Reviewerly/exHarmony huggingface dataset:
UIS-Hunter: Detecting UI Design Smells in Android Apps

Similar to code smells in source code, UI design has visual design smells that indicate violations of good UI design guidelines. UI design guidelines constitute design systems for a vast variety of products, platforms, and services. Following a design system, developers can avoid common design issues and pitfalls. However, a design system is often complex, involving various design dimensions and numerous UI components. Lack of concerns on GUI visual effect results in little support for detecting UI design smells that violate the design guidelines in a complex design system. In this paper, we propose an automated UI design smell detector named UIS-Hunter (UI design Smell Hunter). The tool is able to (i) automatically process UI screenshots or prototype files to detect UI design smells and generate reports, (ii) highlight the violated UI regions and list the material design guidelines that the found design smells violate, and (iii) present conformance and violation UI design examples to assist understanding. This tool consists of a Material Design guidelines gallery website and a tool website. The gallery website is a back-end knowledge base that attaches conformance and violation examples to abstract design guidelines and allows developers and designers to explore the multi-dimensional space of a complex design system in a more structured way. As a front-end application, the tool website takes a UI design as input, returns a detailed UI design smell report, and marks the violation regions (if any). Moreover, the tool website presents conformance and violation examples based on the gallery website. Demo URL: https://uishunter.net.cn/https://uishuntergallery.net.cn/Demo Video: https://youtu.be/7UZ0jtD_1gM.
Multimodal Face Synthesis From Visual Attributes

Synthesis of face images from visual attributes is an important problem in computer vision and biometrics due to its applications in law enforcement and entertainment. Recent advances in deep generative networks have made it possible to synthesize high-quality face images from visual attributes. However, existing methods are specifically designed for generating unimodal images (i.e visible faces) from attributes. In this paper, we propose a novel generative adversarial network that simultaneously synthesizes identity preserving multimodal face images (i.e. visible, sketch, thermal, etc.) from visual attributes without requiring paired data in different domains for training the network. We introduce a novel generator with multimodal stretch-out modules to simultaneously synthesize multimodal face images. Additionally, multimodal stretch-in modules are introduced in the discriminator which discriminates between real and fake images. Extensive experiments and comparisons with several state-of-the-art methods are performed to verify the effectiveness of the proposed attribute-based multimodal synthesis method.
Towards Modeling the Style of Translators in Neural Machine Translation

One key ingredient of neural machine translation is the use of large datasets from different domains and resources (e.g. Europarl, TED talks). These datasets contain documents translated by professional translators using different but consistent translation styles. Despite that, the model is usually trained in a way that neither explicitly captures the variety of translation styles present in the data nor translates new data in different and controllable styles. In this work, we investigate methods to augment the state of the art Transformer model with translator information that is available in part of the training data. We show that our style-augmented translation models are able to capture the style variations of translators and to generate translations with different styles on new data. Indeed, the generated variations differ significantly, up to +4.5 BLEU score difference. Despite that, human evaluation confirms that the translations are of the same quality.
A comparative study of deep learning based language representation learning models

Deep learning (DL) approaches use various processing layers to learn hierarchical representations of data. Recently, many methods and designs of natural language processing (NLP) models have shown significant development, especially in text mining and analysis. For learning vector-space representations of text, there are famous models like Word2vec, GloVe, and fastText. In fact, NLP took a big step forward when BERT and recently GTP-3 came out. In this paper, we highlight the most important language representation learning models in NLP and provide an insight of their evolution. We also summarize, compare and contrast these different models on sentiment analysis, and thus discuss their main strengths and limitations. Our obtained results show that BERT is the best language representation learning model.
SLGPT: Using Transfer Learning to Directly Generate Simulink Model Files and Find Bugs in the Simulink Toolchain

Finding bugs in a commercial cyber-physical system (CPS) development tool such as Simulink is hard as its codebase contains millions of lines of code and complete formal language specifications are not available. While deep learning techniques promise to learn such language specifications from sample models, deep learning needs a large number of training data to work well. SLGPT addresses this problem by using transfer learning to leverage the powerful Generative Pre-trained Transformer 2 (GPT-2) model, which has been pre-trained on a large set of training data. SLGPT adapts GPT-2 to Simulink with both randomly generated models and models mined from open-source repositories. SLGPT produced Simulink models that are both more similar to open-source models than its closest competitor, DeepFuzzSL, and found a super-set of the Simulink development toolchain bugs found by DeepFuzzSL.
How hard is cognitive science?

This is a preprint version of a published paper. Please cite as: Rich, P., de Haan, R., Wareham, T., &amp;amp; van Rooij, I. (2021). How hard is cognitive science? Proceedings of the Annual Meeting of the Cognitive Science Society, 43. Retrieved from https://escholarship.org/uc/item/8cr8x1c4
Detecting positive quantum capacities of quantum channels

Abstract Determining whether a noisy quantum channel can be used to reliably transmit quantum information is a challenging problem in quantum information theory. This is because it requires computation of the channel’s coherent information for an unbounded number of copies of the channel. In this paper, we devise an elementary perturbative technique to solve this problem in a wide variety of circumstances. Our analysis reveals that a channel’s ability to transmit information is intimately connected to the relative sizes of its input, output, and environment spaces. We exploit this link to develop easy tests which can be used to detect positivity of quantum channel capacities simply by comparing the channels’ input, output, and environment dimensions. Several noteworthy examples, such as the depolarizing and transpose-depolarizing channels (including the Werner-Holevo channel), dephasing channels, generalized Pauli channels, multi-level amplitude damping channels, and (conjugate) diagonal unitary covariant channels, serve to aptly exhibit the utility of our method. Notably, in all these examples, the coherent information of a single copy of the channel turns out to be positive.
Real-time Gesture Animation Generation from Speech for Virtual Human Interaction

We propose a real-time system for synthesizing gestures directly from speech. Our data-driven approach is based on Generative Adversarial Neural Networks to model the speech-gesture relationship. We utilize the large amount of speaker video data available online to train our 3D gesture model. Our model generates speaker-specific gestures by taking consecutive audio input chunks of two seconds in length. We animate the predicted gestures on a virtual avatar. We achieve a delay below three seconds between the time of audio input and gesture animation.
Rethinking "Batch" in BatchNorm

BatchNorm is a critical building block in modern convolutional neural networks. Its unique property of operating on "batches" instead of individual samples introduces significantly different behaviors from most other operations in deep learning. As a result, it leads to many hidden caveats that can negatively impact model's performance in subtle ways. This paper thoroughly reviews such problems in visual recognition tasks, and shows that a key to address them is to rethink different choices in the concept of "batch" in BatchNorm. By presenting these caveats and their mitigations, we hope this review can help researchers use BatchNorm more effectively.
Stable Adversarial Learning under Distributional Shifts

Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data. Recently, there are robust learning methods aiming at this problem by minimizing the worst-case risk over an uncertainty set. However, they equally treat all covariates to form the decision sets regardless of the stability of their correlations with the target, resulting in the overwhelmingly large set and low confidence of the learner. In this paper, we propose Stable Adversarial Learning (SAL) algorithm that leverages heterogeneous data sources to construct a more practical uncertainty set and conduct differentiated robustness optimization, where covariates are differentiated according to the stability of their correlations with the target. We theoretically show that our method is tractable for stochastic gradient-based optimization and provide the performance guarantees for our method. Empirical studies on both simulation and real datasets validate the effectiveness of our method in terms of uniformly good performance across unknown distributional shifts.
Cavitylike strong coupling in macroscopic waveguide QED using three coupled qubits in the deep non-Markovian regime

We introduce a three qubit waveguide QED system to mimic the cavity-QED strong coupling regime of a probe qubit embedded in atom-like mirrors, which was realized in recent experiments. We then extend this system into the deep non-Markovian regime and demonstrate the profound role that retardation plays on the dressed resonances, allowing one to significantly improve the polariton lifetimes (by many orders of magnitude), tune the resonances, as well as realize Fano-like resonances and simultaneous coupling to multiple cavity modes. Exact Green function solutions are presented for the spectral resonances, and the quantum dynamics are simulated using matrix product states, where we demonstrate additional control of the cavity-QED system using chiral probe qubits.
Multiparty Cardinality Testing for Threshold Private Intersection

Threshold Private Set Intersection (PSI) allows multiple parties to compute the intersection of their input sets if and only if the intersection is larger than $$n-t$$ , where n is the size of each set and t is some threshold. The main appeal of this primitive is that, in contrast to standard PSI, known upper-bounds on the communication complexity only depend on the threshold t and not on the sizes of the input sets. Current threshold PSI protocols split themselves into two components: A Cardinality Testing phase, where parties decide if the intersection is larger than some threshold; and a PSI phase, where the intersection is computed. The main source of inefficiency of threshold PSI is the former part. In this work, we present a new Cardinality Testing protocol that allows N parties to check if the intersection of their input sets is larger than $$n-t$$ . The protocol incurs in $$\tilde{ \mathcal {O}} (Nt^2)$$ communication complexity. We thus obtain a Threshold PSI scheme for N parties with communication complexity $$\tilde{\mathcal {O}}(Nt^2)$$ .
A semi-supervised zero-shot image classification method based on soft-target

Zero-shot learning (ZSL) aims at training a classification model with data only from seen categories to recognize data from disjoint unseen categories. Domain shift and generalization capability are two fundamental challenges in ZSL. In this paper, we address them with a novel Soft-Target Semi-supervised Classification (STSC) model. Specifically, an autoencoder network is leveraged, where both labeled seen data from the seen categories and unlabeled ancillary data collected from Internet or other datasets are employed as two branches, respectively. For the branch of labeled seen data, side information are employed as the latent vectors to separately connect the input of encoder and the output of decoder. In this way, visual and side information are implicitly aligned. For the branch of unlabeled ancillary data, it explicitly strengthens the reconstruction ability of the network. Meanwhile, these ancillary data can be viewed as a smooth to the domain distribution, which contributes to the alleviation of the domain shift problem. To further guarantee the generation ability, a Softmax-T loss function is proposed by making full use of the soft target. Extensive experiments on three benchmark datasets show the superiority of the proposed approach under tasks of both traditional zero-shot learning and generalized zero-shot learning.
Malicious URL Detection Based on Improved Multilayer Recurrent Convolutional Neural Network Model

The traditional malicious uniform resource locator (URL) detection method excessively relies on the matching rules formulated by the network security personnel, which is hard to fully express the text information of the URL. Thus, an improved multilayer recurrent convolutional neural network model based on the YOLO algorithm is proposed to detect malicious URL in this paper. First, single characters are mapped to dense vectors using word embedding, and the dense vectors are participated in the training process of the whole model according to the structural characteristics of the URL in the method. Then, the CSPDarknet neural network model based on the improved YOLO algorithm is proposed to extract features of the URL. Finally, the extracted features are used to evaluate malicious URL by the bidirectional LSTM recurrent neural network algorithm. In order to verify the validity of the algorithm, a total of 200,000 URLs are collected, including 100,000 normal URLs labeled “good” and 100,000 malicious URLs labeled “bad”. The experimental results show that the method detects malicious URLs more quickly and effectively and has high accuracy, high recall rate, and high accuracy compared with Text-RCNN, BRNN, and other models.
Basketball shooting technology based on acceleration sensor fusion motion capture technology

Abstract Computer vision recognition refers to the use of cameras and computers to replace the human eyes with computer vision, such as target recognition, tracking, measurement, and in-depth graphics processing, to process images to make them more suitable for human vision. Aiming at the problem of combining basketball shooting technology with visual recognition motion capture technology, this article mainly introduces the research of basketball shooting technology based on computer vision recognition fusion motion capture technology. This paper proposes that this technology first performs preprocessing operations such as background removal and filtering denoising on the acquired shooting video images to obtain the action characteristics of the characters in the video sequence and then uses the support vector machine (SVM) and the Gaussian mixture model to obtain the characteristics of the objects. Part of the data samples are extracted from the sample set for the learning and training of the model. After the training is completed, the other parts are classified and recognized. The simulation test results of the action database and the real shot video show that the support vector machine (SVM) can more quickly and effectively identify the actions that appear in the shot video, and the average recognition accuracy rate reaches 95.9%, which verifies the application and feasibility of this technology in the recognition of shooting actions is conducive to follow up and improve shooting techniques.
Performing Usability Evaluation on Multi-Platform Based Application for Efficiency, Effectiveness, and Satisfaction Enhancement

&lt;p class="0abstract"&gt;Usability evaluation is an essential activity to contribute to a higher standard of user experience. This study conducted a usability evaluation on multi-platform application according to usability attributes as defined in ISO 9241-11 standard which are efficiency, effectiveness, and satisfaction. The evaluation was conducted on both platforms; web and mobile application and involved 50 participants. The results discovered some points to usability issues. Therefore, to further enhance the users’ satisfaction, efficiency, and effectiveness, a set of improvement recommendations were proposed. Insights from this study provides a standard basis on the usability evaluation procedure which can serve as a guide for the usability evaluation of multi-platform applications.&lt;/p&gt;
Symmetry Allows for Distinguishability in Totally Destructive Many-Particle Interference

We investigate, in a four-photon interference experiment in a laser-written waveguide structure, how symmetries control the suppression of many-body output events of a Jx unitary. We show that totally destructive interference does not require mutual indistinguishability between all but only between symmetrically paired particles, in agreement with recent theoretical predictions. The outcome of the experiment is well described by a quantitative simulation that accounts for higher-order emission of the photon source, imbalances in the scattering network, partial distinguishability, and photon loss.Received 22 February 2021Accepted 21 April 2021DOI:https://doi.org/10.1103/PRXQuantum.2.020326Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasBoson samplingQuantum foundationsQuantum opticsPropertiesDiscrete symmetriesTechniquesPhoton countingAtomic, Molecular & OpticalGeneral PhysicsQuantum Information
Distance learning information literacy: Undergraduate students experience distance learning during the COVID-19 setting

This qualitative study provides an understanding about undergraduate students’ information literacy experience during their involvement with distance learning in an Indonesian university. Findings present evidence to inform the development of the distance learning process in Indonesia and informed learning responses that enhance the learning of undergraduate students. A total of 17 undergraduate students were interviewed using a semi-structured interview technique. The data collected were analyzed using thematic analysis, revealing four themes related to students’ information literacy experience during distance learning, namely, understanding distance learning, platform literacy, learning strategy, and learning resources. Findings can inform the development of informed learning responses to enhance the transition of undergraduate students into distance learning during a pandemic situation. These results may also be of benefit to teaching faculties for enhancing teaching and learning activities and to provide support for students.
Planetarium Glass Based on Augmented Reality to Improve Science Literacy Knowledge in Madura Primary Schools

One of the important branches of science on an international scale is scientific literacy. This study aimed to increase the science literacy knowledge of elementary school students on the material of the solar system. This study uses the ADDIE type Research and Development (R&amp;D) method, starting from analysis, design, development, implementation, and evaluation. Expert testing of a learning product was carried out by users of the Planetarium Glass application, namely grade VI students of SDN Patengteng 2. The results of this media trial on 22 respondents showed 80% effectiveness. This research instrument uses an online questionnaire to users to determine the effectiveness of this learning media. The final result of the Planetarium Glass application media based on augmented reality is an application format that can be used by utilizing an android-based smartphone. This media provides information about the material of the solar system in the form of 3D virtual animation (3 dimensions) accompanied by an explanation of each object in audio form and is equipped with an application usage module. This media is designed to facilitate students' understanding in understanding the material of the solar system effectively and efficiently because students can see directly the material conveyed via the smartphone they use. Learning using the Planetarium Glass application can be used as an alternative learning media in increasing science literacy knowledge of elementary school students, especially in Madura.
ÐArcher: detecting on-chain-off-chain synchronization bugs in decentralized applications

Since the emergence of Ethereum, blockchain-based decentralized applications (DApps) have become increasingly popular and important. To balance the security, performance, and costs, a DApp typically consists of two layers: an on-chain layer to execute transactions and store crucial data on the blockchain and an off-chain layer to interact with users. A DApp needs to synchronize its off-chain layer with the on-chain layer proactively. Otherwise, the inconsistent data in the off-chain layer could mislead users and cause undesirable consequences, e.g., loss of transaction fees. However, transactions sent to the blockchain are not guaranteed to be executed and could even be reversed after execution due to chain reorganization. Such non-determinism in the transaction execution is unique to blockchain. DApp developers may fail to perform the on-chain-off-chain synchronization accurately due to their lack of familiarity with the complex transaction lifecycle. In this work, we investigate the challenges of synchronizing on-chain and off-chain data in Ethereum-based DApps. We present two types of bugs that could result in inconsistencies between the on-chain and off-chain layers. To help detect such on-chain-off-chain synchronization bugs, we introduce a state transition model to guide the testing of DApps and propose two effective oracles to facilitate the automatic identification of bugs. We build the first testing framework, ÐArcher, to detect on-chain-off-chain synchronization bugs in DApps. We have evaluated ÐArcher on 11 popular real-world DApps. ÐArcher achieves high precision (99.3%), recall (87.6%), and accuracy (89.4%) in bug detection and significantly outperforms the baseline methods. It has found 15 real bugs in the 11 DApps. So far, six of the 15 bugs have been confirmed by the developers, and three have been fixed. These promising results demonstrate the usefulness of ÐArcher.