Reviewerly/exHarmony huggingface dataset:
Teaching and Learning Crystal structures through Virtual Reality based systems

Teaching and learning through Virtual Reality(VR) is an emerging technology in the last few years. In this article, the development and use of a VR based teaching–learning system for crystal structures are discussed. The VR system is designed as a lab environment where a user can do experiments related to crystal structures. The VR system is designed in Unity,1 and Oculus Rift S2 is used as a VR headset. Currently, the system consists of three phases; in the first phase user can visualize the crystal lattice structures, wherein the second one a user can visualize the light interaction with the crystal lattice structure using a virtual torch ray. The third phase is the X-ray Diffraction (XRD) experiment. In this phase, users can perform the XRD experiment in the lab environment by taking a random crystal from a crystal dispenser machine and placing it in the X-ray machine which identifies the chosen crystalline material and analyses the unit cell. The incident ray colour changes when there is a peak found in the crystal for a better understanding of the user. There is also an interactive display where users can increase/decrease the angles of the radiation and also lock and unlock the experiment to view the diffraction plot for the crystal structure. In many cases, it was found that XRD and the crystal structure is available in the course syllabus but there are no experiments to enhance their learning. Therefore an experiment with 39 participants was performed where the maximum participants are new to crystallography. The study was conducted in two phases; in the first phase, participants are asked to watch video tutorials of the topic followed by questionnaires; in the second phase participants are asked to do the VR based experiment and followed by questionnaires related to overall study and experiment. From the analysis of the study we found that everyone found VR based teaching methods are better than traditional book/video studies. Study results give an average score of 56.74% in comparison to VR based learning approach with an average score of 93.81%. Participants who took part in the experiment found the experience interactive and motivating and found it helpful to learn elusive concepts, which can be learned when simulated. For example, one participant wrote: “The VR experience was surreal and was easy to control. Lucid user experience. Got a view of XRD like never seen before”.
Annular-Graph Attention Model for Personalized Sequential Recommendation

Sequential recommendations aim to predict the user&#x2019;s next behaviors items based on their successive historical behaviors sequence. It has been widely applied in lots of online services. However, current sequential recommendations use the adjacent behaviors to capture the features of the sequence, ignoring the features among nonadjacent sequential items and the summarized features of the sequence. To address the above problems, in this paper, we propose an annular-graph attention based sequential recommendation (AGSR) model by exploring user&#x2019;s long-term and short-term preferences for the personalized sequential recommendation. For user&#x2019;s short-term preferences, AGSR builds an annular-graph on the sequence of user behavior. Then, AGSR proposes an annular-graph attention applying on the sub annular-graph to explore local features and applying annular-graph attention on entire annular-graph to explore the global features and the skip features. For user&#x2019;s long-term preferences, the latent factor model are introduced in AGSR. The experimental results on two public datasets show that our model outperforms the state-of-the-art methods.
Efficient Sorting of Homomorphic Encrypted Data With <i>k</i>-Way Sorting Network

In this study, we propose an efficient sorting method for encrypted data using fully homomorphic encryption (FHE). The proposed method extends the existing 2-way sorting method by applying the k-way sorting network for any prime k to reduce the depth in terms of comparison operation from O(log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n) to O(klog <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n), thereby improving performance for k slightly larger than 2, such as k=5. We apply this method to approximate FHE which is widely used due to its efficiency of homomorphic arithmetic operations. In order to build up the k-way sorting network, the k-sorter, which sorts k-numbers with a minimal comparison depth, is used as a building block. The approximate homomorphic comparison, which is the only type of comparison working on approximate FHE, cannot be used for the construction of the k-sorter as it is because the result of the comparison is not binary, unlike the comparison in conventional bit-wise FHEs. To overcome this problem, we propose an efficient k-sorter construction utilizing the features of approximate homomorphic comparison. Also, we propose an efficient construction of a k-way sorting network using cryptographic SIMD operations. To use the proposed method most efficiently, we propose an estimation formula that finds the appropriate k that is expected to reduce the total time cost when the parameters of the approximating comparisons and the performance of the operations provided by the approximate FHE are given. We also show the implementation results of the proposed method, and it shows that sorting 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> = 15625 data using 5-way sorting network can be about 23.3% faster than sorting 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sup> = 16384 data using 2-way.
Deploying Federated Learning in Large-Scale Cellular Networks: Spatial Convergence Analysis

The deployment of federated learning in a wireless network, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated edge learning</i> (FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model while preserving data privacy. In this work, we study the spatial (i.e., spatially averaged) learning performance of FEEL deployed in a large-scale cellular network with spatially random distributed devices. Both the schemes of digital and analog transmission are considered, providing support of error-free uploading and over-the-air aggregation of local model updates by devices. The derived spatial convergence rate for digital transmission is found to be constrained by a limited number of active devices regardless of device density and converges to the ground-true rate exponentially fast as the number grows. The population of active devices depends on network parameters such as processing gain and signal-to-interference threshold for decoding. On the other hand, the limit does not exist for uncoded analog transmission. In this case, the spatial convergence rate is slowed down due to the direct exposure of signals to the perturbation of inter-cell interference. Nevertheless, the effect diminishes when devices are dense as interference is averaged out by aggressive over-the-air aggregation. In terms of learning latency (in second), analog transmission is preferred to the digital scheme as the former dramatically reduces multi-access latency by enabling simultaneous access.
Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

The rapid penetration of photovoltaic generation reduces power grid inertia and increases the need for intelligent energy resources that can cope in real time with the imbalance between power generation and consumption. Virtual power plants are a technology for coordinating such resources and monetizing them, for example on electricity markets with real-time pricing or on frequency reserves markets. Accurate short-term photovoltaic generation forecasts are essential for such virtual power plants. Although significant research has been done on medium- and long-term photovoltaic generation forecasting, the short-term forecasting problem requires special attention to sudden fluctuations due to the high variability of cloud cover and related weather events. Solar irradiance nowcasting aims to resolve this variability by providing reliable short-term forecasts of the expected power generation capacity. Sky images captured in proximity to the photovoltaic panels are used to determine cloud behavior and solar intensity. This is a computationally challenging task for conventional computer vision techniques and only a handful of Artificial Intelligence (AI) methods have been proposed. In this paper, a novel multimodal approach is proposed based on two Long Short-Term Memory Networks (LSTM) that receives a temporal image modality of a stream of sky images, a temporal numerical modality of a time-series of past solar irradiance readings and cloud cover readings as inputs for irradiance nowcasting. The proposed nowcasting pipeline consists of a preprocessing module and an irradiance augmentation module that implements methods for cloud detection, Sun localization and mask generation. The complete approach was empirically evaluated on a real-world solar irradiance case study across the four seasons of the northern hemisphere, resulting in a mean improvement of 39% for multimodality.
Identifying complaints based on semi-supervised mincuts

In today's competitive business world, customer service is often at the heart of businesses that can help strengthen their brands. Customer complaint resolution in a timely and efficient manner is key to improving customer satisfaction. Moreover, customers' complaints play important roles in identifying their requirements which offer a starting point for effective and efficient planning of the company's overall R&D and new product or service development activities. That said, businesses face challenges towards automatically identifying complaints buried deep in massive online content. In this paper, we propose a graph-based semi-supervised learning paradigm leveraging syntactic and semantic representations of tweets. Intrinsic evaluation results on a benchmark dataset illustrate that the proposed approach outperforms state-of-the-art supervised non-graph based classification models for solving the complaints identification task, and confirms the efficacy of the proposed approach. Experimental results also show that the performance of the state-of-the-art supervised complaint classification model trained over hand-crafted features extracted from several linguistic resources can be reached with less than 50% of the training data with the proposed approach.
A compositional model for effort‐aware Just‐In‐Time defect prediction on android apps

Android apps have played important roles in daily life and work. To meet the new requirements from users, the apps encounter frequent updates, which involves a large quantity of code commits. Previous studies proposed to apply Just-in-Time (JIT) defect prediction for apps to timely identify whether the new code commits can introduce defects into apps, aiming to assure their quality. In general, high-quality features are benefits for improving the classification performance. In addition, the number of defective commit instances is much fewer than that of clean ones, that is the defect data is class imbalanced. In this study, a novel compositional model, called KPIDL, is proposed to conduct the JIT defect prediction task for Android apps. More specifically, KPIDL first exploits a feature learning technique to preprocess original data for obtaining better feature representation, and then introduces a state-of-the-art cost-sensitive cross-entropy loss function into the deep neural network to alleviate the class imbalance issue by considering the prior probability of the two types of classes. The experiments were conducted on a benchmark defect data consisting of 15 Android apps. The experimental results show that the proposed KPIDL model performs significantly better than 25 comparative methods in terms of two effort-aware performance indicators in most cases.
A Neural Network Training Processor With 8-Bit Shared Exponent Bias Floating Point and Multiple-Way Fused Multiply-Add Trees

Recent advances in deep neural networks (DNNs) and machine learning algorithms have induced the demand for services based on machine learning algorithms that require a large number of computations, and specialized hardware ranging from accelerators for data centers to on-device computing systems have been introduced. Low-precision math such as 8-bit integers have been used in neural networks for energy-efficient neural network inference, but training with low-precision numbers without performance degradation have remained to be a challenge. To overcome this challenge, this article presents an 8-bit floating-point neural network training processor for state-of-the-art non-sparse neural networks. As naïve 8-bit floating-point numbers are insufficient for training DNNs robustly, two additional methods are introduced to ensure high-performance DNN training. First, a novel numeric system which we dub as 8-bit floating point with shared exponent bias (FP8-SEB) is introduced. Moreover, multiple-way fused multiply-add (FMA) trees are used in FP8-SEB’s hardware implementation to ensure higher numerical precision and reduced energy. FP8-SEB format combined with multiple-way FMA trees is evaluated under various scenarios to show a trained-from-scratch performance that is close to or even surpasses that of current networks trained with full-precision (FP32). Our silicon-verified DNN training processor utilizes 24-way FMA trees implemented with FP8-SEB math and flexible 2-D routing schemes to show <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.48\times $ </tex-math></inline-formula> higher energy efficiency than prior low-power neural network training processors and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$78.1\times $ </tex-math></inline-formula> lower energy than standard GPUs.
Designing Better Exposure Notification Apps: The Role of Persuasive Design

Background Digital contact tracing apps have been deployed worldwide to limit the spread of COVID-19 during this pandemic and to facilitate the lifting of public health restrictions. However, due to privacy-, trust-, and design-related issues, the apps are yet to be widely adopted. This calls for an intervention to enable a critical mass of users to adopt them. Objective The aim of this paper is to provide guidelines to design contact tracing apps as persuasive technologies to make them more appealing and effective. Methods We identified the limitations of the current contact tracing apps on the market using the Government of Canada’s official exposure notification app (COVID Alert) as a case study. Particularly, we identified three interfaces in the COVID Alert app where the design can be improved. The interfaces include the no exposure status interface, exposure interface, and diagnosis report interface. We propose persuasive technology design guidelines to make them more motivational and effective in eliciting the desired behavior change. Results Apart from trust and privacy concerns, we identified the minimalist and nonmotivational design of exposure notification apps as the key design-related factors that contribute to the current low uptake. We proposed persuasive strategies such as self-monitoring of daily contacts and exposure time to make the no exposure and exposure interfaces visually appealing and motivational. Moreover, we proposed social learning, praise, and reward to increase the diagnosis report interface’s effectiveness. Conclusions We demonstrated that exposure notification apps can be designed as persuasive technologies by incorporating key persuasive features, which have the potential to improve uptake, use, COVID-19 diagnosis reporting, and compliance with social distancing guidelines.
An Automated Multi-Tab Website Fingerprinting Attack

In Website Fingerprinting (WF) attack, a local passive eavesdropper utilizes network flow information to identify which web pages a user is browsing. Previous researchers have demonstrated the feasibility and effectiveness of WF attacks under a strong Single Page Assumption: the network flow extracted by the adversary belongs to a single web page. In reality, the assumption may not hold because users tend to open multiple tabs simultaneously (or within a short period of time) so that their network traffic is mixed. In this article, we propose an automated multi-tab Website Fingerprinting attack that is able to accurately classify websites regardless of the number of simultaneously opened pages. Our design is powered by two innovative designs. First, we develop a split point classification method to dynamically identify the split point between the first page and its subsequent pages. As a result, the network traffic before the split point is solely generated for the first page. Then, we propose a new chunk-based WF classifier to infer the websites based on the initial chunk of clean traffic. For both classifiers, we apply automated feature selection to select a concise yet representative feature set. We implement a prototype of our design and perform extensive evaluations using SSH and Tor-based datasets to demonstrate the effectiveness of both our system components individually and the integrated system as a whole.
Quantum Sealed-Bid Auction Without a Trusted Third Party

A sealed-bid auction is an essential tool for trading commodities in modern society. However, the non-winning bidders in previously proposed sealed-bid auction schemes lack privacy protection. In this paper, we define a sealed-bid auction with strong privacy protection, which can protect the privacy of all non-winning bidders. In order to fulfil the task, we first present a quantum secret sharing protocol and a secure quantum summation protocol and then design their corresponding quantum circuits, where the security of the latter is guaranteed by the former, while the former is information-theoretically secure. We further present a quantum seal-bid auction (QSA) scheme with strong privacy protection based on the designed quantum protocols. In our QSA scheme, each bidder first encodes a private vector by his private bid and public agreement information, and then all bidders jointly compute the summations of their respective private vectors without a trusted third party. Finally, we verify the correctness and the feasibility of the proposed QSA scheme by circuit simulations in IBM Qiskit.
Enhanced Feature Alignment for Unsupervised Domain Adaptation of Semantic Segmentation

Unsupervised domain adaptation for semantic segmentation aims to transfer knowledge from a labeled source domain to another unlabeled target domain. However, due to the label noise and domain mismatch, learning directly from source domain data tends to have poor performance. Though adversarial learning methods strive to reduce domain discrepancies by aligning feature distributions, traditional methods suffer from the training imbalance and feature distortion problems. Besides, due to the absence of target domain labels, the classifier is blind to features from the target domain during training. Consequently, the final classifier overfits the source domain features and usually fails to predict the structured outputs of the target domain. To alleviate these problems, we focus on enhancing the adversarial learning based feature alignment from three perspectives. First, a classification constrained discriminator is proposed to balance the adversarial training and alleviate the feature distortion problem. Next, to alleviate the classifier overfitting problem, self-training is collaboratively used to learn a domain robust classifier with target domain pseudo labels. Moreover, an efficient class centroid calculation module is proposed and the domain discrepancy is further reduced by aligning the feature centroids of the same class from different domains. Experimental evaluations on GTA5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> Cityscapes and SYNTHIA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> Cityscapes demonstrate state-of-the-art results compared to other counterpart methods. The source code and models have been made available at. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> [Online]. Available: <uri>https://github.com/NUST-Machine-Intelligence-Laboratory/EFA</uri>. </fn>
CaT: Weakly Supervised Object Detection with Category Transfer

A large gap exists between fully-supervised object detection and weakly-supervised object detection. To narrow this gap, some methods consider knowledge transfer from additional fully-supervised dataset. But these methods do not fully exploit discriminative category information in the fully-supervised dataset, thus causing low mAP. To solve this issue, we propose a novel category transfer framework for weakly supervised object detection. The intuition is to fully leverage both visually-discriminative and semantically-correlated category information in the fully-supervised dataset to enhance the object-classification ability of a weakly-supervised detector. To handle overlapping category transfer, we propose a double-supervision mean teacher to gather common category information and bridge the domain gap between two datasets. To handle non-overlapping category transfer, we propose a semantic graph convolutional network to promote the aggregation of semantic features between correlated categories. Experiments are conducted with Pascal VOC 2007 as the target weakly-supervised dataset and COCO as the source fully-supervised dataset. Our category transfer framework achieves 63.5% mAP and 80.3% CorLoc with 5 overlapping categories between two datasets, which outperforms the state-of-the-art methods. Codes are avaliable at https://github.com/MediaBrain-SJTU/CaT.
The future of continuous learning–Digital badge and microcredential system using blockchain

Continuous Learning is seen as the key to remain competitive and effective at work. Very often, this also takes place outside the walls of a classroom. Nowadays Digital Badges and microcredentials are pathways to acknowledge and recognize achievements and specific skills of a learner. In this research, the different techniques used in existing blockchain-based educational systems have been reviewed and in the end, a novel web-based digital badge and microcredentials system which helps the learners to acquire the desired skills is proposed. For the Blockchain, Ethereum has been used and to develop the front end ReactJS has been used. A digital badging and micro-credential system consisting of quizzes and digital badges was built with Ethereum blockchain together with ipfs to store the badge image and firebase for examination, registration, and storing courses. The proposed system proved to be very much beneficial and addressed the inherent limitations of traditional learning content management systems (LCMS).