Reviewerly/exHarmony huggingface dataset:
Dynamic control of photon lifetime for quantum random number generation

In a passive cavity geometry, there exists a trade-off between resonant enhancement and response time, which is inherently limited by the cavity photon lifetime. We demonstrate frequency-selective, dynamic control of the photon lifetime using a silicon-nitride coupled-ring resonator. The photon lifetime is tuned by controlling an avoided mode crossing using thermo-optic tuning of the cavity resonance with integrated heaters. Using this effect, we achieve fast turn-on/off of a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mi>χ<!-- χ --></mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo stretchy="false">(</mml:mo> <mml:mn>3</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> degenerate optical parametric oscillator (DOPO) and on-chip true random number generation. Our approach allows us to overcome the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Q</mml:mi> </mml:math> -limited generation rate of a single-ring-based DOPO and offers a path toward the development of a scalable integrated high-quality entropy source for modern cryptographic systems.
Block-wise reversible data hiding in encrypted domain using SVD

In this paper, a scheme combining encryption and reversible data hiding is proposed. The encryption is performed twice to provide the double layer security of transmitted media/ digital image using Mersenne Twister and Philox counter-based pseudo random number generator. The proposed work utilizes properties of singular value decomposition (SVD) for the embedding of secret data. The scheme attains desirable results in terms of embedding capacity and security against malicious attacks and thus, can be applicable in many cloud based, computing and privacy protection fields effectively.
SPACE: Finding Key-Speaker in Complex Multi-Person Scenes

Intelligent image processing based on deep learning is a recently emerging artificial Intelligence method which is critical in the next generation computing systems. Key-speaker detection using image processing aims to find the speaker who plays the most important role in complex multi-person scenes. However, existing speaker detection methods just judge whether there is somebody talking. To overcome this problem, this paper proposes a novel key-speaker detection approach named Speaker Pose-Attention deteCtion modEl (SPACE). This approach extracts the space information and analyze people's attention gathering to find the key-speaker. The method consists of two main parts. The first part is our proposed Importance Detection Model (IDM) using human pose estimation and an effective evaluation strategy to find candidates of important speaker. The second part consists of Pose-Attention Graph (PAG) and link analysis algorithm. The PAG represents people's attention graph. The link analysis algorithm analyzes the connectivity of this graph to find the key-speaker. Due to the lack of available key-speaker detection datasets, a self-collected dataset containing speech and meeting scenes has been built to estimate SPACE model. The experimental results show that the proposed method can obtain the best detection accuracy compared with other existing methods.
Eyesthetics

Gaze interaction has been growing fast as a compelling tool for control and immersion for gameplay. Here, we present a conceptual framework focusing on the aesthetic player experience and the potential interpretation (meaning) players could give to playing with gaze interaction capabilities. The framework is illustrated by a survey of state of the art research-based and commercial games. We complement existing frameworks by reflecting on gaze interaction in games as the attention relationship between the player (the subject) and the game (the object) with four dimensions: Identity; Mapping; Attention; and Direction. The framework serves as a design and inquiry toolbox to analyse and communicate gaze mechanics in games, reflect on the complexity of gaze interaction, and formulate new research questions. We visualise the resulting design space, highlighting future opportunities for gaze interaction design and HCI gaze research through the framework's lens. We deem, this novel approach advocates for the design of gaze-based interactions revealing the richness of gaze input in future meaningful game experiences.
The data paper as a sociolinguistic epistemic object: A content analysis on the rhetorical moves used in data paper abstracts

Abstract The data paper is an emerging academic genre that focuses on the description of research data objects. However, there is a lack of empirical knowledge about this rising genre in quantitative science studies, particularly from the perspective of its linguistic features. To fill this gap, this research aims to offer a first quantitative examination of which rhetorical moves—rhetorical units performing a coherent narrative function—are used in data paper abstracts, as well as how these moves are used. To this end, we developed a new classification scheme for rhetorical moves in data paper abstracts by expanding a well‐received system that focuses on English‐language research article abstracts. We used this expanded scheme to classify and analyze rhetorical moves used in two flagship data journals, Scientific Data and Data in Brief . We found that data papers exhibit a combination of introduction, method, results, and discussion‐ and data‐oriented moves and that the usage differences between the journals can be largely explained by journal policies concerning abstract and paper structure. This research offers a novel examination of how the data paper, a data‐oriented knowledge representation, is composed, which greatly contributes to a deeper understanding of research data and its publication in the scholarly communication system.
Hierarchical accumulation network with grid attention for image super-resolution

Deep convolutional neural networks (CNNs) have recently shown promising results in single image super-resolution (SISR) due to their powerful representation ability. However, existing CNN-based SR methods mainly focus on deeper architecture design to obtain high-level semantic information, neglecting the features of intermediate layers containing fine-grained texture information and thus limiting the capacity for producing precise high-resolution images. To tackle this issue, we propose a hierarchical accumulation network (HAN) with grid attention in this paper. Specifically, a hierarchical feature accumulation (HFA) structure is proposed to accumulate outputs of intermediate layers in a grouping manner for exploiting the features of different semantic levels. Moreover, we introduce a multi-scale grid attention module (MGAM) to refine features of the same level. The MGAM employs a pyramid sampling with self-attention mechanism to efficiently model the non-local dependencies between pixel features and produces refined representations. By this means, the universal features in connection with spatial similarity and semantic levels are produced for image SR. Experimental results on five benchmark datasets with different degradation models demonstrate the superiority of our HAN in terms of quantitative metrics and visual quality.
An Empirical Study on Refactoring-Inducing Pull Requests

Background: Pull-based development has shaped the practice of Modern Code Review (MCR), in which reviewers can contribute code improvements, such as refactorings, through comments and commits in Pull Requests (PRs). Past MCR studies uniformly treat all PRs, regardless of whether they induce refactoring or not. We define a PR as refactoring-inducing, when refactoring edits are performed after the initial commit(s), as either a result of discussion among reviewers or spontaneous actions carried out by the PR developer. Aims: This mixed study (quantitative and qualitative) explores code reviewing-related aspects intending to characterize refactoring-inducing PRs. Method: We hypothesize that refactoring-inducing PRs have distinct characteristics than non-refactoring-inducing ones and thus deserve special attention and treatment from researchers, practitioners, and tool builders. To investigate our hypothesis, we mined a sample of 1,845 Apache's merged PRs from GitHub, mined refactoring edits in these PRs, and ran a comparative study between refactoring-inducing and non-refactoring-inducing PRs. We also manually examined 2,096 review comments and 1,891 detected refactorings from 228 refactoring-inducing PRs. Results: We found 30.2% of refactoring-inducing PRs in our sample and that they significantly differ from non-refactoring-inducing ones in terms of number of commits, code churn, number of file changes, number of review comments, length of discussion, and time to merge. However, we found no statistical evidence that the number of reviewers is related to refactoring-inducement. Our qualitative analysis revealed that at least one refactoring edit was induced by review in 133 (58.3%) of the refactoring-inducing PRs examined. Conclusions: Our findings suggest directions for researchers, practitioners, and tool builders to improve practices around pull-based code review.
Pruning-Based YOLOv4 Algorithm for Underwater Gabage Detection

To tackle the problem of aquatic environment pollution, a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. This paper proposes a garbage detection method based on a modified YOLOv4, allowing high-speed and high-precision object detection. More specifically, the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection. With the purpose of further improvement on the detection speed, the channel pruning and layer pruning are implemented on the trained YOLOv4 model, while the fine-tuned mechanism assists the pruned model to restore accuracy. In virtue of the improved detection methods, the robot has the ability to collect garbage autonomously. The experimental results indicate that the pruned YOLOv4 detection method can still maintain the high performance even though the parameter amount is 7.062% of the original model.
Non-prehensile Planar Manipulation via Trajectory Optimization with Complementarity Constraints

Contact adaptation is an essential capability when manipulating objects. Two key contact modes of non-prehensile manipulation are sticking and sliding. This paper presents a Trajectory Optimization (TO) method formulated as a Mathematical Program with Complementarity Constraints (MPCC), which is able to switch between these two modes. We show that this formulation can be applicable to both planning and Model Predictive Control (MPC) for planar manipulation tasks. We numerically compare: (i) our planner against a mixed integer alternative, showing that the MPCC planner converges faster, scales better with respect to the time horizon (TH), and can handle environments with obstacles; (ii) our controller against a state-of-the-art mixed integer approach, showing that the MPCC controller achieves improved tracking and more consistent computation times. Additionally, we experimentally validate both our planner and controller with the KUKA LWR robot on a range of planar manipulation tasks. See our accompanying video here: https://youtu.be/EkU6YHMhjto.
Research on Question Answering System for COVID-19 Based on Knowledge Graph

In the context of the COVID-19 epidemic, it is necessary to establish a question answering (QA) system to help people to get information about the epidemic. This paper creats a framework about the question answering system based on knowledge bases (KBQA) for COVID-19. First, we design the schema of the COVID-19 knowledge graph (KG) and extract knowledge from texts. Second, a rule-based classifier is created to find the user’s intention when they input a question. Then, the matched template is used to transfer the question into Cypher query, and return answer which is retrieved from KG to users. We collected data from Baidu Encyclopedia and extracted triplet knowledge for constructing COVID-19 KG to prove the feasibility of our framework. The experiment on 86 questions shows that the KBQA for COVID-19 can achieve 81.6% accuracy and get a good experience through the feedback of testers.
Normalized Human Pose Features for Human Action Video Alignment

We present a novel approach for extracting human pose features from human action videos. The goal is to let the pose features capture only the poses of the action while being invariant to other factors, including video back-grounds, the video subjects’ anthropometric characteristics and viewpoints. Such human pose features facilitate the comparison of pose similarity and can be used for down-stream tasks, such as human action video alignment and pose retrieval. The key to our approach is to first normalize the poses in the video frames by mapping the poses onto a pre-defined 3D skeleton to not only disentangle subject physical features, such as bone lengths and ratios, but also to unify global orientations of the poses. Then the normalized poses are mapped to a pose embedding space of high-level features, learned via unsupervised metric learning. We evaluate the effectiveness of our normalized features both qualitatively by visualizations, and quantitatively by a video alignment task on the Human3.6M dataset and an action recognition task on the Penn Action dataset.
Obstacle Avoidance Path Planning for Worm-like Robot Using Bézier Curve

Worm-like robots have demonstrated great potential in navigating through environments requiring body shape deformation. Some examples include navigating within a network of pipes, crawling through rubble for search and rescue operations, and medical applications such as endoscopy and colonoscopy. In this work, we developed path planning optimization techniques and obstacle avoidance algorithms for the peristaltic method of locomotion of worm-like robots. Based on our previous path generation study using a modified rapidly exploring random tree (RRT), we have further introduced the Bézier curve to allow more path optimization flexibility. Using Bézier curves, the path planner can explore more areas and gain more flexibility to make the path smoother. We have calculated the obstacle avoidance limitations during turning tests for a six-segment robot with the developed path planning algorithm. Based on the results of our robot simulation, we determined a safe turning clearance distance with a six-body diameter between the robot and the obstacles. When the clearance is less than this value, additional methods such as backward locomotion may need to be applied for paths with high obstacle offset. Furthermore, for a worm-like robot, the paths of subsequent segments will be slightly different than the path of the head segment. Here, we show that as the number of segments increases, the differences between the head path and tail path increase, necessitating greater lateral clearance margins.
Content-Restricted Boltzmann Machines for Diet Recommendation

Nowadays, society is leading towards an unhealthy and inactive and lifestyle. Recent studies show the rapid growth of people suffering from diseases caused due to unhealthy lifestyles and diet. Considering this, recognizing the right type and amount of food to eat with a suitable exercise set is essential to obtain good health. The proposed work develops a framework to recommend the proper diet plans for thyroid patients, and medical experts validate results. The experiments' results illustrate that the proposed Content-Restricted Boltzmann Machines (Content-RBM) produces more relevant recommendations with content-based information.
Intelligent vehicle pedestrian tracking based on YOLOv3 and DASiamRPN

In the field of robot and autopilot, aiming at the difficulty that the traditional single target pedestrian tracking algorithm needs manual frame selection, this paper puts forward a pedestrian tracking algorithm which blends YOLOv3 and DASiamRPN to realize the designated pedestrian tracking. The algorithm automatically selects the tracking target through YOLOv3, and then uses the tracking target as the template to input into DASiamRPN for tracking. The real-time pedestrian tracking experiment based on the Kinect camera was carried out, and the tracking effects of SiamRPN, Deepsort, SiamFC and Mosse+KCF algorithms were compared. The experimental results show that the optimal running speed of DASiamRPN is 34fps while ensuring the tracking accuracy. Finally, the algorithm has been applied to Bulldog robot based on ROS, and the pedestrian tracking is realized successfully, which verifies the real-time and robustness of the algorithm.
Learning Without Forgetting: A New Framework for Network Cyber Security Threat Detection

Progressive learning addresses the problem of incrementally learning new tasks without compromising the prediction accuracy of previously learned tasks. In the context of artificial neural networks, several algorithms exist for achieving the progressive learning goal of learning without forgetting. However these algorithms have traditionally been tested on the well-known and widely available datasets from the domain of image understanding and computer vision. Very little has been done on exploring the suitability of progressive learning algorithms in the important area of network threat detection. On a more fundamental level, progressive learning algorithms are still faced with the challenge of predicting the ultimate ability of a given neural network architecture to add more tasks to its repertoire without undergoing catastrophic forgetting. The goal of this paper is to address such challenge in the context of cyber security threat detection. It does so by providing a unified conceptual and computational framework where progressive learning algorithms can be analyzed, compared, and contrasted in terms of their learning capacity and prediction accuracy for specific datasets from the cloud cyber security domain. In particular, this paper provides rigorous metrics for predicting the onset of catastrophic forgetting in the cyber security domain and contrasts them with their usage in the imaging domain. Our extensive numerical results show that progressive learning along with the proposed criteria for catastrophic forgetting provides a very structured framework for automating network threat detection as new threats emerge throughout network operation.
Semi-supervised learning for person re-identification based on style-transfer-generated data by CycleGANs

Person re-identification (re-ID) is an exceedingly significant branch in the field of computer vision, especially for video surveillance. It is still a challenge to obtain more labeled training data and use them reasonably for more precise matching, though the person re-ID performance has been improved significantly. In order to solve this challenge, this study proposes a semi-supervised learning algorithm for data augmentation, the style-transfer-generated data as an extra class (STGDEC), which is aided by the Cycle-Consistent Adversarial Networks (CycleGANs) in generating extra unlabeled training data. Specifically, the algorithm firstly trains the CycleGANs and Deep Convolutional Generative Adversarial Networks so as to generate large amounts of unlabeled data. Secondly, we propose an adaptive receptive field module to expand the size of receptive fields and select the appropriate receptive field features dynamically in order to learn more contextual information and discriminative feature representation and embed the module in the backbone network easily. Thirdly, we use the combination of label smoothing regularization for outliers and an extra class loss to regularize the generated data and encourage the network not to be too confident to the ground-truth. Finally, this paper proposes three training strategies for the combination of standard dataset and generated samples. Comprehensive experiments based on the STGDEC are conducted, and these results show that the proposed algorithm gains a significant improvement over the baseline, the Basel. + LSRO and state-of-the-art approaches of person re-ID in many cases.
A Systematic Mapping Study on Integration Proposals of the Personas Technique in Agile Methodologies

Agile development processes are increasing their consideration of usability by integrating various user-centered design techniques throughout development. One such technique is Personas, which proposes the creation of fictitious users with real preferences to drive application design. Since applying this technique conflicts with the time constraints of agile development, Personas has been adapted over the years. Our objective is to determine the adoption level and type of integration, as well as to propose improvements to the Personas technique for agile development. A systematic mapping study was performed, retrieving 28 articles grouped by agile methodology type. We found some common integration strategies regardless of the specific agile approach, along with some frequent problems, mainly related to Persona modelling and context representation. Based on these limitations, we propose an adaptation to the technique in order to reduce the creation time for a preliminary persona. The number of publications dealing with Personas and agile development is increasing, which reveals a growing interest in the application of this technique to develop usable agile software.
Referent graph embedding model for name entity recognition of Chinese car reviews

Name entity recognition (NER) is one of the most basic tasks for extracting information from Internet text. Chinese NER remains a major challenge due to the language complexity. Although researchers have recently used domain knowledge to embed word-level information into the deep learning models to deal with the Chinese NER, they have not considered the global interdependence between word-level information, i.e., the entities in the same document should be semantically related to each other. In addition, domain knowledge often cannot be used efficiently due to the presence of irregular expressions in the Internet text, such as abbreviations and aliases. In this paper, we propose a referent graph embedding model for the NER, specifically concentrating on the Chinese car review. First, domain knowledge is used to generate character-level candidate entities and model the global interdependence between these entities based on the referent graph model. Second, the latest BERT-based character vectors and the character-level candidate entities are jointly embedded into the deep learning model to perform the NER. Last, Chinese car reviews are collected and labeled for use as the experimental dataset. The experimental results demonstrate the efficiency and effectiveness of the proposed model for the Chinese car NER task compared with the other start-of-the-art models.