannot extract the effective information from broken images and reduce interference of masked partial information in broken images. To address this issue, we propose an image inpainting model based on a residual attention fusion block and a gated information distillation block. Our model adopts U-net as the backbone of the generator to realize the encoding and decoding operations of the image. The encoder and decoder employ the residual attention fusion block, which can enhance the utilization of practical information in the broken image and reduce the interference of redundant information. Moreover, we embed the gated information distillation block in the skip-connection of the encoder and decoder, which can further extract useful low-level features from the generator. Experiments on public databases show that our RAIDU-Net architecture achieves promising results and outperforms the existing state-of-the-art methods.
MIASNet: A medical image segmentation method predicting future based on past and current cases

Fast and accurate segmentation of medical images is essential for the diagnosis and treatment of diseases. The automatic segmentation technology based on deep learning has achieved encouraging performance in segmentation accuracy. However, the improvement of segmentation accuracy usually requires a larger network structure, which also leads to a decrease in segmentation speed. In this study, we propose a medical image anticipation segmentation net (MIASNet), in order to further improve the segmentation speed under the premise of excellent segmentation accuracy. For 3D medical images, we use the spatial association of the previous frame and the current frame as input data to predict the segmentation results of the next frame. Our approach consists of three lightweight sub-networks, which are used to learn the mapping relationship of the spatial domain. In order to make full use of the generating ability of the deep learning network, we use group convolution to obtain diversified prediction results. On the multimodal brain tumor image segmentation (BraTS) 2020 dataset, MIASNet achieves excellent segmentation accuracy without using the target frame that need to be segmented as the network input. Therefore, our proposed segmentation network can be used in a wider range of real-time medical applications.
Voiced Odia Digit Recognition Using Convolutional Neural Network

Speech recognition in a native language like Odia is a challenging job, particularly for handling tasks in real-life conditions. Voiced Odia digit classification is a part of Odia speech recognition. It is helpful for designing automatic voice dialler systems for physically challenged and illiterate people. Also, it can be used for setting up the PIN numbers for credit and debit cards and entering OTP numbers through voices. In this study, a Convolutional Neural network (CNN) is used for the recognition of voiced Odia digits. The spectrogram representation of voiced samples are given as the input to the CNN model after considering the feature extraction using MFCC. Various performance metrics are obtained by considering a number of experiments with different epoch sizes and variations in data set using the train-validate-test ratio. Experimental outcomes reveal that, the proposed model provides improved accuracy of 91.58% in epoch size of 300 with a split ratio of 80-10-10. From the reported outcome it unravels that, the proposed model has better average recognition accuracy as compared with contemporary models like HMM and SVM.
A Survey of techniques for fine-grained web traffic identification and classification

After decades of rapid development, the scale and complexity of modern networks have far exceed our expectations. In many conditions, traditional traffic identification methods cannot meet the demand of modern networks. Recently, fine-grained network traffic identification has been proved to be an effective solution for managing network resources. There is a massive increase in the use of fine-grained network traffic identification in the communications industry. In this article, we propose a comprehensive overview of fine-grained network traffic identification. Then, we conduct a detailed literature review on fine-grained network traffic identification from three perspectives: wired network, mobile network, and malware traffic identification. Finally, we also draw the conclusion on the challenges of fine-grained network traffic identification and future research prospects.
Remote Device Management via Smart Contracts

With the continued scaling of transistor feature size, the cost of IC development has been escalating. The economics of semiconductor IC development favors high volume manufacturing, while high volume cannot be attained without developing an IC that serves many applications. Some of these applications are in low-margin Internet of Things (IoT) devices, where an SoC cannot command a high price. Consequently, without the ability to customize IC features after production, its lowest-priced application will determine an IC’s price. This motivates the manufacturers to develop chips with provisions for post-manufacturing IC customization. This paper explores how an IC can be programmed repeatedly and securely using blockchain-based smart contracts to allow users to upgrade or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rent</i> features. The availability of such a system could, for example, allow a buyer to upgrade her processor from a low-end to a high-end part by making an additional payment to the manufacturer. Implementing such a system will require remote device management capabilities. Remote device management presents unique design considerations, such as, necessity for transparency of the actions that a device takes on behalf of a user; the requirement of a trusted arbiter; and provision for management of these devices beyond the intended lifespan. To overcome the challenges of transparency, longevity, and the necessity of a trusted arbiter, we propose embedded smart contracts in concert with a blockchain. Our proposed smart contract takes the device feature configuration request as input and outputs the corresponding configuration. To support remote, secure, and authorized updates, we propose an on-die hardware module which communicates with the smart contract and enforces its functionalities. This was prototyped using a programmable system-on-chip working in concert with Ethereum blockchain. The prototype demonstrates the feasibility and practicality of the proposed solution.
Sentiment Analysis Based on Bert and Transformer

With the development of social media, the scale of comment text on network platform is growing rapidly. How to judge the emotional polarity of comments quickly and accurately is of great significance in the fields of commodity review and public opinion monitoring. Short text reviews have the problem of sparse features. At present, most of the sentiment analysis models use TF-IDF, word2vec and groove to obtain word vector representation, which can’t fully express the context semantic information of the text. Moreover, the mainstream bidirectional recurrent neural network model relies on the sequence information to a large extent, so it is difficult to pay attention to the important information of the text. To solve this problem, this paper proposes a sentiment analysis method which combines the best word vector and the transformer self-attention model. The rich semantic representation information is obtained through the Bert model, and the feature weight is dynamically adjusted by the self-attention model to obtain the sentiment classification of short text. The experimental results show that the Bert word vector can enhance the semantic representation of the text, and the self-attention model can reduce the dependence of external parameters, which makes the model pay more attention to its own key information, and the classification performance is significantly improved. There are two innovations in this paper. Firstly, the problem of sparse features of short text is solved by using the Bert word vector to improve the semantic expression of the text and obtain more comprehensive semantic information; secondly, the feature information is weighted by self-attention mechanism to highlight the important information of the text.
Radar Signal Separation Recognition Method based on Semantic Segmentation

With the increasing application of electronic technology in military field, electronic countermeasure technology has been developed gradually. The separation and identification of radar signal is an important part of electronic countermeasure. Only when the enemy's information is fully grasped in the war, can the absolute advantage be obtained in the battle, which makes the separation and identification of radar signal play a very important role. However, radar signal identification is faced with serious time-frequency domain overlap problem, and the analysis of multi-component radar signals and the acquisition of valuable information are still faced with great difficulties, which is an urgent problem to be solved in radar reconnaissance system. In this paper, a one-dimensional signal is represented as a time-frequency graph (TFIS) by using the Choi-Williams distribution (CWD), and then the recognition results are obtained by using UNet to segment the time-frequency image of the signal, and the recognition results are visualized. The simulation results show that the proposed method can effectively solve the multi-signal separation and recognition of time-frequency aliasing.
Model of UAV and Downwash for Multi-UAV Path Planning

This paper develops a method to model the air flow downwash effect generated by the quadrotor unmanned aerial vehicle (UAV) and its effect on the neighboring UAVs. The downwash model derives the resultant downwash force and torque and takes the UAV attitude into the account. Each UAV is shaped by a virtual structure for collision-free path planning. The shape is modified from a standard spherical body to a proposed cylinder to better minimize downwash impact. A flock-based path planning algorithm and an optimal reciprocal collision avoidance (ORCA) algorithm are implemented and investigated in this study to analyze the downwash effect and the performance of the proposed cylindrical shape UAV model. The downwash model simulation shows how the UAV can be affected when it counters the downwash air flow. A flock-based algorithm and an ORCA algorithm along with the spherical and cylindrical shape UAV models are simulated to demonstrate the cylindrical model can improve path planning performance.
Improving Learning-Based DAG Scheduling by Inserting Deliberate Idle Slots

The increasing demands of computing capabilities make it expensive to operate a large-scale cloud cluster. A good scheduling algorithm should be able to reduce the average job completion time (JCT), which is the time duration between a job&#x0027;s arrival and its termination. However, when considering the precedence constraint of stages in each job, and when jobs arrive online, designing a scheduler to minimize the average JCT is challenging. Counterintuitively, we find that inserting idle time before some jobs might reduce the JCT, which is ignored by many schedulers. The state-of-the-art scheduler, which uses reinforcement learning (RL) techniques to solve scheduling problems, does not consider deliberate idle time. We integrate our observations to the RL agent and let the agent learn the best length of idle time. We carefully design the features used in RL. The shape of each job DAG is captured by the critical path length and the average width, and the detailed precedence constraints in each job DAG are extracted by graph neural networks. The experiment results on both synthetic and realworld datasets show that inserting the deliberate idle time could reduce the average JCT. Also, the results illustrate the significant contribution made by our proposed features.
Deep Learning and Linguistic Feature Based Automatic Multiple Choice Question Generation from Text

Multiple Choice Questions (MCQs) feature more and more in modern-day assessments due to more straightforward exams and faster checking. However, with this comes the challenge of creating a diverse pool of MCQs specific to a given subject matter. This work proposes an MCQ generation system that uses linguistic features and Deep Learning techniques to create MCQs from a given text. The entire process has three steps: i) The pre-trained DL state-of-the-art model summarizes a text paragraph to get relevant information. ii) Linguistic features generate question (stem) and answer (key) pairs. iii) Distractor generation using the key or correct answer. DL-based paraphrasing models are used to augment the MCQs dataset with questions of similar type and difficulty level.
Privacy and Trust Models for Cloud-Based EHRs Using Multilevel Cryptography and Artificial Intelligence

The pervasive as well as service-oriented nature of cloud computing has necessitated its adoption for development, deployment and servicing of Health Records Management solutions in today's modern world. However, there are rapidly growing concerns for security/privacy and trustworthiness of such cloud-based systems/services. The dynamics of electronic health records (EHR) management system makes privacy preservation and trust, non-negotiable features of a presumably reliable cloud service. To cater for these concerns, this chapter proposes a multilevel cryptography approach for achieving security (privacy) in electronic health system. This approach involves using AES algorithm for on-premise encryption service and ECC for cloud-based encryption services towards ensuring data security/privacy at both the local hospitals' electronic medical record (EMR) systems and the central cloud electronic health record (EHR) ends. Also, an artificial intelligence (AI) approach, which is referred to as subjective logic-based belief or reasoning model, was used towards measuring the trustworthiness of the medical personnel. Metrics such as the average system computation time otherwise referred to as the encryption/decryption runtime, system throughput, and system output size were utilized for appraising the privacy model in the newly developed system. The system was also compared with existing related systems in terms of standard reputation measurement metrics in order to determine its reputation performance. Results obtained from implementation shows that the proposed system offer better or highly improved performance in terms of security/privacy for users' data, as well as reputation measurements.
The Reality of Electronic Distance Learning in the Presence of COVID19 Pandemic from Jordanian Student Perspective

This paper identify the reality of distance learning in the presence of the emerging corona pandemic "COVID 19" from the student point of view in the practice in Jordan student as case study. To achieve the aim of the study, the descriptive and analytical approach was used, whereby we prepare an electronic questionnaire that include 20 questions that were distributed electronically to a random sample of 318 students. The result of the study conclude the following: The importance of using e-learning systems in distance learning programs. Some difficulty face the student during the use of distance learning programs such as lack of resources. Student absence shows statistically significant differences at each student levels in responses of the sample student members. The results of the paper study recommend and encouraging the ministry of education in Jordan to benefit from e-learning technology to facilitate and improve educational learning practice.
Driving Behavior Aware Caption Generation for Egocentric Driving Videos Using In-Vehicle Sensors

Video captioning aims to generate textual descriptions according to the video contents. The risk assessment of autonomous driving vehicles has become essential for an insurance company for providing adequate insurance coverage, in particular, for emerging MaaS business. The insurers need to assess the risk of autonomous driving business plans with a fixed route by analyzing a large number of driving data, including videos recorded by dash cameras and sensor signals. To make the process more efficient, generating captions for driving videos can provide insurers concise information to understand the video contents quickly. A natural problem with driving video captioning is, since the absence of egovehicles in these egocentric videos, descriptions of latent driving behaviors are difficult to be grounded in specific visual cues. To address this issue, we focus on generating driving video captions with accurate behavior descriptions, and propose to incorporate in-vehicle sensors which encapsulate the driving behavior information to assist the caption generation. We evaluate our method on the Japanese driving video captioning dataset called City Traffic, where the results demonstrate the effectiveness of in-vehicle sensors on improving the overall performance of generated captions, especially on generating more accurate descriptions for the driving behaviors.
A Gesture Recognition Method Based on YCbCr and SURF for Service Robot Interaction

With the increasing requirements of human-computer interaction experience, gesture recognition is used in many applications as a novel interaction method. However, the effects of complex backgrounds, occlusions and illumination pose difficulties for the successful recognition of gestures. For this problem, we propose a gesture recognition method based on the combination of SURF and YcbCr. First, RGB-D was calibrated, and then the collected hand images were processed for noise reduction. Secondly, the coarse recognition of gestures is completed by SURF feature point matching algorithm, and then the fine recognition of gestures is achieved by YCbCr skin tone segmentation algorithm. Finally, the gesture images are further processed by a combined morphological algorithm to improve the integrity of gesture recognition and reduce the influence of interfering factors. The experimental results show that the method can recognize gestures quickly and effectively with certain accuracy and robustness.
Named Entity Recognition using Negative Sampling and Reinforcement Learning

In this paper we propose a new named entity recognition (NER) model that combines negative sampling and reinforcement learning to reduce the impact of unlabeled entities and make full use of the labeled entities. Generally, there are a small number of correct entities in the training data which are not labeled inevitably. These unlabeled entities will greatly affect the performance of NER models. So in our model reinforcement learning is exploited to select the negative instances correctly and avoid those unlabeled entities for NER classifier because unlabeled entities are noisy data for negative instances. Then a NER classifier is trained on these negative instances and the manually labeled entities. Thus our model can be trained in the right direction under the guidance of the labeled entities and eliminate the influence of unlabeled entities. To evaluate our model, experiments are conducted on three public datasets, BioNLP11EPI, BioNLP13CG and BioNLP13GE, and with two kinds of embeddings, BERT and word2vec. Experiments show that our model can outperform the most of other models, and achieve comparable performance to the PFT model. Notably PFT need collect a large number of related sentences from unlabeled documents to pre-fine-tune BERT while our model need no additional corpus for training. Meanwhile when some entities are masked randomly, the performance of our model is significantly better than negative sampling model.