Reviewerly/exHarmony huggingface dataset:
Improving Engagement of Animated Visualization with Visual Foreshadowing

Animated visualization is becoming increasingly popular as a compelling way to illustrate changes in time series data. However, maintaining the viewer's focus throughout the entire animation is difficult because of its time-consuming nature. Viewers are likely to become bored and distracted during the ever-changing animated visualization. Informed by the role of foreshadowing that builds the expectation in film and literature, we introduce visual foreshadowing to improve the engagement of animated visualizations. In specific, we propose designs of visual foreshadowing that engage the audience while watching the animation. To demonstrate our approach, we built a proof-of-concept animated visualization authoring tool that incorporates visual foreshadowing techniques with various styles. Our user study indicates the effectiveness of our foreshadowing techniques on improving engagement for animated visualization.
Motion Sickness Reduction through Vibro-motor Reprocessing Therapy: A First Study

In order to reduce motion sickness (MS), a novel vibro-motor reprocessing therapy (VRT), based on an Eye Movement Desensitization and Reprocessing (EMDR) technique is investigated. To the best of the authors knowledge, this is the first time that reprocessing therapy has been evaluated for alleviating MS. Experimentally, MS was induced using visual stimulus of motion videos. Subjective MS was then recorded at baseline for both VRT and non-VRT stimulation conditions, and after each condition, evaluated using a Motion Sickness Assessment Questionnaire (MSAQ). MSAQ scores were compared for both conditions in eight test subjects, with a significant and clear reduction in motion sickness symptoms revealed when applying VRT stimulation. While the subject pool is small, this pilot study indicates that the proposed approach has potential for future exploration in terms of non-pharmacological treatment and management of MS.
Attribute-Based Searchable Encryption Scheme with Fuzzy Keywords

With the rapid development of cloud computing technology, users prefer cloud servers to store data. Scholars have proposed many searchable encryption schemes to ensure the confidentiality and search ability of the data. Based on the existing scheme, this paper proposes a new attribute-based searchable encryption scheme with fuzzy keywords. The primary purpose of this scheme is to achieve user access control and fuzzy keyword search. In this scheme, a tree-shaped access structure is used to implement the user’s access control, and only the user whose attributes meet the requirements can perform search operations. Fuzzy multi-keyword search can be achieved by using a collision-free hash function and a secure KNN encryption method. Through security analysis, this scheme satisfies the confidentiality of index and search trapdoors. There is also no relevance in search trapdoors, and this scheme achieves CPA security.
A simplistic approach to design a prototype of smart home for the activation of home appliances based on Electrooculography(EOG)

In this paper, a concept of EOG based human machine interface (HMI) is proposed with the purpose of designing a smart home surroundings and helping disable human beings. This interface permits us to observe the motion of the eyes measuring the potential among the cornea and the retina. Eye movement can be recorded by the Quadriplegia and spinal cord injury patients as well as normal human being to carry out regular household duties, for examples to activate a device as a way to get attention or to control domestic home equipment while they're alone. This study takes a look at and investigates the reliability of EOG alerts for activating domestic home equipment. To make this viable, a living room with four home appliances is considered and the EOG signals are recorded for five eye direction when the subjects are sitting steadily. Therefore, this study provides a useful assistive tool for a disable user to carry out vital works in addition to for ordinary people.
Swarm Robotics meets Blockchain to deploy Surveillance missions

Swarm Robotics can be used in many applications. But they suffer from certain limitations that make them undesirable in Surveillance applications. Among their limitations is the absence of scalability and security. Currently, these robots do not have framework that allows it to communicate with each other to execute missions autonomously and transparently. Blockchain is the new technology for distributed autonomous systems by providing set of computer nodes for data storage and distribution among the nodes in the system. Each participating node can protect and validate the data in the blockchain system. No modifications can be performed on the data records due to the fact that it is been watched by all the users. Based on the blockchain features, we focus on this research how blockchain applications can make swarm robots securely connected using Ethereum smart contracts. Starting with surveillance missions deployment task, we propose a decentralized application (DApp) for that. Then, blockchain is used by the swarms to establish the Ethereum private networks that allows them to communicate and carry out tasks. We set a test swarm robotics system and evaluate the blockchain for its performance, scalability, recoverability, and responsiveness. In conclusion, we found that blockchain enables a swarm to be globally securely connected, but there are still need for performance enhancement.
Myanmar Text-to-Speech Synthesis Using End-to-End Model

In this paper, we propose a Myanmar speech synthesis system based on an End-to-End neural network model, which integrates the Myanmar phone model into the Tacotron2 End-to-End model. Based on the Seq2seq model architecture, we use phone-level embedding to form a feature prediction network from phone sequences to Mel spectrum, and combine with a semi-supervised speech generation network to generate high-quality Myanmar synthesized speech. In addition, we introduced the BERT pre-training decoder module to assist the phone feature extraction, which reduces the system's dependence on the phone feature extraction network and improve the text feature richness. Compared with other Myanmar speech synthesis systems, this method effectively improves the naturalness and accuracy of synthesized speech under low resource conditions.
Nonparametric Bayesian functional two‐part random effects model for longitudinal semicontinuous data analysis

Longitudinal semicontinuous data, characterized by repeated measures of a large portion of zeros and continuous positive values, are frequently encountered in many applications including biomedical, epidemiological, and social science studies. Two-part random effects models (TPREM) have been used to investigate the association between such longitudinal semicontinuous data and covariates accounting for the within-subject correlation. The existing TPREM is, however, limited to incorporate a functional covariate, which is often available in a longitudinal study. Moreover, the existing TPREM typically assumes the normality of subject-specific random effects, which can be easily violated when there exists a subgroup structure. In this article, we propose a nonparametric Bayesian functional TPREM to assess the relationship between the longitudinal semicontinuous outcome and various types of covariates including a functional covariate. The proposed model also relaxes the normality assumption for the random effects through a Dirichlet process mixture of normals, which allows for identifying an underlying subgroup structure. The methodology is illustrated through an application to social insurance expenditure data collected by the Korean Welfare Panel Study and a simulation study.
Programmable Delay and Variable Bit Rate Enabled Video Streaming using C++

Live media streaming (audio and video) technology has boosted up in the recent years. With this, the real time data flow over the network has increased and many algorithms are developed to prevent the congestion of the network. Most of the them are developed for TCP network and only few of them for UDP network. Moreover, these algorithms use programming languages such as Python, Java, etc. which has a vast variety of libraries. This paper highlights an algorithm implemented in C++, to control the bit rate of real-time video over UDP network. The streaming is accomplished with simultaneous execution of image streaming and audio steaming, hence realizing video streaming as a whole. The packets are sent into the network in a manner to attain the required bit rate, which is achieved by varying the size of the buffer at sending device. Total travel time of a single frame is estimated using timestamps and average travel time is calculated. Total travel time along with size of buffer together decides the bit rate. Additionally, required network delay is introduced with the help of timer. It is a simple and stand-alone media streaming process, with highly-reduced complexity. The main aim of the paper is to achieve programmable delay and variable bit rate enabled video streaming for space applications.
Usability Evaluation and User Interface Design of University Staffing Information System

Human Resources X Information System or “Sistem Informasi Kepegawaian X (SIPEG-X)” is a staffing information system that is used to monitor staff performance at a large public university in Indonesia. Since this information system was first developed, there has been no significant update on the system, especially if it is viewed in terms of user-interface (UI) or user experience (UX). This then led to the difficulty of assessing performance against the university staff because of a lack of satisfaction and comfort in using the information system. Therefore, usability evaluation and design refinement of these university staffing information systems was carried out using user-centered design methodology so that a new system in the form of a mobile application was developed to provide solutions to the interface of this staffing information system. Evaluation is conducted through surveys, usability testing, and contextual interviews to assess the usability aspects of the current implemented website application. The evaluation results show that the usability of these information systems needs to be improved and based on the principle of Shneiderman's Eight Golden Rules of Interaction Design, a prototype is proposed to improve the interface as well as the user experience.
TAMPC: A Controller for Escaping Traps in Novel Environments

We propose an approach to online model adaptation and control in the challenging case of hybrid and discontinuous dynamics where actions may lead to difficult-to-escape “trap” states, under a given controller. We first learn dynamics for a system without traps from a randomly collected training set (since we do not know what traps will be encountered online). These “nominal” dynamics allow us to perform tasks in scenarios where the dynamics matches the training data, but when unexpected traps arise in execution, we must find a way to adapt our dynamics and control strategy and continue attempting the task. Our approach, Trap-Aware Model Predictive Control (TAMPC), is a two-level hierarchical control algorithm that reasons about traps and non-nominal dynamics to decide between goal-seeking and recovery policies. An important requirement of our method is the ability to recognize nominal dynamics even when we encounter data that is out-of-distribution w.r.t the training data. We achieve this by learning a representation for dynamics that exploits invariance in the nominal environment, thus allowing better generalization. We evaluate our method on simulated planar pushing and peg-in-hole as well as real robot peg-in-hole problems against adaptive control, reinforcement learning, trap-handling baselines, where traps arise due to unexpected obstacles that we only observe through contact. Our results show that our method outperforms the baselines on difficult tasks, and is comparable to prior trap-handling methods on easier tasks.
L2-Constrained RemNet for Camera Model Identification and Image Manipulation Detection

Source camera model identification (CMI) and image manipulation detection are of paramount importance in image forensics. In this paper, we propose an L2-constrained Remnant Convolutional Neural Network (L2-constrained RemNet) for performing these two crucial tasks. The proposed network architecture consists of a dynamic preprocessor block and a classification block. An L2 loss is applied to the output of the preprocessor block, and categorical crossentropy loss is calculated based on the output of the classification block. The whole network is trained in an end-to-end manner by minimizing the total loss, which is a combination of the L2 loss and the categorical crossentropy loss. Aided by the L2 loss, the data-adaptive preprocessor learns to suppress the unnecessary image contents and assists the classification block in extracting robust image forensics features. We train and test the network on the Dresden database and achieve an overall accuracy of 98.15%, where all the test images are from devices and scenes not used during training to replicate practical applications. The network also outperforms other state-of-the-art CNNs even when the images are manipulated. Furthermore, we attain an overall accuracy of 99.68% in image manipulation detection, which implies that it can be used as a general-purpose network for image forensic tasks.
TLD tracking algorithm for metro construction site security

In view of security issues, this paper studies how to track the target at the metro construction site accurately and effectively. It is proposed to replace the tracking module in the TLD(Tracking-Leaning-Detection) algorithm with the GOTURN algorithm, to enhance the tracking robustness of the original algorithm in response to the target's illumination changes, rotation, occlusion, etc. To solve the problem of inaccurate sample division in the TLD algorithm learning module, a sample constraint mechanism is introduced to divide positive and negative samples more objectively and accurately. The sample deletion mechanism of weighted model matching is introduced to delete samples that are weak in characterizing the current target, which improves the tracking accuracy and efficiency of the algorithm. The experimental results show that the improved algorithm can well reduce the interference that is caused by the target pedestrian's illumination change, turning, occlusion, etc. It can accurately track the target pedestrian for a long time, and has good robustness and real-time performance.
Automatic Threat Detection in Baggage Security Imagery using Deep Learning Models

Automating object detection for surveillance purpose and threat detection is beneficial as it may compensate for the human error and will save time, which is of significant economic value. For the end-to-end classification process and feature extraction, the CNN approach requires large amounts of data. To overcome this limited availability of data, we have presented a transfer learning approach with various object detection models for single and multiple detections on two types of the dataset: Single-channelled (GDXray dataset) and Multichanneled(SIXray dataset). We have presented comparisons between the various models(Faster R-CNN with ResNet50, SSD with VGG16, YOLOv3 with ResNet50, and RetinaNet with ResNet50). The best results were achieved on Faster-RCNN(ResNet50) with 0.966 mAP for the four-class object detection problem(GDXray Dataset) and 0.845 mAP for the two-class object detection problem(SIXray Dataset).
EFSSD: An Enhanced Fusion SSD with Feature Fusion and Visual Object Association Method

The single-shot multibox detector (SSD) is a landmark work in object detection field that achieves excellent performance in target detection. In this paper, an accurate single-stage object detector, namely enhanced fusion SSD (EFSSD) is proposed for the object detection problems. The residual network is introduced to extract deeper feature information. Moreover, we use the feature fusion method to fuse feature maps of different layers. To further detect small objects, a visual object association method is developed, which makes full use of relationships between targets instead of the feature of the target itself. To evaluate the proposed EFSSD, it was trained on PASCAL VOC2007 and PASCAL VOC2012 training datasets and tested on the PASCAL VOC2007 test dataset. For 300 × 300 input, EFSSD achieves 80.1 % and 53 frames per second (FPS) and EFSSD achieves 83.7% and 17.1 FPS for 512 × 512 input. The above experimental performances show that EFSSD achieves higher accuracy comparing with SSD and other advanced detectors.
Anchor-free pedestrain detection model with semantic context of traffic scenario

Pedestrian detection is an important and challenging issue for autonomous driving. Most of the pedestrian detecting methods utilize the general object detection framework, which follows the two-stage or one-stage pipeline to detect the pedestrian. Nevertheless, these methods usually define the fixed size anchors according to the statistics of the dataset. In this paper, we propose an anchor-free pedestrian detection model. Our model considers pedestrians' semantic context in the traffic scene, which contributes to improving the robustness for small-scale pedestrian detection. Our paper's contributions are: (1) We propose an anchor-free detection network that integrates the segmentation feature. (2) We add an attention module to the network to improve the robustness of detection and make the training process more manageable. (3) We conduct experiments on CityPersons dataset and compared the detection with some state-of-the-art algorithms. Experimental results demonstrate that our algorithm achieves a significant improvement.
Secure Cross-Domain Data Sharing Technology Based on Blockchain

Considering the difficulties of cross-domain data sharing and secure access tracking between multiple organizations, this paper proposes a cross-domain data sharing model based on blockchain and the features of cloud computing storage; puts forward a data sharing protocol via digital digest matching algorithm. By matching information within digital digest, this protocol can realize the restricted sharing of data while protecting data privacy. This paper also proposes a multi-level, secure storage architecture for data according to authorization, introduces a method for adding heterogeneous data to the chain, develops a data access mechanism and a privacy protection mechanism, and provides a solution for data storage and trusted sharing in untrusted environments.
Novel Data Protection for Bounded-Error IoT with Blockchain Smart Contract

Due to the rapid development of IoT systems and applications, a large amount of sensors' data has been generated. Processing and analyzing these data provide users with better applications in diversity. However, current wireless sensing nodes still have a significant issue of power consumption. As the application requirements usually have bounded-error tolerance in IoT systems, our previous research proposed bounded-error data compression to extend the lifetime of IoT system. Meanwhile, the IoT data storing and sharing often rely on the third party service providers. It created another issues of data privacy. To solve these problems, this paper proposes a bounded-error IoT (BIoT) data privacy protection scheme via blockchain smart contract to provide secure data storing and sharing. BIoT not only extends the lifetime of IoT systems, but also provides different levels of service quality in privacy protection according to the agreement of the rights and payment in smart contract. Experimental results demonstrate that BIoT can prolong the IoT system lifetime with data privacy protection.
Study Concept on the Development of an Urban Cyber Physical System for Enhancing the Capability to Respond to Large-Scale Earthquakes

Various technologies for improving earthquake-disaster-mitigation capability in urban communities are being developed and proposed. However, these technologies are sometimes difficult to use in actual applications due to the lack of incentives for owners of buildings or infrastructures, because the owners cannot calculate their cost-effectiveness and thus consider the installation payment for these technologies as a cost to be reduced. To address this problem, we propose the construction of an “Urban cyber physical system (CPS).” This urban CPS evaluates the earthquake-resistant capability of buildings and/or social infrastructures to help owners easily understand the cost-effectiveness of adopting these technologies. The CPS also calculates the effects of newly-developed technologies, thereby allowing owners to accept new technologies based on their effectiveness. The study concept of the urban CPS is as follows: (1) Construction of an “Information platform” by using data aggregation and analysis of existing vibration data of structures, datasets of building (or construction) information modeling and various other available databases; (2) Development of a “Simulation platform” that has a prediction function to calculate the behaviors of urban communities during earthquakes by using data in the Information platform and an identification function to identify structural systems from input earthquake motions and responses of structures; and (3) Establishment of an “Eco-system” to operate the urban CPS in urban community design, based on the perspective of earthquake resilience.
A generative adversarial network based image augmentation method for ship segmentation in SAR images

Synthetic aperture radar (SAR) has good performance and is widely used. Ship monitoring plays an important role in the military field, and ship segmentation plays an important role in related research. SAR image segmentation with neural networks requires large amounts of data. In the case of a small data set, traditional data augmentation methods such as scaling and rotation methods have limited promotion on image segmentation. We propose a new method for SAR image augmentation: based on CycleGAN, combined with Slide Window Filtering, convert aerial images to SAR style images, and use it for SAR image segmentation. Experiments show that compared with the traditional data augmentation methods, this method can improve the segmentation result better.
Autocorrelation Sequence Prediction Model Based On Reference Function Transformation: Taking Epidemic Prediction As An Example

Autocorrelation sequence prediction is one of the hotspots in machine learning and statistics. At present, the problem of epidemic prediction is concerned by the whole world in this field. In this paper, a prediction algorithm of autocorrelation sequence based on transformation is proposed. It construct a reference function based on the time series of regions that have experienced the whole process of epidemic situation. The reference function coordinates are transformed with the incomplete observation data of other regions as the supervision. Under the condition that the cost of transformation is kept as small as possible, the transformed function can effectively predict the series values that are not observed in other regions. This algorithm needs a little data and has good training stability. On this basis, we study how to use the information provided by other exogenous variables on the basis of autocorrelation prediction to make the model achieve better results. We use the covid-19 epidemic data set provided by Baidu to test our model, and the results show that it has good fitting metrics. It also has better effect in comparison with LSTM epidemic prediction model baseline.