Reviewerly/exHarmony huggingface dataset:
Learning to Build User-tag Profile in Recommendation System

User profiling is one of the most important components in recommendation systems, where a user is profiled using demographic (e.g. gender, age, and location) and user behavior information (e.g. browsing and search history). Among different dimensions of user profiling, tagging is an explainable and widely-used representation of user interest. In this paper, we propose a user tag profiling model (UTPM) to study user-tag profiling as a multi-label classification task using deep neural networks. Different from the conventional model, our UTPM model is a multi-head attention mechanism with shared query vectors to learn sparse features across different fields. Besides, we introduce the improved FM-based cross feature layer, which outperforms many state-of-the-art cross feature methods and further enhances model performance. Meanwhile, we design a novel joint method to learn the preference of different tags from a single clicked news article in recommendation systems. Furthermore, our UTPM model is deployed in the WeChat "Top Stories" recommender system, where both online and offline experiments demonstrate the superiority of the proposed model over baseline models.
A Multi-Modal Approach for Driver Gaze Prediction to Remove Identity Bias

Driver gaze prediction is an important task in Advanced Driver Assistance System (ADAS). Although the Convolutional Neural Network (CNN) can greatly improve the recognition ability, there are still several unsolved problems due to the challenge of illumination, pose and camera placement. To solve these difficulties, we propose an effective multi-model fusion method for driver gaze estimation. Rich appearance representations, i.e. holistic and eyes regions, and geometric representations, i.e. landmarks and Delaunay angles, are separately learned to predict the gaze, followed by a score-level fusion system. Moreover, pseudo-3D appearance supervision and identity-adaptive geometric normalization are proposed to further enhance the prediction accuracy. Finally, the proposed method achieves state-of-the-art accuracy of 82.5288% on the test data, which ranks 1st at the EmotiW2020 driver gaze prediction sub-challenge.
Multi-Scroll Chaotic System Model and Its Cryptographic Application

This paper introduces a new chaotic system model that could generate multi-direction multi-scroll (MDMS) chaotic attractors. The fundamental dynamics characteristics of this model are investigated, and the feasibility of the proposed method is confirmed by numerical simulation based on MATLAB. Additionally, the new chaotic system is implemented in FPGA-based platform. Implementation results are the same in MATLAB and FPGA, which indicates that they are suitable for practical applications. To investigate the cryptographic application of the new chaotic system, the chaotic sequences generated by the proposed system are used to encrypt images. The key space of the algorithm as well as key sensitivity, plain image sensitivity, plaintext histogram, correlation and information entropy are simulated and analyzed. In contrast to several encryption schemes, the proposed algorithm is very sensitive to plaintext and the key.
Coinduction in Uniform: Foundations for Corecursive Proof Search with Horn Clauses

We establish proof-theoretic, constructive and coalgebraic foundations for proof search in coinductive Horn clause theories. Operational semantics of coinductive Horn clause resolution is cast in terms of coinductive uniform proofs; its constructive content is exposed via soundness relative to an intuitionistic first-order logic with recursion controlled by the later modality; and soundness of both proof systems is proven relative to a novel coalgebraic description of complete Herbrand models.
LiFtEr: Language to Encode Induction Heuristics for Isabelle/HOL

Proof assistants, such as Isabelle/HOL, offer tools to facilitate inductive theorem proving. Isabelle experts know how to use these tools effectively; however, there is a little tool support for transferring this expert knowledge to a wider user audience. To address this problem, we present our domain-specific language, LiFtEr. LiFtEr allows experienced Isabelle users to encode their induction heuristics in a style independent of any problem domain. LiFtEr’s interpreter mechanically checks if a given application of induction tool matches the heuristics, thus automating the knowledge transfer loop.
Modular Quantum Key Distribution Setup for Research and Development Applications

Quantum key distribution (QKD), ensuring the unconditional security of information, attracts a significant deal of interest. An important task is to design QKD systems as a platform for education as well as for research and development applications and fast prototyping new QKD protocols. Here we present a modular QKD setup driven by National Instruments (NI) cards with open source LabView code, open source Python code for post-processing procedures, and open source protocol for external applications. An important feature of the developed apparatus is its flexibility offering possibilities to modify optical schemes and verify novel QKD protocols. Another distinctive feature of the developed setup is the implementation of the decoy-state protocol, which is a standard tool for secure long-distance quantum communications. By testing the plug-and-play scheme realizing BB84 and decoy-state BB84 QKD protocols, we demonstrate that developed QKD setup shows a high degree of robustness beyond laboratory conditions. We demonstrate the results of the use of the developed modular setup for urban QKD experiments.
Local conformal autoencoder for standardized data coordinates

Significance A fundamental issue in empirical science is the ability to calibrate between different types of measurements/observations of the same phenomenon. This naturally suggests the selection of canonical variables, in the spirit of principal components, to enable matching/calibration among different observation modalities/instruments. We develop a method for extracting standardized, nonlinear, intrinsic coordinates from measured data, leading to a generalized isometric embedding of the observations. This is achieved through a local burst data acquisition strategy that allows us to capture the local z-scored structure. We implement this method using a local conformal autoencoder architecture and illustrate it computationally. The proposed embedding is fast, parallelizable, easy to implement using existing open-source neural network implementations and exhibits surprising interpolation and extrapolation capabilities.
Femtosecond Laser Direct Writing of Integrated Photonic Quantum Chips for Generating Path-Encoded Bell States

Integrated photonic quantum chip provides a promising platform to perform quantum computation, quantum simulation, quantum metrology and quantum communication. Femtosecond laser direct writing (FLDW) is a potential technique to fabricate various integrated photonic quantum chips in glass. Several quantum logic gates fabricated by FLDW have been reported, such as polarization and path encoded quantum controlled-NOT (CNOT) gates. By combining several single qubit gates and two qubit gates, the quantum circuit can realize different functions, such as generating quantum entangled states and performing quantum computation algorithms. Here we demonstrate the FLDW of integrated photonic quantum chips composed of one Hadamard gate and one CNOT gate for generating all four path-encoded Bell states. The experimental results show that the average fidelity of the reconstructed truth table reaches as high as 98.8 ± 0.3%. Our work is of great importance to be widely applied in many quantum circuits, therefore this technique would offer great potential to fabricate more complex circuits to realize more advanced functions.
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation

Large amounts of data has made neural machine translation (NMT) a big success in recent years.But it is still a challenge if we train these models on small-scale corpora.In this case, the way of using data appears to be more important.Here, we investigate the effective use of training data for low-resource NMT.In particular, we propose a dynamic curriculum learning (DCL) method to reorder training samples in training.Unlike previous work, we do not use a static scoring function for reordering.Instead, the order of training samples is dynamically determined in two ways -loss decline and model competence.This eases training by highlighting easy samples that the current model has enough competence to learn.We test our DCL method in a Transformerbased system.Experimental results show that DCL outperforms several strong baselines on three low-resource machine translation benchmarks and different sized data of WMT'16 En-De.
Searching for boundary extension

<h2>Summary</h2> Bainbridge and Baker [1] argue that boundary extension (BE), false memory beyond a view, is an artifact of stimulus selection. They dismiss theoretical explanations that include <i>scene construction</i> [2,3], and suggest removal of BE from textbooks. Their empirical work is an admirable study of scene errors, but the bridge between their data and their sweeping conclusions about BE is not well-grounded. They claim that BE is considered 'universal' and, thus, their observation of contraction (loss of peripheral content) in addition to extension violates a fundamental premise. They claim that reliance on narrow 'recycled' stimulus sets: object(s) centered on 'generic', non-scenic backgrounds created the artifact. Neither claim is correct.
Deep Learning Model for Cell Nuclei Segmentation and Lymphocyte Identification in Whole Slide Histology Images

Anti-cancer immunotherapy dramatically changes the clinical management of many types of tumours towards less harmful and more personalized treatment plans than conventional chemotherapy or radiation.Precise analysis of the spatial distribution of immune cells in the tumourous tissue is necessary to select patients that would best respond to the treatment.Here, we introduce a deep learning-based workflow for cell nuclei segmentation and subsequent immune cell identification in routine diagnostic images.We applied our workflow on a set of hematoxylin and eosin (H&E) stained breast cancer and colorectal cancer tissue images to detect tumour-infiltrating lymphocytes.Firstly, to segment all nuclei in the tissue, we applied the multiple-image input layer architecture (Micro-Net, Dice coefficient (DC) 0.79 ± 0.02).We supplemented the Micro-Net with an introduced texture block to increase segmentation accuracy (DC = 0.80 ± 0.02).We preserved the shallow architecture of the segmentation network with only 280 K trainable parameters (e.g.U-net with ∼1900 K parameters, DC = 0.78 ± 0.03).Subsequently, we added an active contour layer to the ground truth images to further increase the performance (DC = 0.81 ± 0.02).Secondly, to discriminate lymphocytes from the set of all segmented nuclei, we explored multilayer perceptron and achieved a 0.70 classification f-score.Remarkably, the binary classification of segmented nuclei was significantly improved (f-score = 0.80) by colour normalization.To inspect model generalization, we have evaluated trained models on a public dataset that was not put to use during training.We conclude that the proposed workflow achieved promising results and, with little effort, can be employed in multi-class nuclei segmentation and identification tasks.
Improving the Prediction Quality in Memory-Based Collaborative Filtering Using Categorical Features

Despite years of evolution of recommender systems, improving prediction accuracy remains one of the core problems among researchers and industry. It is common to use side information to bolster the accuracy of recommender systems. In this work, we focus on using item categories, specifically movie genres, to improve the prediction accuracy as well as coverage, precision, and recall. We derive the user’s taste for an item using the ratings expressed. Similarly, using the collective ratings given to an item, we identify how much each item belongs to a certain genre. These two vectors are then combined to get a user-item-weight matrix. In contrast to the similarity-based weight matrix in memory-based collaborative filtering, we use user-item-weight to make predictions. The user-item-weights can be used to explain to users why certain items have been recommended. We evaluate our proposed method using three real-world datasets. The proposed model performs significantly better than the baseline methods. In addition, we use the user-item-weight matrix to alleviate the sparsity problem associated with correlation-based similarity. In addition to that, the proposed model has a better computational complexity for making predictions than the k-nearest neighbor (kNN) method.
Unveiling the role of plasticity rules in reservoir computing

Reservoir Computing (RC) is an appealing approach in Machine Learning that combines the high computational capabilities of Recurrent Neural Networks with a fast and easy training method. Likewise, successful implementation of neuro-inspired plasticity rules into RC artificial networks has boosted the performance of the original models. In this manuscript, we analyze the role that plasticity rules play on the changes that lead to a better performance of RC. To this end, we implement synaptic and non-synaptic plasticity rules in a paradigmatic example of RC model: the Echo State Network. Testing on nonlinear time series prediction tasks, we show evidence that improved performance in all plastic models are linked to a decrease of the pair-wise correlations in the reservoir, as well as a significant increase of individual neurons ability to separate similar inputs in their activity space. Here we provide new insights on this observed improvement through the study of different stages on the plastic learning. From the perspective of the reservoir dynamics, optimal performance is found to occur close to the so-called edge of instability. Our results also show that it is possible to combine different forms of plasticity (namely synaptic and non-synaptic rules) to further improve the performance on prediction tasks, obtaining better results than those achieved with single-plasticity models.
Multi-stakeholder News Recommendation Using Hypergraph Learning

Abstract Recommender systems are meant to fulfil user preferences. Nevertheless, there are multiple examples where users are not the only stakeholder in a recommendation platform. For instance, in news aggregator websites apart from readers, one can consider magazines (news agencies) or authors as other stakeholders. A multi-stakeholder recommender system generates a ranked list of items taking into account the preferences of multiple stakeholders. In this study, news recommendation is handled as a hypergraph ranking task, where relations between multiple types of objects and stakeholders are modeled in a unified hypergraph. The obtained results indicate that ranking on hypergraphs can be utilized as a natural multi-stakeholder recommender system that is able to adapt recommendations based on the importance of stakeholders.
Research and Practice of Informatization Teaching Reform Based on Ubiquitous Learning Environment and Education Big Data

With the continuous accumulation of application data of various business information systems of network and informatization, a campus big data environment with distinctive characteristics of colleges and universities has been formed.Through the in-depth mining and analysis of the existing smart campus data using big data technology, based on the support of intelligent decision-making, personalized adaptive learning analysis and campus security warning of big data, and using the results to feed back the improvement of information literacy of university teachers and students in the era of big data, the construction of ubiquitous learning environment is carried out, which points out the direction for the construction of ubiquitous learning environment to explore the role of big data in improving the quality of education, promoting educational equity and optimizing educational philosophy, so as to improve the ubiquitous education environment.
The CNAME of the Game: Large-scale Analysis of DNS-based Tracking Evasion

Abstract Online tracking is a whack-a-mole game between trackers who build and monetize behavioral user profiles through intrusive data collection, and anti-tracking mechanisms that are deployed as browser extensions, DNS resolvers, or built-in to the browser. As a response to pervasive and opaque online tracking, more and more users adopt anti-tracking measures to preserve their privacy. Consequently, as the information that trackers can gather on users is being curbed, some trackers are looking for ways to evade these protections. In this paper we report on a large-scale longitudinal evaluation of an anti-tracking evasion scheme that leverages CNAME records to include tracker resources in a same-site context, which effectively bypasses anti-tracking measures that rely on fixed hostname-based block lists. Using historical HTTP Archive data we find that this tracking scheme is rapidly gaining traction, especially among high-traffic websites. Furthermore, we report on several privacy and security issues inherent to the technical setup of CNAME-based tracking that we detected through a combination of automated and manual analyses. We find that some trackers are using the technique against the Safari browser, which is known to include strict anti-tracking configurations. Our findings show that websites using CNAME trackers must take extra precautions to avoid leaking sensitive information to third parties.
Wheelchair Size and Material Application in Human-machine System Model

Abstract Based on investigation of existing folding wheelchairs’ usage, this paper analyzed wheelchair size and material in human-machine system model. The importance of new environmentally friendly materials’ application on wheelchair component producing in recent years was also explained. On the basis of strength and stiffness theory and calculation, applied materials were determined and rod mechanical calculation were carried out. The calculated results will have certain theoretical significance for wheelchair research and design.
Professional public accreditation of educational programs in the education quality assessment system

This article aims to examine professional public accreditation of educational programs as a means of ensuring that higher education remains relevant to the current socio-economic public needs in the context of shifting requirements of the labour market, digitalization of most areas of life, and new challenges in technical and technological development. The study actualizes new approaches that can be used in assessing the quality of educational programs implemented in the field of environmental management, energy, and environmental protection. A systems approach views professional public accreditation of educational programs as a set of measures. They aim to ensure the compliance of educational programs with the requirements of professional standards and employers’ expectations. An institutional approach enabled to identify the subjective characteristics of the examined phenomenon. We also used methods of problem analysis, comparative analysis, data collection, and statistical processing. The result of the study is the established need to develop professional public accreditation of educational programs in the system of independent assessment of education quality, which would make it possible to estimate the significant characteristics of an educational program and to determine whether its implementation in a specific timespan is necessary. This study can contribute to the development of the specific practices of professional public accreditation of educational programs, including in the field of rational and sustainable use of natural resources, and it can facilitate the development of the legal, theoretical, and organizational foundations of this type of public activity.
Concurrence Percolation in Quantum Networks

Establishing long-distance quantum entanglement, i.e., entanglement transmission, in quantum networks (QN) is a key and timely challenge for developing efficient quantum communication. Traditional comprehension based on classical percolation assumes a necessary condition for successful entanglement transmission between any two infinitely distant nodes: they must be connected by at least a path of perfectly entangled states (singlets). Here, we relax this condition by explicitly showing that one can focus not on optimally converting singlets but on establishing concurrence -- a key measure of bipartite entanglement. We thereby introduce a new statistical theory, concurrence percolation theory (ConPT), remotely analogous to classical percolation but fundamentally different, built by generalizing bond percolation in terms of "sponge-crossing" paths instead of clusters. Inspired by resistance network analysis, we determine the path connectivity by series/parallel rules and approximate higher-order rules via star-mesh transforms. Interestingly, we find that the entanglement transmission threshold predicted by ConPT is lower than the known classical-percolation-based results and is readily achievable on any series-parallel networks such as the Bethe lattice. ConPT promotes our understanding of how well quantum communication can be further systematically improved versus classical statistical predictions under the limitation of QN locality -- a "quantum advantage" that is more general and efficient than expected. ConPT also shows a percolation-like universal critical behavior derived by finite-size analysis on the Bethe lattice and regular two-dimensional lattices, offering new perspectives for a theory of criticality in entanglement statistics.
A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System

Fault detection and identification are vital for guaranteeing the precision and reliability of tightly coupled inertial navigation system (INS)/global navigation satellite system (GNSS)-integrated navigation systems. A variance shift outlier model (VSOM) was employed to detect faults in the raw pseudo-range data in this paper. The measurements were partially excluded or included in the estimation process depending on the size of the associated shift in the variance. As an objective measure, likelihood ratio and score test statistics were used to determine whether the measurements inflated variance and were deemed to be faulty. The VSOM is appealing because the down-weighting of faulty measurements with the proper weighting factors in the analysis automatically becomes part of the estimation procedure instead of deletion. A parametric bootstrap procedure for significance assessment and multiple testing to identify faults in the VSOM is proposed. The results show that VSOM was validated through field tests, and it works well when single or multiple faults exist in GNSS measurements.