Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Touch/Tangible Spatial 3D Data Selection

We discuss spatial selection techniques for three‐dimensional datasets. Such 3D spatial selection is fundamental to exploratory data analysis. While 2D selection is efficient for datasets with explicit shapes and structures, it is less efficient for data without such properties. We first propose a new taxonomy of 3D selection techniques, focusing on the amount of control the user has to define the selection volume. We then describe the 3D spatial selection technique Tangible Brush, which gives manual control over the final selection volume. It combines 2D touch with 6‐DOF 3D tangible input to allow users to perform 3D selections in volumetric data. We use touch input to draw a 2D lasso, extruding it to a 3D selection volume based on the motion of a tangible, spatially‐aware tablet. We describe our approach and present its quantitative and qualitative comparison to state‐of‐the‐art structure‐dependent selection. Our results show that, in addition to being dataset‐independent, Tangible Brush is more accurate than existing dataset‐dependent techniques, thus providing a trade‐off between precision and effort.
Dual Channel Audio Watermarking Algorithm Based on Embedded Strength Optimization

This work proposes a new dual-channel audio watermarking algorithm to improve the imperceptibility of watermark while maintaining the robustness. First, we use the correlation method to detect the pitch of the origin audio, by which the audio will be segmented adaptively. Then, we implement the discrete wavelet transform (DWT) on the audio signal to get the approximate decomposition coefficients. On this basis, the coefficients are divided into odd-order and even-order channels by the coefficient serial number. According to the high similarity between the two channels, the coefficients of the even-order channel are embedded into the odd-order channel in the form of an echo. To improve the imperceptibility of the watermark, we apply the cross-correlation method to optimize the embedding strength to minimize the signal-to-noise ratio (SNR). By this way, the robustness of the watermark will be improved without significantly affecting the original audio quality. In the watermark extraction stage, the coefficients cross-correlation between the embedding channel and the embedded channel is used to detect the signal peaks. According to the peak positions, the watermark can be extracted, so the blind detection is realized. We test the proposed method by simulation experiments, and the experimental data show that the robustness of the proposed method is higher than the method proposed in [1].
Optimizing Stepwise Animation in Dynamic Set Diagrams

A set diagram represents the membership relation among data elements. It is often visualized as secondary information on top of primary information, such as the spatial positions of elements on maps and charts. Visualizing the temporal evolution of such set diagrams as well as their primary features is quite important; however, conventional approaches have only focused on the temporal behavior of the primary features and do not provide an effective means to highlight notable transitions within the set relationships. This paper presents an approach for generating a stepwise animation between set diagrams by decomposing the entire transition into atomic changes associated with individual data elements. The key idea behind our approach is to optimize the ordering of the atomic changes such that the synthesized animation minimizes unwanted set occlusions by considering their depth ordering and reduces the gaze shift between two consecutive stepwise changes. Experimental results and a user study demonstrate that the proposed approach effectively facilitates the visual identification of the detailed transitions inherent in dynamic set diagrams.
Online vs. Face-to-Face: A Comparison of Student Outcomes with Random Assignment

The following study contrasts the efficacy of online delivery relative to face-toface delivery using an enrolment protocol that largely eliminates self-selection bias. Only a few previous studies even attempt to control for sample selection. The study utilizes random assignment of the registrants of a Principles of Macroeconomics class into two alternative venues: online and face-to-face. The same professor taught both sections with the same course objectives and exams. Both the change in student scores from the pre-test to the post-test and the student’s exam average are modelled as a function of the course environment, the student’s SAT math score (or ACT equivalent), the student’s GPA prior to taking the course, the student’s gender and the student’s overall credit hours prior to taking the course. The preand post-test had both standardized and instructor-specific questions. Students in the face-to-face section have statistically significantly higher exam scores and statistically significantly greater improvement on the post-test instructor questions. There is no statistical difference in the improvement on the post-test overall nor in the improvement in the post-test standardized questions. These mixed results suggest that both course objectives and the mechanism used to assess the relative effectiveness of the two modes of education may play an important part in determining the relative effectiveness of alternative delivery methods.
Maritime Shift Workers Sleepiness Detection System With Multi-Modality Cues

Sleepiness has been recognized as a causal factor in many round-the-clock industries. While individuals can subjectively express their momentary sleepiness level, sleepiness-related contextual factors (CF) can influence their perception of sleepiness and cognitive performance. In this paper, the self-reported sleepiness value (vSRS) was improved by transforming it into a kernel density estimate and the assignment of the class’s score is done using a likelihood ratio test (IvSRS). We integrated multiple CF and IvSRS to model sleepiness using a Bayesian network (BN). The BN produced a single probability estimate calculated based on the prior and posterior probability of the CF and IvSRS. The results showed IvSRS performed better (p < 0.05) in classifying sleepiness to three states, compared to non-modified vSRS. Considering each CF and IvSRS as stand alone indicators, integrating all these information under a BN significantly improved the systems performance (p ≤ 0.05). In addition to being able to function well in the event of missing vSRS, the proposed system has a prediction horizon of 12 h, with F1-measure > 78%.
DISCOVERY OF STRAIN IN ONLINE NETWORKS WITH THE SUPPORT OF PUBLIC INTERACTIONS

Psychological stress threatens people's health. It is not easy to spot stress in a timely and proactive manner. With popular social networking, people are accustomed to sharing their daily activities and interacting with friends on social media platforms, making it possible to take advantage of social network data over the Internet to detect stress. In this paper, users' stress status is closely related to their friends in social networks, and we use a broad set of data from real social platforms to conduct a systematic study of the relationship of stress. And social interactions of users. First, we define a set of textual, visual and social characteristics associated with stress from multiple aspects, and then propose a new hybrid model: a global graphical model shared with the neural network to leverage the use of Twitter content and social interaction information to detect stress. Experimental results show that the proposed model can improve detection performance by 5-10% in F1. In further analysis of social interaction data, we also found many interesting phenomena, i.e., the number of dispersed social connections (i.e., without Delta connections) of users is approximately 13% higher than non-fanatical users, indicating that the social structure of stressful friends tends to be less connected and less complex than non-authorized users.
Dense Refinement Residual Network for Road Extraction From Aerial Imagery Data

Extraction of roads from high-resolution aerial images with a high degree of accuracy is a prerequisite in various applications. In aerial images, road pixels and background pixels are generally in the ratio of ones-to-tens, which implies a class imbalance problem. Existing semantic segmentation architectures generally do well in road-dominated cases but fail in background-dominated scenarios. This paper proposes a dense refinement residual network (DRR Net) for semantic segmentation of aerial imagery data. The proposed semantic segmentation architecture is composed of multiple DRR modules for the extraction of diversified roads alleviating the class imbalance problem. Each module of the proposed architecture utilizes dense convolutions at various scales only in the encoder for feature learning. Residual connections in each module of the proposed architecture provide the guided learning path by propagating the combined features to subsequent DRR modules. Segmentation maps undergo various levels of refinement based on the number of DRR modules utilized in the architecture. To emphasize more on small object instances, the proposed architecture has been trained with a composite loss function. The qualitative and quantitative results are reported by utilizing the Massachusetts roads dataset. The experimental results report that the proposed architecture provides better results as compared to other recent architectures.
Free Vibration Analysis of a Thin-Walled Beam with Shear Sensitive Material

This paper presents a method for a free vibration analysis of a thin-walled beam of doubly asymmetric cross section filled with shear sensitive material. In the study, first of all, a dynamic transfer matrix method was obtained for planar shear flexure and torsional motion. Then, uncoupled angular frequencies were obtained by using dynamic element transfer matrices and boundary conditions. Coupled frequencies were obtained by the well-known two-dimensional approaches. At the end of the study, a sample taken from the literature was solved, and the results were evaluated in order to test the convenience of the method.
Speckle Reduction for Holographic Display Using Optical Path Difference and Random Phase Generator

In this paper, we present a concept for speckle reduction using optical superposition instead of temporal superposition. The proposed speckle reduction method does not contain any mechanical movement nor time-multiplexing technique. Therefore, the system is stable and free from the frame rate degradation or additional computation load. In addition, the angle diversity method is adopted with the proposed method to intensify the speckle reduction. In order to apply the angle diversity method, the required minimum angle difference is analyzed theoretically. To realize the proposed speckle reduction concept, the system is implemented with practical optical components. The system includes an echelon, which is a stair-shaped glass and a static diffuser. The echelon is employed to decrease the spatial coherence of the incident beam. The static diffuser imposes the band-limited random phases on each of the split beams that have passed through the echelon. Also, the static diffuser is used to combine mutually incoherent split beams into a single speckle-reduced beam. The proposed speckle-reduced light source is applied to the holographic display to demonstrate the speckle reduction effect. Experimental results show that the speckle contrast of the reconstructed image is reduced by 55%.
Dosage Prediction in Pediatric Medication Leveraging Prescription Big Data

Rational use of medicines is of great importance in pediatric clinical medication. As the pharmacokinetics and pharmacodynamics of the pediatric group is highly dynamic, it is a great challenge to determine the rational dosage for pediatric patients. Traditional clinical decision support systems for dosage guidance largely rely on manual collection of medication information, which usually suffers from incomplete and missing evidences for the pediatric group. In this paper, we propose a data-driven approach to accurately predict pediatric medication dosages by leveraging prescription big data. More specifically, we first identify two relevant factors of pediatric medication dosage, i.e., the physiology factors including patients’ body weight and age group, and the indication factors that affect clinical dosage patterns. We then extract the corresponding physiology and indication features, and propose a hybrid-learning-based method to adaptively integrate the two sets of heterogeneous features into a model for accurate pediatric dosage prediction. We evaluate our method on real-world prescription datasets from two tertiary children’s hospitals. Results show that our method predicts pediatric medication dosages with an accuracy above 81.3%, and consistently outperforms other baselines.
Selected Paper at the ICCMIT’19 in Vienna, Austria Enhancing Learning from Imbalanced Classes via Data Preprocessing: A Data-Driven Application in Metabolomics Data Mining (cid:73)

This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes which is known to deteriorate the performance of classifiers. It also influences its validity and generalizablity. The classification models in this study were built using five machine learning algorithms known as PLS-DA, MLP, SVM, C4.5 and ID3. This model is built after carrying out a number of intensive data preprocessing procedures to tackle the problem of imbalanced classes and improve the performance of the constructed classifiers. These procedures involves applying data transformation, normalization, standardization, re-sampling and data reduction procedures using a number of variables importance scorers. The best performance was achieved by building an MLP model that was trained and tested using five-fold cross-validation using datasets that were re-sampled using SMOTE method and then reduced using SVM variable importance scorer. This model was successful in classifying samples with excellent accuracy and also in identifying the potential disease biomarkers. The results confirm the validity of metabolomics data mining for diagnosis of cachexia. It also emphasizes the importance of data preprocessing procedures such as sampling and data reduction for improving data mining results, particularly when data suffers from the problem of imbalanced classes
Attention Model based Deep Learning Architecture for EEG Seizure Detection

Because voice data is similar to EEG data, they are both temporal and concurrent. In order to explore the general appropriateness of speech recognition model in EEG database, this paper introduces the attention model which has been very good in speech field in recent years, and carries out experiments on TESC data set. The attention model in this paper is based on the traditional HMM model, and Z module is added to introduce the attention mechanism. At the input end of the whole framework, PCANet model is introduced to solve the problem of too high dimension of TESC raw data. The convolution size used in PCANet is the most important group of super parameters. It is optimized by convex optimization alone. Many groups of training have been carried out on the horizontal and vertical dimensions, and the maximum local accuracy has been obtained. In order to improve the recall rate, the penalty of incorrect data classification has been greatly increased. In order to further improve the accuracy, the depth of PCANet model is increased. The input and output dimensions of PCANet model are adjusted to the same size and cascaded in multiple layers. The final accuracy rate was raised to 60%.
Optimal sleep time controller based on traffic prediction and residual energy in duty-cycled wireless sensor networks

In duty-cycled wireless sensor networks, energy efficiency and packet latency are two most important metrics in the design of medium access control and routing algorithms. However, these two problems cannot be addressed well at the same time. In this article, we investigate the trade-off between energy consumption and latency and formulate them into a multi-objective optimization problem. By applying the single exponential smoothing method, we estimate the amount of data of next period and design two optimal sleep time controllers according to time scheduling model of network, so as to dynamically adjust the duty cycle of end node. Our controllers also consider the residual energy of end node. Finally, we propose two dynamic and adaptive medium access control algorithms for synchronous and asynchronous duty-cycled wireless sensor networks, respectively. We evaluate our protocols with different parameters and compare them with existing works. Performance results show that our proposed algorithms can balance power consumption among nodes and improve power efficiency while guaranteeing packet latency is minimized.
Research on Intelligent Campus Monitoring Management System Based on Deep Neural Network Algorithm

With the development of the economy and the advancement of science and technology, the informationization of education management has become an inevitable trend, and the construction of a intelligent campus cannot be delayed. In order to comply with the current social development, major universities have invested heavily in IoT equipment to improve the management level of the school. Based on the large number of video cameras installed in colleges and universities, this paper uses the monitoring image big data and the CNN algorithm to train a set of graphical features that are easy to manage, without adding additional hardware purchases. In addition, combined with the camera position and other information, an intelligent campus monitoring system is built to help the campus administrator to understand the people flow, traffic flow and physical condition in real time, which is convenient for enhancing the effective monitoring and management of campus crowds, environment and security.
Static verification of data-consistency properties

Writing correct shared-memory concurrent programs is hard. Not only must a programmer reason about the correctness of the sequential execution of code, but also about the possible side effects caused by interleaved execution of concurrently executing threads. Incorrect use of synchronization primitives can lead to data-consistency errors, which can have drastic consequences (cf. the Northeast Blackout of 2003). This dissertation presents techniques to verify statically that the programmer's use of synchronization primitives preserves data consistency. The notion of data consistency used throughout the dissertation is atomic-set serializability (AS-serializability), which was first proposed by Vaziri et al. (2006). AS-serializability is a property of a program execution, and is a relaxation of serializability. An execution is serializable if its outcome is equivalent to an execution where all transactions are executed serially. AS-serializability relaxes serializability to be only with respect to specific memory locations. The approach taken is to use software model checking to verify that every possible execution of a concurrent program is AS-serializable. First, an abstract program is generated from a concrete program. The abstract program is defined such that it over-approximates the set of behaviors of the concrete program. Second, a software model checker explores all possible executions of the abstract program. The challenge is to define abstractions and techniques that account for the multitude of sources of unboundedness in a concrete program. Concrete programs have dynamic memory allocation, recursion, dynamic thread creation, and reentrant locks, to name a few. The contributions of the dissertation are generic techniques to permit model checking to be performed in the presence of several sources of unboundedness. My research addressed the problem of determining whether all possible executions of a certain class of models of concurrent Java programs are AS-serializable. Somewhat surprisingly, given the many sources of unboundedness allowed in the models considered, I was able to show that the problem is decidable, and gave a practical algorithm for the problem. The technique has been implemented in a tool called EMPIRE, which has been used to find known bugs in concurrent Java programs.
Null distance on a spacetime

Given a time function τ on a spacetime M, we define a null distance function, d ˆ τ ? > , built from and closely related to the causal structure of M. In basic models with timelike ∇ τ ? > , we show that (1) d ˆ τ ? > is a definite distance function, which induces the manifold topology, (2) the causal structure of M is completely encoded in d ˆ τ ? > and τ. In general, d ˆ τ ? > is a conformally invariant pseudometric, which may be indefinite. We give an ‘anti-Lipschitz’ condition on τ, which ensures that d ˆ τ ? > is definite, and show this condition to be satisfied whenever τ has gradient vectors ∇ τ ? > almost everywhere, with ∇ τ ? > locally ‘bounded away from the light cones’. As a consequence, we show that the cosmological time function of Andersson et al (1998 Class. Quantum Grav. 15 309–22) is anti-Lipschitz when ‘regular’, and hence induces a definite null distance function. This provides what may be interpreted as a canonical metric space structure on spacetimes which emanate from a common initial singularity, e.g. a ‘big bang’.
Around-device interaction for exploring large information spaces on mobile devices

II Acknowledgements . III Copyright Notices and Disclaimers . V Chapter 1: Introduction . 1 Motivation . 2 Research Objective . 4 Research Questions . 4 Research Approach . 6 Contribution . 10 Chapter 2: Related Work . 14 Around-Device Interaction . 14 Around-Device Sensing . 21 Infrared proximity sensor . 22 Vision-Based Sensing . 23 Magnetic-Field Based Sensing . 26 On-Board Sensor Based Sensing . 27 Non-Speech Voice and Acoustic-Based Sensing . 27 Electric Field Sensing Approaches . 29 Other Sensing Approaches . 29 Around-Device Feedback . 31 On-Device Visual Feedback . 31 In-Air Visual Feedback . 33 Non-Visual On-Device Feedback . 36 Non-Visual In-Air Feedback . 37 AD-Interactions and Social Acceptance . 40 Social Acceptability . 41 Thumb Input on Mobile Devices . 44 Bimanual Interaction . 46 VIII Information Browsing on Mobile Devices . 47 Chapter 3: Design and Evaluation of the Around-Device Space for Accessing Mobile Device Content . 51 AD-Binning Design Framework . 52 Design Factors . 52 Selection methods . 52 Bin size . 53 Visual feedback . 53 Space discretization for bins . 54 Input range around the device . 54 Ideal binning locations . 54 Binning methods . 54 Mode switching . 55 AD-Binning Apparatus and Input Range . 55 Apparatus . 55 AD-Binning Input Range . 56 Study 1: Design Factor Exploration . 57 Selection Methods . 58 Session A – Selection Methods and Bin Size . 59 Results . 60 Summary – Session A . 63 Session B – Space Discretization & Binning Locations . 63 Task and experimental design . 64 Results . 65 Summary – Session B . 68 Study 2 – Binning & Retrieval . 68 Participants, Task and Experimental Design. 69 Results . 71 Summary . 73 Study 3 – Analytic Task . 74