Reviewerly/exHarmony huggingface dataset:
Self-Supervised Monocular Depth Estimation With Geometric Prior and Pixel-Level Sensitivity

Self-supervised monocular depth estimation has gained popularity due to its convenience of training network without dense ground truth depth annotation. Specifically, the multi-frame monocular depth estimation achieves promising results in virtue of temporal information. However, existing multi-frame solutions ignore the different impacts of pixels of input frame on depth estimation and the geometric information is still insufficiently explored. In this paper, a self-supervised monocular depth estimation framework with geometric prior and pixel-level sensitivity is proposed. Geometric constraint is involved through a geometric pose estimator with prior depth predictor and optical flow predictor. Further, an alternative learning strategy is designed to improve the learning of prior depth predictor by decoupling it with the ego-motion from the geometric pose estimator. On this basis, prior feature consistency regularization is introduced into the depth encoder. By taking the dense prior cost volume based on optical flow map and ego-motion as the supervising signal for feature consistency learning, the cost volume is obtained with more reasonable feature matching. To deal with the pixel-level difference of sensitivity in input frame, a sensitivity-adaptive depth decoder is built by flexibly adding a shorter path from cost volume to the final depth prediction. In this way, the back propagation of gradient to cost volume is adaptively adjusted, and an accurate depth map is decoded. The effectiveness of the proposed method is verified on public datasets.
A Quantitative Analysis of Redirected Walking in Virtual Reality Using Saccadic Eye Movements

In this work we investigated redirected walking in virtual reality using saccadic eye movements to determine when to rotate the participant in their virtual environment. We tested participants by placing them into a virtual environment where they worked on completing a series of simple tasks while being rotated according to their saccadic eye movements at a randomized degree of rotation. After testing, our results displayed a marked difference between the participant’s movement in the virtual environment versus the real-world environment. This difference shows the potential of using saccadic eye movement to keep a user immersed even within a smaller than optimal play area.
Long- and Short-term Preference Learning with Enhanced Spatial Transformer for Next POI Recommendation

With the widespread adoption of mobile devices and Internet technologies, location-based social networks (LBSNs) provide a multitude of services to people. Next point of interest (POI) recommendation has become an important problem. The purpose of this task is to discover the location history activities from the user's preferences and recommend the next POI. The researchers utilized RNN model or attention mechanism to integrate long- and short-term interests and achieve success. However, existing works manually designed feature interaction to fuse different preference, or shallowly mined spatio-temporal information. To address the limitations, we propose an transformer-based model named Long- and Short-term Preference Learning with Enhanced Spatial Transformer(LSEST). Our model adopts a unified model to simultaneously model long-term and short-term preferences, so that the two user preferences can be interacted deeply to represent a comprehensive user preference. In addition, our model utilizes two transformer encoders to capture the temporal and spatial dependencies, respectively, and enhances the spatio-temporal consistency greatly. Extensive experiment on two real-world check-in datasets shows the superiority of our model compared to the state-of-the-art methods.
Script-Independent Text Segmentation from Document Images

Document image analysis finds broad application in the digital world for the purpose of information retrieval. This includes optical character recognition (OCR), indexing of digital libraries, web image processing, etc. One of the important steps in this field is text segmentation. This segmentation becomes complicated for the documents containing text of uneven spacing and characters of varying font sizes. In this paper, script-independent text-line segmentation and word segmentation algorithms are presented. Fast marching method is used for text-line segmentation, whereas wavelet transform with connected components (CCs) labeling is used for word segmentation. Fast marching method is used as a region growing process that detects potential text-lines. For word segmentation, energy map is calculated using wavelet transform to create text-blocks. Both the proposed algorithms are evaluated on different databases containing documents of different scripts, where highest text-line and word segmentation accuracies of 98.9% and 99.1%, respectively, are obtained.
CR Image Optimization and Radiation Dose Limitation With Collimation Adjusting on Cervical Radiography

Background: Radiological examination in cervical services, Radiographers often opening the collimation wider of the size of the imaging plate for fear of being cut. This is done because it is supported by the image cropping facility on the CR. So there is a habit of opening the collimation width as wide as the imaging plate, which is often done with the assumption that it is better to widen the collimation than cut off the object. Obviously this will increase the radiation dose in patients with basic limitations. According to ICRP, patient safety must refer to the principle of radiation protection ALARA namely in the shortest possible time to get quality radiographs and patients receive the most minimum radiation exposure as possible.Methods: This research is a descriptive analytic research conducted with a cross sectional approach. The study was conducted by varying the radiation field area to be used for cervical examination. There are 10 variations of collimation area with 3 (three) exposures in each collimation. Then measured contrast, noise and radiation dose. Radiation dose is calculated by means of a babyline device.Results: The results showed that the collimation setting did not affect contrast radiography in CR because collimation was not the main factor that could affect contrast radiography on CR. The collimation setting on cervical radiography has an effect on the radiation dose, so it is necessary to adjust the collimation to get the minimum dose possible. The most optimal collimation area on cervical radiographic examination was obtained at a size of 12 x 24 cm with the smallest noise reception and dose compared to others and had higher contrast. Average contrast value achieved was 0.45 with a dose of 39.23 µGy.Conclusion: The collimation setting on cervical radiography has an effect on the radiation dose, so it is very necessary to adjust the collimation to get the minimum dose possible. The most optimal collimation area on cervical radiographic examination is obtained at a size of 12 x 24 cm with the smallest noise reception and dose compared to others and has a higher contrast.
Effective, Efficient and Robust Neural Architecture Search

Designing neural network architecture for embedded devices is practical but challenging because the models are expected to be not only accurate but also enough lightweight and robust. However, it is challenging to balance those trade-offs manually because of the large search space. To solve this problem, we propose an Effective, Efficient, and Robust Neural Architecture Search (E2RNAS) method to automatically search a neural network architecture that balances the performance, robustness, and resource consumption. Unlike previous studies, the objective function of the proposed E2RNAS method is formulated as a multi-objective bi-level optimization problem with the upper-level subproblem as a multi-objective optimization problem that considers the performance, robustness, and resource consumption. To solve the proposed objective function, we integrate the multiple-gradient descent algorithm, a widely studied gradient-based multi-objective optimization algorithm, with the bi-level optimization. Experiments on benchmark datasets show that the proposed E2RNAS method can find robust architecture with low resource consumption and comparable classification accuracy.
Comparative Study Analysis on News Articles Categorization using LSA and NMF Approaches

Due to exponentially growing news articles every day, most of their important data goes unnoticed. It is important to come up with the ability to automatically analyse these articles and segregate them based on the context and related to their particular domain. This paper applies topic modelling which is one of the most growing unsupervised machine learning fields on a million headlines articles in order to produce topics to describe the context of the news article. There are various generative models but we specifically focusing on the non-negative matrix factorization (NMF) and Latent Semantic Analysis (LSA) for implementing and evaluating news dataset. Furthermore, the findings reveal that both NMF and LSA are useful topic modelling tools and classification frameworks, but based on the experimental results the LSA model performed well to identify the hidden data with better mean coherence values and also consumes lesser execution time than NMF.
A Data-Centric Methodology and Task Typology for Time-Stamped Event Sequences

Task abstractions and taxonomic structures for tasks are useful for designers of interactive data analysis approaches, serving as design targets and evaluation criteria alike. For individual data types, dataset-specific taxonomic structures capture unique data characteristics, while being generalizable across application domains. The creation of dataset-centric but domain-agnostic taxonomic structures is difficult, especially if best practices for a focused data type are still missing, observing experts is not feasible, and means for reflection and generalization are scarce. We discovered this need for methodological support when working with time-stamped event sequences, a datatype that has not yet been fully systematically studied in visualization research. To address this shortcoming, we present a methodology that enables researchers to abstract tasks and build dataset-centric taxonomic structures in five phases (data collection, coding, task categorization, task synthesis, and action-target-(criterion) crosscut). We validate the methodology by applying it to time-stamped event sequences and present a task typology that uses triples as a novel language of description for tasks: (1) action, (2) data target, and (3) data criterion. We further evaluate the descriptive power of the typology with a real-world case on cybersecurity.
BLOC: Balancing Load with Overload Control In the Microservices Architecture

The microservices architecture has become ubiquitous in the cloud environment. It simplifies application development by breaking monolithic applications into manageable micro services that can be developed and deployed independently of the whole. However, the move from a monolithic or simple multi-tier architecture to a distributed microservice "service mesh" leads to new challenges due to the more complex application topology.A particular problem when automatically managing the performance of microservices is that since each service component scales up and down independently, it can easily create load imbalance problems on shared backend services accessed by multiple components. Traditional load balancing algorithms were designed for centralized load balancers sitting between a group of clients and a server farm. These algorithms, however, do not port over well to a distributed microservice architecture where load balancers are deployed client-side. In this paper we propose a self managing load balancing system, BLOC, which provides consistent response times to users without using a centralized metadata store or explicit messaging between nodes.We show that different service layers scaling independently can create unacceptably wide response time distributions and long tails, hurting client experience. This is because popular microservice load balancing algorithms, like Least Connection, only use a single component’s view of the backend load to guide decisions. This limited perspective leads to an unevenly balanced system and the potential for incast problems where a large number of frontend components can easily overload a shared backend. BLOC uses overload control approaches like rate limiting, active queue management and backpressure to provide feedback to the load balancers. The load balancers react to this feedback with techniques like backoff and retries. We show that this performs significantly better in solving the incast problem in microservice architectures.Evaluating this framework, we found that BLOC improves the response time distribution range, between the 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> and 90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentiles, by 2 to 4 times and the tail, 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile, latency by two times.
A Robust and Fast Occlusion-based Frontier Method for Autonomous Navigation in Unknown Cluttered Environments

Navigation through unknown, cluttered environments is a fundamental and challenging task for autonomous vehicles as they must deal with a myriad of obstacle configurations typically unknown a priori. Challenges arise because obstacles of unknown shapes and dimensions can create occlusions limiting sensor field of view and leading to uncertainty in motion planning. In this paper we propose to leverage such occlusions to quickly explore and cover unknown cluttered environments. Specifically, this work presents a novel occlusion-aware frontier-based approach that estimates gaps in point cloud data and shadows in the field of view to generate waypoints to navigate. Our scheme also proposes a breadcrumbing technique to save states of interest during exploration that can be exploited in future missions. For the latter aspect we focus primarily on the generation of the minimum number of breadcrumbs that will increase coverage and visibility of an explored environment. Extensive simulations and experiment results on an unmanned ground vehicle (UGV) are demonstrated to validate the proposed technique, showing improvements over traditional state of the art frontier-based exploration methods.
Space Time Attention Transformer for Non-Event Detection in UAV Videos

Most of the classification models are built for closed set environments, where the model is trained to assign samples to a set of predefined categories. This assumption cannot be hold for models built for UAV aerial videos, where novel videos are likely to be encountered in the test phase. Dealing with unknown videos is fundamental for a reliable classification model. Therefore, in this work, we propose a model for recognizing events acquired using UAV platforms with the non-event detection property. Our model utilized the power of the attention-based model to extract discriminative spatiotemporal features from the video clips. Then, the model is trained with IsoMax loss to detect out-of-distribution videos. The proposed model is evaluated on UAV Events Recognition Dataset (ERA), and the results show that our model is able to detect non-event videos with 70.87% precision. Moreover, non-event detection has increased the accuracy of recognizing known events to 68.44%, which outperforms the accuracy of other state-of-the-art models.
An Improved Sparrow Approach

The basic sparrow search algorithm reduces the population diversity at the later stage of the iteration and is easy to fall into local extremes. An improved sparrow algorithm (ISSA) is proposed basen on fusion of Cauchy mutation and reverse learning. Firstly, a Sin chaotic initialization population with unlimited number of mapping folds is used to lay the foundation for global optimization. Secondly, the previous generation global optimal solution in the discoverer position update method is introduced to improve the sufficiency of the global search, and at the same time, adaptive weights are added, local mining and global exploration capabilities are coordinated to speed up the convergence speed; then, the Cauchy mutation operator and the reverse learning strategy are combined to perform perturbation mutation at the optimal solution position, new solutions are generated, the algorithm's ability is enhanced to jump out of local space. Finally, the paper compared with three basic algorithms and two improved sparrow algorithms.
VR4HcRE: Virtual Reality Platform for Human-centric Requirements Elicitation

Virtual reality (VR) is an important interactive technology for collaboration and communication. However, VR has not been widely used for eliciting human-centric requirements in software engineering contexts, which is an important gap in both the application of VR technology and the research of software engineering. To fill this gap, in this paper, we develop the VR4HcRE platform as a remote multiplayer collaboration and communication system to explore its benefits in human-centric requirement engineering. We consult with software developers and requirements engineers, as stakeholders of the system, to define the features of the system, including remote multiplayer collaboration, audio transcription, simulation of human and environmental factors, and UI design based on mobile phone prototype. We conduct a pilot experiment with 21 participants. Preliminary results indicate that using VR to capture user or special user needs has the potential value, including improving collaboration interaction and requirements expression, and bringing a different sense of participation compared with the physical world. As an ongoing work, we hope this paper generates a series of new research ideas in virtual reality and requirements elicitation.
Blockchain Based RFID Prototype For Medicine Traceability along the Healthcare Supply Chain

Healthcare supply chain refers to the process of procurement and distribution of medicines and other healthcare products as they move from the manufacturing plant to the patient. This is a very complex and distributed process that comes with a set of challenges like scalability, synchronization and trust between the involved parties. The increase in adoption of Radio-frequency Identification (RFID) based systems in the healthcare supply chain shows that RFID technology is a very promising technique for traceability, identification and communication. However, these RFID systems still face issues of privacy and security which need to be addressed to get their full benefit. In this research work, we have integrated RFID systems belonging to the Healthcare supply chain with secure decentralized storage such as Blockchain. Blockchains provides a secure and transparent decentralized mechanism that is used to keep track of all transactions. It offers a new kind of implementation focused on trust, transparency, and accountability. Information about the RFID enabled medicines and other Healthcare products can be added to our proposed framework via a blockchain-based decentralized application (Dapp). The proposed system is based on an Ethereum node, and this information can be stored in a Blockchain using multi-stage interfaces provided by the Smart Contract. Our proposed system has been deployed both on an offline and online environment, and it offers many advantages like monitoring and tracing of medicine through the Blockchain network, determining if the medicine is genuine or counterfeit, and reducing the number of counterfeit and unapproved medicine entering the Healthcare system. Based on simulation results, the proposed framework successfully provides the needed security and privacy required for the healthcare supply chain.
Robust Multi-Channel Far-Field Speaker Verification Under Different In-Domain Data Availability Scenarios

The popularity and application of smart home devices have made far-field speaker verification an urgent need. However, speaker verification performance is unsatisfactory under far-field environments despite its significant improvements enabled by deep neural networks (DNN). In this paper, we summarize our previous work and propose multiple training strategies and models for multi-channel far-field speaker verification with different in-domain data availability scenarios. The experiments are conducted on the FFSVC20 dataset, and we proposed the cross-device and cross-domain trials. We focus on single-channel and multi-channel speaker verification training based on the dataset. For single-channel speaker verification, considering the size of training data and availability of labels, we introduce three training scenarios and given our proposed training methods, including 1) given zero out-of-domain data and few in-domain labeled data; 2) given large-scale out-of-domain labeled data and few in-domain labeled data; 3) given large-scale out-of-domain labeled data and few in-domain unlabeled data. To this end, we propose a meta-learning approach, refined transfer learning methods, and semi-supervised learning for three scenarios, respectively. For multi-channel speaker verification, we first introduce two types of 3 dimension convolution (3D Conv) residual network (ResNet) models proposed in our previous works, including fully 3D ResNet and incorporating 3D Conv with 2D Conv ResNet (3D2D-ResNet). In this paper, we propose channel-wise 3D squeeze-and-excitation ResNet (C3DSE-ResNet) and spatial-wise 3D SE ResNet (S3DSE-ResNet) to further explore the channel dependencies and improve the 3D ConvNet performance. The results show that the proposed strategies and models can significantly boost performance under the far-field scenario.
LBLCO: A Lightweight Blockchain with Low Communication Overhead for Internet of Things

With the increasing demand for Internet of things (IOT) applications, more devices need to be added to IOT. IOT system using centralized server mode face great security challenges. Blockchain, based on distributed ledger technology, could be a good solution. In order to combine blockchain technology and IOT applications appropriately, this paper proposes a lightweight blockchain with low communication overhead (LBLCO). A new consensus algorithm named proof of repute score based on hidden block (HBPORS) is proposed in LBLCO. The nodes with higher repute score will be more likely to become winner in HBPORS. The repute score mechanism dynamically update the repute score of the nodes according to the contribution and evil behavior of the node in the blockchain system. When the honest node with high repute score obtains the accounting right, it can trigger the hidden block mechanism. Instead of broadcasting the complete information of the packaged block to all nodes, winners can broadcast the low overhead information to other nodes. Through experimental tests, LBLCO can meet the requirements of large number of nodes and fast response speed in the IOT.
Cultural Discourse on Keyboards: The Selection of Alphabets

By investigating into the alphabets of the Apple keyboard in 16 languages, we have arrived at two findings. First, language marks, special letters with marks and alphabets are chosen and listed on keyboards in all languages except simplified Chinese; Second, influenced by the English-oriented design form, English language places every letter on a single key within letter areas, other languages prefer scattering, squeezing, or cropping their letters within the existing letter, number, or punctuation areas, which we call as the phenomenon of language gravity. We therefore propose that Chinese pinyin—23 initials, 24 finals, and 5 tones—should be fully displayed on a keyboard, that self-contained keyboards should be developed to meet the increasing demands of non-English keyboard market.