Reviewerly/exHarmony huggingface dataset:
CQNet: A Clustering-Based Quadruplet Network for Decentralized Application Classification via Encrypted Traffic

Decentralized applications (DApps), along with the development of blockchain technology, are increasingly developed and deployed on blockchain platforms. DApps based on the same platform usually adopt similar traffic encryption settings and the same communication interface, leading to traffic less distinguishable. However, existing classification methods either require manual-design features or need lots of data to train the classifier, otherwise suffering from low accuracy. In this paper, we apply metric learning to DApps encrypted traffic classification problem and propose the clustering-based quadruplet network (CQNet). The CQNet can filter out useless samples to reduce the training dataset’s redundancy data by utilizing the proposed algorithm, thereby improving the classifier’s efficiency. Moreover, we adopt a quadruplet structure that can mine more restrictive relationships among quadruplets and provide rich information to the classifier. Our comprehensive experiments on the real-world dataset covering 60 DApps indicate that CQNet can achieve excellent performance with high efficiency and is superior to the state-of-the-art methods in terms of accuracy and efficiency.
Coupled numerical simulations of the structure and acoustics of a violin body

This study is aimed at predicting the characteristics of vibration and sound radiation of violins and understanding the relationships among the properties of its wood, vibration, and sound radiation. Numerical simulations of the vibration mode of a violin body are performed, and the sound radiated by it are analyzed using the finite element method. The geometry of a real violin is scanned using a micro-computed tomography scanner, and the orthotropic properties of spruce and maple, such as Young's modulus, rigidity modulus, and Poisson's ratio, are set as the parameters of the numerical simulation. The main vibration modes, such as A0 and center bout rotation, and the acoustic pressure level around the violin body are calculated. This paper describes the influence of the density and longitudinal stiffness on the eigenfrequencies and sound radiation.
GigaMVS: A Benchmark for Ultra-Large-Scale Gigapixel-Level 3D Reconstruction

Multiview stereopsis (MVS) methods, which can reconstruct both the 3D geometry and texture from multiple images, have been rapidly developed and extensively investigated from the feature engineering methods to the data-driven ones. However, there is no dataset containing both the 3D geometry of large-scale scenes and high-resolution observations of small details to benchmark the algorithms. To this end, we present GigaMVS, the first gigapixel-image-based 3D reconstruction benchmark for ultra-large-scale scenes. The gigapixel images, with both wide field-of-view and high-resolution details, can clearly observe both the <i>Palace</i> -scale scene structure and <i>Relievo</i> -scale local details. The ground-truth geometry is captured by the laser scanner, which covers ultra-large-scale scenes with an average area of 8667 m <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> and a maximum area of 32007 m <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> . Owing to the extremely large scale, complex occlusion, and gigapixel-level images, GigaMVS exposes problems that emerge from the poor scalability and efficiency of the existing MVS algorithms. We thoroughly investigate the state-of-the-art methods in terms of geometric and textural measurements, which point to the weakness of the existing methods and promising opportunities for future works. We believe that GigaMVS can benefit the community of 3D reconstruction and support the development of novel algorithms balancing robustness, scalability and accuracy.
White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain Magnetic Resonance Imaging Using Adaptive U-Net and Local Convolutional Neural Network

Abstract According to the World Alzheimer Report 2015, 46 million people are living with dementia in the world. The diagnosis of diseases helps doctors treating patients better. One of the signs of diseases is related to white matter, grey matter and cerebrospinal fluid. Therefore, the automatic segmentation of three tissues in brain imaging especially from magnetic resonance imaging (MRI) plays an important role in medical analysis. In this research, we proposed an effective approach to segment automatically these tissues in three-dimensional (3D) brain MRI. First, a deep learning model is used to segment the sure and unsure regions. In the unsure region, another deep learning model is used to classify each pixel. In the experiments, an adaptive U-net model is used to segment the sure and unsure regions, and the Local Convolutional Neural Network (CNN) model with multiple inputs is used to classify each pixel only in the unsure region. Our method was evaluated with a real image database, Internet Brain Segmentation Repository database, with 18 persons (IBSR 18) (https://www.nitrc.org/projects/ibsr) and compared with state of art methods being the results very promising.
Massively Parallel Computation via Remote Memory Access

We introduce the Adaptive Massively Parallel Computation (AMPC) model, which is an extension of the Massively Parallel Computation (MPC) model. At a high level, the AMPC model strengthens the MPC model by storing all messages sent within a round in a distributed data store. In the following round, all machines are provided with random read access to the data store, subject to the same constraints on the total amount of communication as in the MPC model. Our model is inspired by the previous empirical studies of distributed graph algorithms [8, 30] using MapReduce and a distributed hash table service [17]. This extension allows us to give new graph algorithms with much lower round complexities compared to the best-known solutions in the MPC model. In particular, in the AMPC model we show how to solve maximal independent set in O (1) rounds and connectivity/minimum spanning tree in O (log log m / n n rounds both using O ( n δ ) space per machine for constant δ &lt; 1. In the same memory regime for MPC, the best-known algorithms for these problems require poly log n rounds. Our results imply that the 2-C YCLE conjecture, which is widely believed to hold in the MPC model, does not hold in the AMPC model.
Applying virtual reality to teach the software development process to novice software engineers

Software development is a complicated process that requires experienced human resources to produce successful software products. Although this process needs experience from the individuals, it is hard to provide this experience without encountering real incidents during the software development process. To fill this gap, this study proposes a Virtual Reality Based Software Development Framework (VR-SODEF), which provides an interactive virtual reality experience for individuals learning about the tasks of software development starting from requirement analysis through software testing. In the VR-SODEF, the participant takes on the role of a novice software developer being recruited into a virtual software development organisation who should work alongside five virtual characters, played by artificial intelligence. This exclusive viewpoint draws participants from the 2D separation of the classical experience and virtually into the world of the software development itself. Participants experience the intense dramatic elements created for simulation and confront the challenges of virtual software practitioners in a somewhat uncompromising virtual simulation environment. To examine the efficiency of the VR-SODEF, it was tested on 32 computing students, with results indicating that virtual reality can be an effective educational medium, especially for skills that might traditionally be acquired through experience rather than traditional classroom-based teaching.
Urban IoT ontologies for sharing and electric mobility

Cities worldwide are facing the challenge of digital information governance: different and competing service providers operating Internet of Things (IoT) devices often produce and maintain large amounts of data related to the urban environment. As a consequence, the need for interoperability arises between heterogeneous and distributed information, to enable city councils to make data-driven decisions and to provide new and effective added value services to their citizens. In this paper, we present the Urban IoT suite of ontologies, a common conceptual model to harmonise the data exchanges between municipalities and service providers, with specific focus on the sharing mobility and electric mobility domains.
Three-Dimensional Microscopic Image Reconstruction Based on Structured Light Illumination

In this paper, we propose and experimentally demonstrate a three-dimensional (3D) microscopic system that reconstructs a 3D image based on structured light illumination. The spatial pattern of the structured light changes according to the profile of the object, and by measuring the change, a 3D image of the object is reconstructed. The structured light is generated with a digital micro-mirror device (DMD), which controls the structured light pattern to change in a kHz rate and enables the system to record the 3D information in real time. The working distance of the imaging system is 9 cm at a resolution of 20 μm. The resolution, working distance, and real-time 3D imaging enable the system to be applied in bridge and road crack examinations, and structure fault detection of transportation infrastructures.
Lacking the Embedding of a Word? Look it up into a Traditional Dictionary

Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often covered by traditional dictionaries. In this paper, we propose to use definitions retrieved in traditional dictionaries to produce word embeddings for rare words. For this purpose, we introduce two methods: Definition Neural Network (DefiNNet) and Define BERT (DefBERT). In our experiments, DefiNNet and DefBERT significantly outperform state-of-the-art as well as baseline methods devised for producing embeddings of unknown words. In fact, DefiNNet significantly outperforms FastText, which implements a method for the same task-based on n-grams, and DefBERT significantly outperforms the BERT method for OOV words. Then, definitions in traditional dictionaries are useful to build word embeddings for rare words.
Set-membership filtering for automatic guided vehicles with unknown-but-bounded noises

This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and [Formula: see text] filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.
Do Cryptocurrencies Offer Diversification Benefits for Equity Portfolios?

Abstract Cryptocurrencies are becoming an exciting topic for legislative bodies, practitioners, media, and scholars with diverse academic backgrounds. The work identifies diversification benefits when cryptocurrencies are combined with the equity instruments from Visegrad Stock Exchanges. Furthermore, the results of the study explore financial and economic benefits for the investors of combining cryptocurrencies with equity stocks on the mixed portfolio. Three different independent experiments were conducted to observe diversification benefits generated from cryptocurrencies. Results from the two experiments show that cryptocurrencies employ higher portfolio risk and generate higher returns when they are involved with equity stocks portfolios. The first experiment indicates that cryptocurrencies reduce the risk level of the equity portfolios while increase average returns. Providing the equity portfolios with additional equity stocks lower the portfolio risk which is in line with the theoretical paradigms. Results indicate that cryptocurrencies must be seriously considered by the portfolio managers as an essential aspect of the portfolio diversification benefits. Future studies might raise the samples of selected portfolios with stocks from different stock indexes, to identify the problem from a broader perspective.
Modelling chaotic dynamical attractor with fractal-fractional differential operators

&lt;abstract&gt;&lt;p&gt;Differential operators based on convolution have been recognized as powerful mathematical operators able to depict and capture chaotic behaviors, especially those that are not able to be depicted using classical differential and integral operators. While these differential operators have being applied with great success in many fields of science, especially in the case of dynamical system, we have to confess that they were not able depict some chaotic behaviors, especially those with additionally similar patterns. To solve this issue new class of differential and integral operators were proposed and applied in few problems. In this paper, we aim to depict chaotic behavior using the newly defined differential and integral operators with fractional order and fractal dimension. Additionally we introduced a new chaotic operators with strange attractors. Several simulations have been conducted and illustrations of the results are provided to show the efficiency of the models.&lt;/p&gt;&lt;/abstract&gt;
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis

Global models are typically trained to be as generalizable as possible.Invariance to the specific user is considered desirable since models are shared across multitudes of users.However, these models are often unable to produce personalized responses for individual users, based on their data.Contrary to widely-used personalization techniques based on few-shot and meta-learning, we propose UserIdentifier, a novel scheme for training a single shared model for all users.Our approach produces personalized responses by prepending a fixed, user-specific non-trainable string (called "user identifier") to each user's input text.Unlike prior work, this method doesn't need any additional model parameters, any extra rounds of personal few-shot learning, or any change made to the vocabulary.We empirically study different types of user identifiers (numeric, alphanumeric, and also randomly generated) and demonstrate that, surprisingly, randomly generated user identifiers outperform the prefixtuning based state-of-the-art approach by up to 13%, on a suite of sentiment analysis datasets.
Learnable Boundary Guided Adversarial Training

Previous adversarial training raises model robustness under the compromise of accuracy on natural data. In this paper, our target is to reduce natural accuracy degradation. We use the model logits from one clean model $\mathcal{M}^{natural}$ to guide learning of the robust model $\mathcal{M}^{robust}$, taking into consideration that logits from the well trained clean model $\mathcal{M}^{natural}$ embed the most discriminative features of natural data, {\it e.g.}, generalizable classifier boundary. Our solution is to constrain logits from the robust model $\mathcal{M}^{robust}$ that takes adversarial examples as input and make it similar to those from a clean model $\mathcal{M}^{natural}$ fed with corresponding natural data. It lets $\mathcal{M}^{robust}$ inherit the classifier boundary of $\mathcal{M}^{natural}$. Thus, we name our method Boundary Guided Adversarial Training (BGAT). Moreover, we generalize BGAT to Learnable Boundary Guided Adversarial Training (LBGAT) by training $\mathcal{M}^{natural}$ and $\mathcal{M}^{robust}$ simultaneously and collaboratively to learn one most robustness-friendly classifier boundary for the strongest robustness. Extensive experiments are conducted on CIFAR-10, CIFAR-100, and challenging Tiny ImageNet datasets. Along with other state-of-the-art adversarial training approaches, {\it e.g.}, Adversarial Logit Pairing (ALP) and TRADES, the performance is further enhanced.
The Effects of Network Outages on User Experience in Augmented Reality Based Remote Collaboration - An Empirical Study

Augmented Reality (AR) applications can enable geographically distant users to collaborate using shared video feeds or interactive 3D holograms, and may be particularly useful in the socially distant context of the Covid-19 pandemic. However, a good user experience is key for their success and could be negatively impacted by network impairments, which are an inevitable occurrence in today's best-effort Internet. In this paper, we present the findings of an empirical user study, aimed at understanding the effects of network outages, on user experience and behavior, in a collaborative AR task. We highlight how network outages affected users in different ways depending on their role in the collaborative task, and how giving users explicit information about poor network conditions helped them deal with some of these negative effects. Furthermore, we report the strategies that users themselves adopted, to deal with outages, such as batching instructions, or shifting to a different spatial referencing style when communicating with their partners. Lastly, based on our findings, we present some design implications for future remote-collaborative AR applications.
Towards the no-code era: a vision and plan for the future of software development

This paper provides a highly opinionated and biased vision and a two-stage plan with guidelines to reach a new era of software development, where anyone can create software without bothering to write code. Moreover, this paper explores in depth the first of these stages, which consists of creating a no-code tool based on six principles: configuration driven development, APIs, open-source, cross-platform, cloud computing, and design systems. An examination of each principle is presented and a case is made for why such a combination of principles would lay the foundation for future development efforts. Possible enquiries are addressed and a path is laid out for future works.
Integrating sample similarities into latent class analysis: a tree‐structured shrinkage approach

Abstract This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree‐structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data‐driven leaf groups with distinct vectors of class probabilities, and (3) individual‐level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions.
Deconfounded Causal Collaborative Filtering

Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving the problem usually design each specific model for each specific confounder. However, real-world systems may include a huge number of confounders and thus designing each specific model for each specific confounder could be unrealistic. More importantly, except for those ``explicit confounders'' that experts can manually identify and process such as item's position in the ranking list, there are also many ``latent confounders'' that are beyond the imagination of experts. For example, users' rating on a song may depend on their current mood or the current weather, and users' preference on ice creams may depend on the air temperature. Such latent confounders may be unobservable in the recorded training data. To solve the problem, we propose Deconfounded Causal Collaborative Filtering (DCCF). We first frame user behaviors with unobserved confounders into a causal graph, and then we design a front-door adjustment model carefully fused with machine learning to deconfound the influence of unobserved confounders. Experiments on real-world datasets show that our method is able to deconfound unobserved confounders to achieve better recommendation performance.
Voice and Touch Based Error-tolerant Multimodal Text Editing and Correction for Smartphones

Editing operations such as cut, copy, paste, and correcting errors in typed text are often tedious and challenging to perform on smartphones. In this paper, we present VT, a voice and touch-based multi-modal text editing and correction method for smartphones. To edit text with VT, the user glides over a text fragment with a finger and dictates a command, such as "bold" to change the format of the fragment, or the user can tap inside a text area and speak a command such as "highlight this paragraph" to edit the text. For text correcting, the user taps approximately at the area of erroneous text fragment and dictates the new content for substitution or insertion. VT combines touch and voice inputs with language context such as language model and phrase similarity to infer a user's editing intention, which can handle ambiguities and noisy input signals. It is a great advantage over the existing error correction methods (e.g., iOS's Voice Control) which require precise cursor control or text selection. Our evaluation shows that VT significantly improves the efficiency of text editing and text correcting on smartphones over the touch-only method and the iOS's Voice Control method. Our user studies showed that VT reduced the text editing time by 30.80%, and text correcting time by 29.97% over the touch-only method. VT reduced the text editing time by 30.81%, and text correcting time by 47.96% over the iOS's Voice Control method.