Reviewerly/exHarmony huggingface dataset:
Enhanced Association With Supervoxels in Multiple Hypothesis Tracking

Remarkable progress has been made in the field of multi-object tracking.Although trackingby-detection has recently became one of the most popular frameworks, it still has one main drawback: this approach relies heavily on the quality of detection.Thus, the missing detections caused by partial occlusion usually lead to fragment problem.To address this problem, this paper introduces supervoxels to represent objects with partial occlusion, even for missing detections.We first extract superpixels of the foreground, and then our proposed supervoxel consists of spatial-temporal sequences of superpixels.The supervoxels represent tracklets at the image level, so it is robust for initial detection.Then, we incorporate supervoxels into multiple hypotheses tracking by considering the enhanced association with supervoxels (EAS).Moreover, we propose a detection refinement method based on EAS.As our approach allows us to handle partial occlusion problems, we achieve remarkable results in crowded scenes.Finally, our experiments on both MOT15 and MOT16 benchmarks show that our EAS is competitive with the state-of-the-art trackers.
swMR: A Framework for Accelerating MapReduce Applications on Sunway Taihulight

MapReduce has already been an indispensable framework for the programmers to develop big data applications at scale without concerning the underlying complex system details. However, such an important framework is missing on the Sunway many-core processor that powers the world-leading supercomputer Sunway Taihulight. This paper fills the gap by implementing an efficient MapReduce framework on Sunway many-core processor which takes full advantage of the architecture features such as local device memory and register communication. Specifically, we propose three different schemes that adopt different methods to partition the computation of map and reduce across the MPE and CPEs of Sunway processor. Especially in CPE-accelerated Dynamic Partitioning Scheme (CDPS), the processing role of the CPEs within each CPE pair can be transformed dynamically between map and reduce, which is effective to improve the load balance during runtime. In addition, we adopt the local device memory as well as register communication on each CPE to improve the efficiency of data access and communication. The experiment results demonstrate our MapReduce framework based on CDPS achieves 36.9× performance speedup on average across representative benchmarks.
An Assigned Color Depth Packing Method With Centralized Texture Depth Packing Formats for 3D VR Broadcasting Services

The ability to distribute 3D video and audio in traditional television channels is key to deliver three dimensional (3D) virtual reality (VR) broadcasting services, which allows a limited number of connected players and unlimited number of audiences to enjoy 3D video, audio, and passive interactions. Recently, the centralized texture depth packing (CTDP) formats with color depth packing are proposed for delivering 3D videos. In this paper, we revise the procedure of CTDP computation and introduce an assigned color depth packing method that packs the depth information of VR in RGB color space and depacks it in YCbCr or RGB color space even if the video coder is conducted in YCbCr 4:2:0 and 4:2:2 chroma formats. Under uncoded and coded conditions, the performances of the depacked depth, texture, and synthesized frames of the proposed method are compared to those of the existed packing algorithms. Finally, a trial 3D video and VR broadcasting system based on the left-ad-right CTDP format have been tested in Hsin Yeong, a cable company in Taiwan.
Predicting Diverse Future Frames With Local Transformation-Guided Masking

Video prediction is the challenging task of generating the future frames of a video given a sequence of previously observed frames. This task involves the construction of an internal representation that accurately models the frame evolutions, including contents and dynamics. Video prediction is considered difficult due to the inherent compounding of errors in recursive pixel level prediction. In this paper, we present a novel video prediction system that focuses on regions of interest (ROIs) rather than on entire frames and learns frame evolutions at the transformation level rather than at the pixel level. We provide two strategies to generate high-quality ROIs that contains potential moving visual cues. The frame evolutions are modeled with a transformation generator that produces transformers and masks simultaneously, which are then combined to generate the future frame in a transformation-guided masking procedure. Compared with recent approaches, our system is able to generate more accurate predictions by modeling the visual evolutions at the transformation level rather than at the pixel level. Focusing on ROIs avoids a heavy computational burden and enables our system to generate high-quality long-term future frames without severely amplified signal loss. Moreover, our system is able to generate diverse plausible future frames, which is important in many real-world scenarios. Furthermore, we enable our system to perform video prediction conditioned on a single frame by revising the transformation generator to produce motion-centric transformers. We test our system on four datasets with different experimental settings and demonstrate its advantages over recent methods, both quantitatively and qualitatively.
Anonymous and Updatable Identity-Based Hash Proof System

In EUROCRYPT 2002, Alwen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> demonstrated that an identity-based hash proof system (IB-HPS) can be used to generate an identity-based encryption (IBE) scheme, which is secure against partial leakage of the decryption key of the target identity. However, in many real-world applications, the leakage is normally unbounded, and an adversary could violate the security of such cryptography schemes by performing continuous leakage attacks. Therefore, cryptography schemes are desirable to maintain their claimed security even in the continuous leakage setting. In this paper, we first design an improved IB-HPS called updatable IB-HPS (U-IB-HPS), which has an additional key update algorithm to embed some fresh randomness into the user's private key, ensuring that enough entropy is maintained in the updated keys. Then, we further use the U-IB-HPS to propose a generic construction of continuously leakage-resilient IBE schemes. Finally, an instantiation of the U-IB-HPS with anonymity is presented. As a result, our construction delivers an anonymous IBE scheme that is secure against continuous leakage of user's private key. The security of our proposed scheme has been proved in the random oracle model under the classic decisional bilinear Diffie–Hellman assumption, which is a standard static security assumption.
Inside a 3D simulation: Realism, dramatism and challenge in the development of students’ teacher digital competence

The universalisation of immersive technologies and digital 3D environments has enabled simulated experiences to be created in various contexts, one of which is education. These environments have immense potential for, for example, generating immersive situations in which students can experience real learning. Although realism, dramatism and challenge have been little explored in the field of education, they are increasingly viewed as interesting conditions to be satisfied when simulations aimed at ensuring meaningful learning in the virtual world are designed. This article reflects on these three qualities in a simulation experience for initial teacher training conducted under the Simul@b project, the main objective of which was to design, implement and evaluate a 3D simulations laboratory for developing teacher digital competence among primary and secondary schoolteachers. The results of this experience show that simulation in 3D environments is a productive strategy with enormous potential. Moreover, these environments enable the creation of realistic scenarios that allow teaching competences to be developed and whose simulation has a positive impact on learning.
Benchmarking Classification Models for Cell Viability on Novel Cancer Image Datasets

Background: Dye-exclusion based cell viability analysis has been broadly used in cell biology including anticancer drug discovery studies. Viability analysis refers to the whole decision making process for the distinction of dead cells from live ones. Basically, cell culture samples are dyed with a special stain called trypan blue, so that the dead cells are selectively colored to darkish. This distinction provides critical information that may be used to expose influences of the studied drug on considering cell culture including cancer. Examiner’s experience and tiredness substantially affect the consistency throughout the manual observation of cell viability. The unsteady results of cell viability may end up with biased experimental results accordingly. Therefore, a machine learning based automated decision-making procedure is inevitably needed to improve consistency of the cell viability analysis. Objective: In this study, we investigate various combinations of classifiers and feature extractors (i.e. classification models) to maximize the performance of computer vision-based viability analysis. Method: The classification models are tested on novel hemocytometer image datasets which contain two types of cancer cell images, namely, caucasian promyelocytic leukemia (HL60), and chronic myelogenous leukemia (K562). Results: From the experimental results, k-Nearest Neighbor (KNN) and Random Forest (RF) by combining Local Phase Quantization (LPQ) achieve the lowest misclassification rates that are 0.031 and 0.082, respectively. Conclusion: The experimental results show that KNN and RF with LPQ can be powerful alternatives to the conventional manual cell viability analysis. Also, the collected datasets are released from the “biochem.atilim.edu.tr/datasets/” web address publically to academic studies.
Rating prediction for recommendation: Constructing user profiles and item characteristics using backpropagation

Recommender systems play an important role in quickly identifying and recommending most acceptable products to the users. The latent user factors and item characteristics determine the degree of user satisfaction on an item. While many of the methods in the literature have assumed that these factors are linear, there are some other methods that treat these factors as nonlinear; but they do it in a more implicit way. In this paper, we have investigated the effect of true nature (i.e., nonlinearity) of the user factors and item characteristics, and their complex layered relationship on rating prediction. We propose a new deep feedforward network that learns both the factors and their complex relationship concurrently. The aim of our study was to automate the construction of user profiles and item characteristics without using any demographic information and then use these constructed features to predict the degree of acceptability of an item to a user. We constructed the user and item factors by using separate learner weights at the lower layers, and modeled their complex relationship in the upper layers. The construction of the user profiles and the item characteristics, solely based on rating triples (i.e., user id, item id, rating), overcomes the requirement of explicit demographic information be given to the system. We have tested our model on three real world datasets: Jester, Movielens, and Yahoo music. Our model produces better rating predictions than some of the state-of-the-art methods which use demographic information. The root mean squared error incurred by our model on these datasets are 4.0873, 0.8110, and 0.9408 respectively. The errors are smaller than current best existing models' errors in these datasets. The results show that our system can be integrated to any web store where development of hand engineered features for recommending products is less feasible due to huge traffics and also that there is a lack of demographic information about the users and the items.
Towards a visual guide for communicating uncertainty in Visual Analytics

This article presents a first step towards the definition of a visual guide for communicating uncertainty which is to fit into existing visualisation frameworks and toolkits. The first entry in our guide is made by a set of visual variables appropriate for representing areal uncertainty in algorithm mechanics. Such visualisations show users how data points are distributed in the classification space and allow them to understand the “goodness-of-fit” of their data to the algorithm. This is important for Visual Analytics applications, which combine Information Visualisation with information mining techniques in an interactive decision-making process. Model uncertainties stemming from widely spread data points need to be visualised so that the user can make adjustments and improve the analysis. To capitalise on established knowledge and meaning, we explore whether popular visual variables for representing areal uncertainty in the domain of geospatial visualisation may also be effective for representing uncertainty in the visualisation of the mechanics of K-means clustering and Linear Regression algorithms, as both use a spatial distribution of data points. In a study with 500 participants we find that overall the visual means opacity performs best, followed by texture, but that grid and blur may be unsuitable for quantifying uncertainty. The performance of contour lines appears to depend on the algorithm visualisation. Using this study, we extend the validity of a set of domain-specific findings from geospatial visualisation to the visualisation of algorithm mechanics and use these to form the first building blocks of a cross-disciplinary visual guide for representing uncertainty, laying promising foundations for future work.
Introduction to the special issue on affect awareness in software engineering

In the past decade, brain and autonomic nervous system activity measurement received increasing attention in the study of software engineering (SE). This paper presents a systematic literature review (SLR) to survey the existing NeuroSE literature. Based on a rigorous search protocol, we identified 89 papers (hereafter denoted as NeuroSE papers). We analyzed these papers to develop a comprehensive understanding of who had published NeuroSE research and classified the contributions according to their type. The 47 articles presenting completed empirical research were analyzed in detail. The SLR revealed that the number of authors publishing NeuroSE research is still relatively small. The thematic focus so far has been on code comprehension, while code inspection, programming, and bug fixing have been less frequently studied. NeuroSE publications primarily used methods related to brain activity measurement (particularly fMRI and EEG), while methods related to the measurement of autonomic nervous system activity (e.g., pupil dilation, heart rate, skin conductance) received less attention. We also present details of how the empirical research was conducted, including stimuli and independent and dependent variables, and discuss implications for future research. The body of NeuroSE literature is still small. Yet, high quality contributions exist constituting a valuable basis for future studies.
Investigation and comparison of ECG signal sparsity and features variations due to pre-processing steps

The pre-processing steps such as ﬁltering, derivatives, and wavelet transform are necessary for many applications before data transmission, especially in telemedicine; however, the pre-processing makes variations on the signal sparsity, entropy, and compression metrics. In this paper, aiming at an information-theoretical study, we exemplify pre-processing by Savitzky Golay ﬁltering because of its special properties, and then, show that (i) adding noise to an ECG signal decreases its sparsity and increases the diversity index named Gini-Sympson as a special case of Tsallis entropy; (ii) the sparsity of ﬁltered, and wavelet transformed ECG is increased; (iii) Gini index of the modified signal is not more than that of the main one, but the non-zero elements are decreased, (iv) the compression metrics such as PRD and CR are improved if the compressed sensing method is performed on the ﬁltered signal. And, finally, theoretical claims are validated by numerical results.
Geochemical pattern recognition through matrix decomposition

This study compares the use of joint singular value decomposition and semi-discrete decomposition (JSS) and non-negative matrix factorisation with univariate analysis of raw data, to detect multi-element patterns in soils related to geochemical dispersion from Mississippi Valley-Type Pb-Zn deposits in the Irankuh area of central Iran. Joint singular value decomposition and semi-discrete decomposition clustering of the data identified a suite of mineralisation-related variables and corresponding sample clusters that are spatially associated with mineralisation or variations in parent lithology. Non-negative matrix factorisation generated three main factors and sample clusters that relate to the main zones of sulphide mineralisation, variations in clay mineralogy and the composition of unaltered host rocks. These two matrix decomposition techniques deliver similar results, though JSS delivers a significantly higher sample classification accuracy. Both methods result in delineation of a contiguous cluster of samples above an extensive zone of blind mineralisation at the Tappe Sorkh Pb-Zn deposit and define new potentially mineralised targets between this deposit and the Gushfil Pb-Zn deposit. More conventional univariate methods to detect anomalous populations of Pb and Zn in the soil geochemical data, such as number-size fractal analysis, were less effective at defining these targets. Joint application of practical matrix decomposition techniques, utilising noise removal and modelling of the underlying geochemical patterns associated with ore-forming processes, has led to more reliable prospectivity mapping of the Pb-Zn deposits in Irankuh.
Two distinct routes for inducing emotions in HCI design

While there is a proliferation of research on emotion detection, little research has focused on emotion induction in HCI literature. We propose an emotion-induction model that points out the two fundamental design goals (i.e., sensemaking vs. exploration) as antecedents of the four different types of emotions, which, in turn, introspectively reinforce cognitive outcomes (i.e., attitude; behavioral intention). Design elements that facilitate the sensemaking goal (i.e., coherence; legibility) induce emotions along a low-arousal-positive (LAN) to a high-arousal-negative (HAN) continuum (e.g., "quiescence" to "agitation"). By contrast, the design elements that facilitate exploration goal (i.e., mystery; complexity) induce emotions along a high-arousal-positive (HAP) to a low-arousal-negative (LAN) continuum (e.g., "cheerfulness" to "dejection"). Among them, only the two types of emotions (i.e., HAP; HAN) have a significant mediation effect of reinforcing attitude. The HAP emotions (e.g., cheerfulness) signal a success in exploration efforts. The HAN emotions (e.g., agitation) signal a failure in sensemaking efforts. Thus, the design elements for sensemaking goals are responsible for the emotions that enhance positive (but not negative) attitude, while the design features for exploration are responsible for the emotions that enhance negative (but not positive) attitude. Incidentally, our data reveals that people place more weight on emotional signals that are consistent with their concurrent motivational concern than those inconsistent with it.
Capacity-Achieving Codes That Mitigate Intercell Interference and Charge Leakage in Flash Memories

We investigate constant-composition constrained codes for the mitigation of intercell interference for multilevel cell flash memories with a dynamic threshold scheme. The first explicit formula for the maximum size of a q-ary F-avoiding code with a given composition and certain families of substrings F is presented. In addition, we provide methods to determine the asymptotic rate for F-avoiding codes with any composition ratio and to find the optimal composition ratio that maximizes the asymptotic rate. We also give the first efficient encoder/decoder for these q-ary constant-composition codes achieving the channel capacity, for all q values.
Generator Polynomial Model-Based Eye Diagram Estimation Method for Bose-Chaudhuri-Hocquenghem (BCH) Code and Reed-Solomon (RS) Code

This paper proposes a generator polynomial model-based eye diagram estimation method for the error-correction codes (ECCs), which are the Bose-Chaudhuri-Hocquenghem (BCH) code and the Reed-Solomon (RS) code. The ECCs compensate for the channel loss at the bit level, so most of the systems include the ECC block. The BCH and RS codes are the generator polynomial-based ECCs; this paper proposes the generator polynomial model-based method. The model provides the accurate probability density function of the code words for the BCH and RS code. Thus, the proposed method is capable of achieving the signal integrity analysis for the system level. For verification, we design the BCH and RS encoders. The estimated eye diagrams are nearly the same as that of the transient simulation for the BCH and RS codes. Furthermore, the voltage bathtub curves are the same up to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-8</sup> . For a smaller bit-error rate value, the proposed method only provides the bathtub curve. This paper successfully verifies the eye diagram estimation method for the BCH and RS codes.
A density-based competitive data stream clustering network with self-adaptive distance metric

Data stream clustering is a branch of clustering where patterns are processed as an ordered sequence. In this paper, we propose an unsupervised learning neural network named Density Based Self Organizing Incremental Neural Network(DenSOINN) for data stream clustering tasks. DenSOINN is a self organizing competitive network that grows incrementally to learn suitable nodes to fit the distribution of learning data, combining online unsupervised learning and topology learning by means of competitive Hebbian learning rule. By adopting a density-based clustering mechanism, DenSOINN discovers arbitrarily shaped clusters and diminishes the negative effect of noise. In addition, we adopt a self-adaptive distance framework to obtain good performance for learning unnormalized input data. Experiments show that the DenSOINN can achieve high standard performance comparing to state-of-the-art methods.
Digital stone rubbing from 3D models

Stone rubbing is a traditional method to record surface features of a stone on paper. It has a long history and is still used by Chinese archaeologists and artists up to now. However, stone rubbing can cause permanent damage to precious cultural heritage due to abrasion. We propose a new method to create digital stone rubbing from 3D digital models. We assume the surface of a stone is flat locally and fit a least-squares plane to each point's neighbors individually. To prevent the local plane from being fitted across sharp edges or corners of the model, we make use of the smoothed point normals in the selection of local surface region. The height of a point is defined as the signed distance between the point and its local approximation plane. The estimated height of each point is then mapped to its grey value by a nonlinear transformation function. Finally, we render the 3D model into a rubbing image using orthogonal projection. We demonstrate the effectiveness of the method on real experimental dataset in archaeology.