Reviewerly/exHarmony huggingface dataset:
MIDAS: Towards Efficient and Effective Maintenance of Canned Patterns in Visual Graph Query Interfaces

Several visual graph query interfaces (a.k.a gui) expose a set of canned patterns (i.e., small subgraph patterns) to expedite subgraph query formulation by enabling pattern-at-a-time construction. Unfortunately, manual generation of canned patterns is not only labour intensive but also may lack diversity to support efficient visual formulation of a wide range of subgraph queries. Recent efforts have taken a data-driven approach to select high-quality canned patterns for a gui automatically from the underlying graph database. However, as the underlying database evolves, these selected patterns may become stale and adversely impact efficient query formulation. In this paper, we present a novel framework called Midas for efficient and effective maintenance of the canned patterns as the database evolves. Specifically, it adopts a selective maintenance strategy that guarantees progressive gain of coverage of the patterns without sacrificing their diversity and cognitive load. Experimental study with real-world datasets and visual graph interfaces demonstrates the effectiveness of Midas compared to static guis.
Can audio-visual integration strengthen robustness under multimodal attacks?

In this paper, we propose to make a systematic study on machines’ multisensory perception under attacks. We use the audio-visual event recognition task against multi-modal adversarial attacks as a proxy to investigate the robustness of audio-visual learning. We attack audio, visual, and both modalities to explore whether audio-visual integration still strengthens perception and how different fusion mechanisms affect the robustness of audio-visual models. For interpreting the multimodal interactions under attacks, we learn a weakly-supervised sound source visual localization model to localize sounding regions in videos. To mitigate multimodal attacks, we propose an audio-visual defense approach based on an audio-visual dissimilarity constraint and external feature memory banks. Extensive experiments demonstrate that audio-visual models are susceptible to multimodal adversarial attacks; audio-visual integration could decrease the model robustness rather than strengthen under multimodal attacks; even a weakly-supervised sound source visual localization model can be successfully fooled; our defense method can improve the invulnerability of audio-visual networks without significantly sacrificing clean model performance. The source code and pre-trained models are released in https://github.com/YapengTian/AV-Robustness-CVPR21.
Social Robots in Service Contexts: Exploring the Rewards and Risks of Personalization and Re-embodiment

Social agents and robots are moving into front-line positions in brick and mortar services, taking on roles where they directly interact with customers. These agents could potentially recognize customers to personalize service. Will customers like this, or might they feel monitored and profiled? Robots could also re-embody (move their "personality" between one body and another) in order to take on multiple roles that are typically performed by different people. Will this make customers feel more taken care of, or will it raise concerns about the robot's competence and expertise? Our work investigates when robots should and should not recognize customers and re-embody. Our online study used storyboards to present possible future interactions between robots and customers across several different service contexts. Our findings suggest that people generally accept robots identifying customers and taking on vastly different roles. However, in some contexts, these robot behaviors seem creepy and untrustworthy.
Surface finish classification using depth camera data

We propose a novel approach for surface finish classification of digitally fabricated structures using an industrial depth camera. Data collected at different viewpoints are jointly processed to derive the spatial distribution of features describing the reflectance, which is in turn related to the surface finish. The features can be used to classify the surfaces according to their finish e.g., for assessing the homogeneity or conformance. We apply the method to four sprayed plaster specimens of similar visual appearance but different roughness. Using nearest neighbor classification we achieve an accuracy of 97% for the plaster samples. The approach is a contribution towards real-time quality inspection in digital fabrication.
DDPG with Meta-Learning-Based Experience Replay Separation for Robot Trajectory Planning

Prioritized experience replay (PER) chooses the experience data based on the value of Temporal-Difference (TD) error, it can improve the utilization of experience in deep reinforcement learning based methods. But since the value of TD error needs to be calculated when experience data sampled at each time, so the computational complexity of PER is high. And the hyperparameters in PER also affect the learning process. The hyperparameters need to be adjusted carefully. To cope above problems, we propose meta-learning-based experience replay buffer separation (MSER) in this paper. Firstly, the original experience replay buffer is divided into a successful experience buffer and a failure experience buffer, and the experience data of successful exploration or failure exploration are stored respectively. Secondly, the experience data is sampled randomly from two experience replay buffers according to a ratio, and the ratio is learned through the neural network designed by us. Finally, we conduct experiments in a simulation environment for trajectory planning of robot manipulator in V-REP. Extensive experiments show that the MSER proposed is capable of improving convergence rate by up to 9.1% in DDPG, compared to DDPG with PER. The convergence mean reward rises by up to 3.3% and the standard deviation reduce by 16.9%. In the evaluation, the success rate of trajectory planning for robot manipulator is up to 99%.
Hate Speech Detection Using Natural Language Processing: Applications and Challenges

The internet has become a common platform for everyone to share their ideas and opinions. The user has freedom to post whatever he/she likes in social networking and blogging sites. However, sometimes the content when directed towards certain group of individuals with an intention to incite hate or discrimination, causes a turmoil in the society. Such content is known as hate speech. Hate speech can be a serious problem to peace and harmony in the society. There are instances where hate speech have led to social unrest and extremism. Thus, hate speech in the internet needs to be monitored. In this paper, we discuss the relevant works done in the field of hate speech detection. Different types of hate speech like racism, sexism, religious hate speech, etc. and the various methods proposed to tackle them are discussed. Further, we identify the challenges and propose the solutions to challenges in hate speech detection in the public internet sphere.
DeepDive

Deep Separable Convolutional Neural Network (DSCNN) has become the emerging paradigm by offering modular networks with structural sparsity to achieve higher accuracy with relatively lower operations and parameters. However, there is a lack of customized architectures that can provide flexible solutions that fit the sparsity of the DSCNNs. This paper introduces DeepDive, a fully-functional vertical co-design framework, for power-efficient implementation of DSCNNs on edge FPGAs. DeepDive's architecture supports crucial heterogeneous Compute Units (CUs) to fully support DSCNNs with various convolutional operators interconnected with structural sparsity. It offers FPGA-aware training and online quantization combined with modular synthesizable C++ CUs, customized for DSCNNs. The execution results on Xilinx's ZCU102 FPGA board demonstrate 47.4 and 233.3 FPS/Watt for MobileNet-V2 and a compact version of EfficientNet, respectively, as two state-of-the-art depthwise separable CNNs. These comparisons showcase how DeepDive improves FPS/Watt by 2.2× and 1.51× over Jetson Nano high and low power modes, respectively. It also enhances FPS/Watt by about 2.27× and 37.25× over two other FPGA implementations.
HARfog: An Ensemble Deep Learning Model for Activity Recognition Leveraging IoT and Fog Architectures

Ambulatory monitoring has become predominant in today's world where monitoring and recognition of activities and physiological parameters of human beings are required. Continuous monitoring of physiological parameters from elderly people, infected children and patients in remote places pave effective prediction and prevention of diseases. Activity recognition is mandatory for mentally challenged people, so that they can be cautioned during vulnerable activities. An effective and efficient Internet of Medical Things (IoMT) architecture leveraging fog computing and deep learning algorithms are required to handle such time critical tasks. The gateway layer, composed of commodity devices, contains algorithms for processing and analyzing the data extracted out from the human being via sensors. The response time for the task can be minimized by reducing the number of hits to the cloud layer, avoiding the unnecessary data transit time of readings from physiological sensors. In this paper, we have developed an ensemble deep learning paradigm to recognize activities of human beings through sensors. The paradigm is orchestrated with a fog-assisted cloud computing framework, FogBus. The accuracy of the model against mHealth dataset is observed and the performance of the architecture is evaluated leveraging FogBus framework.
Application of A-Star Algorithm on Pathfinding Game

Abstract Pathfinding is the method for determining the shortest path. Pathfinding is widely used in various fields of Computer Science. One of them is game development. This research will design and analyze the pathfinding using the A-Star algorithm and implement it on a tree planting game. The Artificial Intelligence in the game will find its own shortest path from the start node to the goal node. The heuristic of A-Star is the Manhattan Distance that allows movement in four different directions. The result shows that the number of visited nodes will increase if the grid has many obstacle nodes, and the average time of the A-Star pathfinding process is 0.0732 seconds.
Cybersecurity Training in the Healthcare Workforce – Utilization of the ADDIE Model

Cybersecurity attacks in healthcare have evolved towards social engineering and human-based intervention, showing increased frequency and success of such attacks. Increased priority and resources towards human factors training can reduce cybercriminal opportunity in healthcare. A cybersecurity workshop, developed following the ADDIE model, allowed frontline healthcare workforce and nursing students to increase their knowledge and confidence abilities against windows of opportunity for cybercrime. Participants confidence in cybersecurity skills use in daily practice significantly increased while participants knowledge showed a positive tendency. A list of actions for enhancing the cybersecurity in healthcare training is discussed as guidance for current and future curricula.
Forward-Reverse Observational Equivalences in CCSK

In the context of CCSK, a reversible extension of CCS, we study observational equivalences that distinguish forward moves from backward ones. We present a refinement of the notion of forward-reverse bisimilarity and show that it coincides with a notion of forward-reverse barbed congruence. We also show a set of sound axioms allowing one to reason equationally on process equivalences.
Person image generation with attention-based injection network

Person image generation becomes a challenging problem due to the content ambiguity and style inconsistency. In this paper, we propose a novel Attention-based Injection Network (AIN) to address this issue. Instead of directly learning the relationship between the source and target image, we decompose the process into two accessible modules, namely Semantic-guided Attention Network (SAN) and Pose-guided Attention Network (PAN). SAN is proposed to capture the semantic information which can embed the human attributes into the latent space via the semantic layout. PAN enables a natural re-coupling of the pose and appearance, which can selectively integrate features to complete the human pose transformation. Additionally, a semantic layout loss is proposed to focus on the semantic content similarity between the source and generated images. Compared with other methods, our networks can enforce the local textures and styles consistency between the source and generated image. Experiments show that superior both qualitative and quantitative results are obtained on Market-1501 and DeepFashion datasets. On the basis of AIN, our network can further achieve the data augmentation for person re-identification (Re-ID) with dramatically improving the person Re-ID accuracy.
Hybrid Speech and Text Analysis Methods for Speaker Change Detection

Speaker Change Detection (SCD) is the task of segmenting an input audio-recording according to speaker interchanges. Nowadays, many applications, such as Speaker Diarization (SD) or automatic vocal transcription, depend on this segmentation task. In this paper, we focus on the essential task of the SD problem, the audio segmenting process, and suggest a solution for the SCD problem, as well as the assignment of clustered speaker labels for the extracted segments, and applying the solution over two datasets: a commercial dataset in Hebrew and the ICSI Meeting Corpus. As such, we propose a hybrid framework for the SCD problem that is learned by textual information and speech signals and the meta-data features that can be extracted from them. Moreover, we demonstrate the negative correlation between an increase in the number of speakers in the training dataset and the influence on the overall diarization system's performance, which is improved using our efficient SCD component. Finally, we show how our proposed hybrid framework remains robust compared to the ICSI Meeting Corpus, as the experimental evaluation's training and testing is based on two languages.
Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives

Abstract Purpose of Review Automated breast ultrasound (ABUS) is a three-dimensional imaging technique, used as a supplemental screening tool in women with dense breasts. This review considers the technical aspects, pitfalls, and the use of ABUS in screening and clinical practice, together with new developments and future perspectives. Recent Findings ABUS has been approved in the USA and Europe as a screening tool for asymptomatic women with dense breasts in addition to mammography. Supplemental US screening has high sensitivity for cancer detection, especially early-stage invasive cancers, and reduces the frequency of interval cancers. ABUS has similar diagnostic performance to handheld ultrasound (HHUS) and is designed to overcome the drawbacks of operator dependence and poor reproducibility. Concerns with ABUS, like HHUS, include relatively high recall rates and lengthy reading time when compared to mammography. ABUS is a new technique with unique features; therefore, adequate training is required to improve detection and reduce false positives. Computer-aided detection may reduce reading times and improve cancer detection. Other potential applications of ABUS include local staging, treatment response evaluation, breast density assessment, and integration of radiomics. Summary ABUS provides an efficient, reproducible, and comprehensive supplemental imaging technique in breast screening. Developments with computer-aided detection may improve the sensitivity and specificity as well as radiologist confidence and reduce reading times, making this modality acceptable in large volume screening centers.
Early Warning System of Tennis Sports Injury Risk Based on Mobile Computing

In recent years, sports injuries in professional tennis players have gradually increased and sports injuries will break the sports training system and affect the long-term growth of new tennis players. Avoiding athlete injuries has become an important factor in improving training quality and game performance and ensuring the sustainable development of young tennis players’ competitiveness. Therefore, this article will use the RBF neural network algorithm and cluster analysis method to establish a tennis sports injury risk early warning model and finally establish a tennis sports injury risk early warning system so that tennis players can reduce their injuries. In this article, we use the questionnaire survey method, expert interview method, mathematical statistics method, and logical analysis method to investigate and analyze the results of training injuries of Chinese tennis players and coaches. The experimental results in this article show that among 48 tennis players of different ages, who are participating in formal training and tennis competitions, 15 young tennis players have been injured more than 6 times, accounting for 31.2% of the total; 20 have been injured 3 to 6 times, accounting for 41.7% of the total; 9 of them have been injured several times, accounting for 18.8% of the total; and 4 have been injured, accounting for 8.3% of the total. After using the tennis sports injury risk warning system based on the algorithm of RBF neural network in mobile computing, the tennis sports injury rate has dropped to 5%. It can be seen that the system has high feasibility and practicability.
Automatic liver and tumor segmentation based on deep learning and globally optimized refinement

Abstract Automatic segmentation of the liver and hepatic lesions from abdominal 3D computed tomography (CT) images is fundamental tasks in computer-assisted liver surgery planning. However, due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver, accurate liver segmentation and tumor detection are still challenging problems. To address these difficulties, we propose an automatic segmentation framework based on 3D U-net with dense connections and globally optimized refinement. Firstly, a deep U-net architecture with dense connections is trained to learn the probability map of the liver. Then the probability map goes into the following refinement step as the initial surface and prior shape. The segmentation of liver tumor is based on the similar network architecture with the help of segmentation results of liver. In order to reduce the influence of the surrounding tissues with the similar intensity and texture behavior with the tumor region, during the training procedure, I × liver label is the input of the network for the segmentation of liver tumor. By doing this, the accuracy of segmentation can be improved. The proposed method is fully automatic without any user interaction. Both qualitative and quantitative results reveal that the proposed approach is efficient and accurate for liver volume estimation in clinical application. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and non-reproducible manual segmentation method.
Analysis of speech MEL scale and its classification as big data by parameterized KNN

Recognizing emotions and human speech has always been an exciting challenge for scientists. In our work the parameterization of the vector is obtained and realized from the sentence divided into the containing emotional-informational part and the informational part is effectively applied. The expressiveness of human speech is improved by the emotion it conveys. There are several characteristics and features of speech that differentiate it among utterances, i.e. various prosodic features like pitch, timbre, loudness and vocal tone which categorize speech into several emotions. They were supplemented by us with a new classification feature of speech, which consists in dividing a sentence into an emotionally loaded part of the sentence and a part that carries only informational load. Therefore, the sample speech is changed when it is subjected to various emotional environments. As the identification of the speaker’s emotional states can be done based on the Mel scale, MFCC is one such variant to study the emotional aspects of a speaker’s utterances. In this work, we implement a model to identify several emotional states from MFCC for two datasets, classify emotions for them on the basis of MFCC features and give the correspondent comparison of them. Overall, this work implements the classification model based on dataset minimization that is done by taking the mean of features for the improvement of the classification accuracy rate in different machine learning algorithms. In addition to the static analysis of the author's tonal portrait, which is used in particular in MFFC, we propose a new method for the dynamic analysis of the phrase in processing and studying as a new linguistic-emotional entity pronounced by the same author. Due to the ranking by the importance of the MEL scale features, we are able to parameterize the vectors coordinates be processed by the parametrized KNN method. Language recognition is a multi-level task of pattern recognition. Here acoustic signals are analyzed and structured in a hierarchy of structural elements, words, phrases and sentences. Each level of such a hierarchy may provide some temporal constants: possible word sequences or known types of pronunciation that reduce the number of recognition errors at a lower level. An analysis of voice and speech dynamics is appropriate for improving the quality of human perception and the formation of human speech by a machine and is within the capabilities of artificial intelligence. Emotion results can be widely applied in e-learning platforms, vehicle on-board systems, medicine, etc
Multi-View Face Synthesis via Progressive Face Flow

Existing GAN-based multi-view face synthesis methods rely heavily on “creating” faces, and thus they struggle in reproducing the faithful facial texture and fail to preserve identity when undergoing a large angle rotation. In this paper, we combat this problem by dividing the challenging large-angle face synthesis into a series of easy small-angle rotations, and each of them is guided by a face flow to maintain faithful facial details. In particular, we propose a Face Flow-guided Generative Adversarial Network (FFlowGAN) that is specifically trained for small-angle synthesis. The proposed network consists of two modules, a face flow module that aims to compute a dense correspondence between the input and target faces. It provides strong guidance to the second module, face synthesis module, for emphasizing salient facial texture. We apply FFlowGAN multiple times to progressively synthesize different views, and therefore facial features can be propagated to the target view from the very beginning. All these multiple executions are cascaded and trained end-to-end with a unified back-propagation, and thus we ensure each intermediate step contributes to the final result. Extensive experiments demonstrate the proposed divide-and-conquer strategy is effective, and our method outperforms the state-of-the-art on four benchmark datasets qualitatively and quantitatively.