Reviewerly/exHarmony huggingface dataset:
College Smart Classroom Attendance Management System Based on Internet of Things

Since entering the information age, educational informatization reform has become the inevitable trend of the development of colleges and universities. The traditional education management methods, especially the classroom attendance methods, not only need to rely on a large number of manpower for data collection and analysis but also cannot dynamically monitor students' attendance and low efficiency. The development of Internet of things technology provides technical support for the informatization reform of education management in colleges and universities and makes the classroom attendance management in colleges and universities have a new development direction. In this study, a college smart classroom attendance management system based on RFID technology and face recognition technology is constructed under the architecture of the Internet of things, and the corresponding simulation experiments are carried out. The experimental results show that the smart classroom attendance management system based on RFID technology can accurately identify the absence and substitution of students and has the advantages of fast response and low cost. However, its recognition is easily affected by obstructions, which requires students to place identification cards uniformly. The smart classroom attendance management system based on face recognition technology can accurately record and identify the situation of students entering and leaving the classroom and identify the situations of being late and leaving early, absenteeism, and substitute classes. The experimental results are basically consistent with the sample results, and the error rate is low. However, the system is easily affected by environmental light, students' sitting posture, expression, and other factors, so it cannot be recognized. Generally speaking, both can meet the needs of classroom attendance in colleges and universities and have high accuracy and efficiency.
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement

Getting rid of the fundamental limitations in fitting to the paired training data, recent unsupervised low-light enhancement methods excel in adjusting illumination and contrast of images. However, for unsupervised low light enhancement, the remaining noise suppression issue due to the lacking of supervision of detailed signal largely impedes the wide deployment of these methods in real-world applications. Herein, we propose a novel Cycle-Interactive Generative Adversarial Network (CIGAN) for unsupervised low-light image enhancement, which is capable of not only better transferring illumination distributions between low/normal-light images but also manipulating detailed signals between two domains, e.g., suppressing/synthesizing realistic noise in the cyclic enhancement/degradation process. In particular, the proposed low-light guided transformation feed-forwards the features of low-light images from the generator of enhancement GAN (eGAN) into the generator of degradation GAN (dGAN). With the learned information of real low-light images, dGAN can synthesize more realistic diverse illumination and contrast in low-light images. Moreover, the feature randomized perturbation module in dGAN learns to increase the feature randomness to produce diverse feature distributions, persuading the synthesized low-light images to contain realistic noise. Extensive experiments demonstrate both the superiority of the proposed method and the effectiveness of each module in CIGAN.
Where Do Queries Come From?

Where do queries -- the words searchers type into a search box -- come from? The Information Retrieval community understands the performance of queries and search engines extensively, and has recently begun to examine the impact of query variation, showing that different queries for the same information need produce different results. In an information environment where bad actors try to nudge searchers toward misinformation, this is worrisome. The source of query variation -- searcher characteristics, contextual or linguistic prompts, cognitive biases, or even the influence of external parties -- while studied in a piecemeal fashion by other research communities has not been studied by ours. In this paper we draw on a variety of literatures (including information seeking, psychology, and misinformation), and report some small experiments to describe what is known about where queries come from, and demonstrate a clear literature gap around the source of query variations in IR. We chart a way forward for IR to research, document and understand this important question, with a view to creating search engines that provide more consistent, accurate and relevant search results regardless of the searcher's framing of the query.
RESETBERT4Rec: A Pre-training Model Integrating Time And User Historical Behavior for Sequential Recommendation

Sequential recommendation methods are very important in modern recommender systems because they can well capture users' dynamic interests from their interaction history, and make accurate recommendations for users, thereby helping enterprises succeed in business. However, despite the great success of existing sequential recommendation-based methods, they focus too much on item-level modeling of users' click history and lack information about the user's entire click history (such as click order, click time, etc.). To tackle this problem, inspired by recent advances in pre-training techniques in the field of natural language processing, we build a new pre-training task based on the original BERT pre-training framework and incorporate temporal information. Specifically, we propose a new model called the RE arrange S equence prE -training and T ime embedding model via BERT for sequential R ecommendation (RESETBERT4Rec ) \footnoteThis work was completed during JD internship., it further captures the information of the user's whole click history by adding a rearrange sequence prediction task to the original BERT pre-training framework, while it integrates different views of time information. Comprehensive experiments on two public datasets as well as one e-commerce dataset demonstrate that RESETBERT4Rec achieves state-of-the-art performance over existing baselines.
Conversational Question Answering on Heterogeneous Sources

Conversational question answering (ConvQA) tackles sequential information needs where contexts in follow-up questions are left implicit. Current ConvQA systems operate over homogeneous sources of information: either a knowledge base (KB), or a text corpus, or a collection of tables. This paper addresses the novel issue of jointly tapping into all of these together, this way boosting answer coverage and confidence. We present CONVINSE, an end-to-end pipeline for ConvQA over heterogeneous sources, operating in three stages: i) learning an explicit structured representation of an incoming question and its conversational context, ii) harnessing this frame-like representation to uniformly capture relevant evidences from KB, text, and tables, and iii) running a fusion-in-decoder model to generate the answer. We construct and release the first benchmark, ConvMix, for ConvQA over heterogeneous sources, comprising 3000 real-user conversations with 16000 questions, along with entity annotations, completed question utterances, and question paraphrases. Experiments demonstrate the viability and advantages of our method, compared to state-of-the-art baselines.
Verification and Validation for data marketplaces via a blockchain and smart contracts

Actual challenges with data in physical infrastructure include: 1) the adversity of its velocity based on access and retrieval, thus integration; 2) its value as its intrinsic quality; 3) its extensive volume with a limited variety in terms of systems; and finally, 4) its veracity, as data can be modified to obtain an economical advantage. Physical infrastructure design based on Agile project management and minimum viable products provides benefits against the traditional waterfall method. Agile supports an early return on investment that promotes circular reinvesting while making the product more adaptable to variable social-economical environments. However, Agile also presents inherent issues due to its iterative approach. Furthermore, project information requires an efficient record of the aims, requirements, and governance not only for the investors, owners, or users but also to keep evidence in future health & safety and other statutory compliance. In order to address these issues, this article presents a Validation and Verification (V&V) model for data marketplaces with a hierarchical process; each data V&V stage provides a layer of data abstraction, value-added services, and authenticity based on Artificial Intelligence (AI). In addition, this proposed solution applies Distributed Ledger Technology (DLT) for a decentralised approach where each user keeps and maintains the data within a ledger. The presented model is validated in real data marketplace applications: 1) live data for the Newcastle Urban Observatory Smart City Project, where data are collected from sensors embedded within the smart city via APIs; 2) static data for University College London (UCL)—Real Estate—PEARL Project, where different project users and stakeholders introduce data into a Project Information Model (PIM).
PharmaChain: A blockchain to ensure counterfeit‐free pharmaceutical supply chain

Globalisation has facilitated different industries to eliminate geographical boundaries and equipped organisations to work collectively to produce goods. Pharmaceuticals is one such industry which has majorly benefited from globalisation to reduce costs and increase profitability at different aspects of research, development, manufacturing of drugs and finally distribution of drugs. Globalisation not only reduces costs and increases profits for the industry, it also makes essential medication available to all individuals even at remote areas of undeveloped, developing and developed countries. As the number of entities or participants grew in the Pharmaceutical Supply Chain (PSC), the complexity and abstractness of the drug delivery from manufacturer to consumer has increased, which raised new concerns like counterfeit medication, incorrect, incomplete or no information about the drug reaching the consumer thus undermining customer confidence and most importantly distribution delays which can cause serious impact on the life of the consumer as well as business growth. Considering all these challenges, there is a dire need for a robust PSC which can eliminate blind parties and provide a transparent chain. A transparent PSC will eliminate data fragmentation between different participating entities by creating a trail of secure, single source of truth for the entire life cycle of a medicine, thereby eliminating the introduction of counterfeits and ensuring the consumer safety. This article proposes a novel Distributed Ledger Technology (DLT) based transparent supply chain for PSC and proof-of-concept is implemented to analyse the scalability and efficiency of the proposed architecture. Distributed Ledger Technology (DLT) is one of the technologies which can provide such transparent PSC and Smart Contracts (SC) are the most commonly used component in such systems to implement business logic and access control mechanisms. The proposed PharmaChain model investigates all the interactions between the main entities in PSC and also addresses smart contract issues such as re-entrance, randomness and lacking trustworthy data feed. Results are compared with other proposed solutions for PSC based on DLT.
Deterministic generation of entanglement in a quantum network by coherent absorption of a single photon

Advanced quantum information protocols rely on the operation of multinodal quantum networks where entanglement is distributed across the nodes. Existing protocols of entanglement generation are probabilistic, with the efficiency dropping exponentially with the size of the system. We formulate an approach for the deterministic generation of entangled states of a multinodal quantum network of arbitrary size by coupling a single photon standing wave with the nodes of the network. We show experimentally how this can be implemented in a simple binodal system. Since this approach relies on collective excitation of the network---not on local interaction with individual nodes---it allows generation of entanglement with unitary efficiency, independent of the size and the nature of the network.
Revisiting Bundle Recommendation

Product bundling is a commonly-used marketing strategy in both offline retailers and online e-commerce systems. Current research on bundle recommendation is limited by: (1) noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session; and (2) specific tasks, holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. In this paper, we propose to take a step back and consider the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich meta information, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion, ranking, to explanation and auto-naming. Finally, we conduct extensive experiments and in-depth analysis that demonstrate the challenges of bundle recommendation, arising from the need for capturing complex relations among users, products, and bundles, as well as the research opportunities, especially in graph-based neural methods. To sum up, our study delivers new data sources, opens up new research directions, and provides useful guidance for product bundling in real e-commerce platforms. Our datasets are available at GitHub (\urlhttps://github.com/BundleRec/bundle_recommendation ).
Activation Energies in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mrow><mml:mi>Mo</mml:mi><mml:mi>Si</mml:mi></mml:mrow><mml:mo>/</mml:mo><mml:mi>Al</mml:mi></mml:math> Superconducting Nanowire Single-Photon Detectors

Superconducting nanowire single-photon detectors (SNSPDs) are receiving the interest of the scientific and industrial communities due to their unparallel high performances in the infrared. However, even though the fabrication process permits the achievement of about 98% efficiency and less than 1 cps dark-count rate, the physical mechanism inducing the detection remains unclear. It is clear however that normal core vortices play a crucial role. In this work we investigate the role of vortices in two-dimensional (2D) molybdenum silicide SNSPDs of different widths covered with an $\mathrm{Al}$ layer through the analysis of the switching current distributions from the superconducting to resistive regime, in a wide interval of temperatures from 4.5 K down to 10 mK. This analysis provides the energy scales of different mechanisms that are responsible for fluctuations and dark counts in SNSPDs. We consider two models based on vortices, the unbinding of vortex-antivortex pairs (VAPs) and vortices hopping over the edge barrier (VH) and we underline the differences among different devices made by different materials. We also estimate the energy scales of similar $\mathrm{Nb}\text{\ensuremath{-}}\mathrm{Ti}\text{\ensuremath{-}}\mathrm{N}$ and $\mathrm{Nb}\mathrm{N}$ devices and compare the results. The lower activation energies obtained for $\mathrm{Mo}\mathrm{Si}/\mathrm{Al}$ devices, explain the peculiarity of this material to work at longer wavelengths with a higher quantum detection efficiency.
A Non-Factoid Question-Answering Taxonomy

Non-factoid question answering (NFQA) is a challenging and under-researched task that requires constructing long-form answers, such as explanations or opinions, to open-ended non-factoid questions - NFQs. There is still little understanding of the categories of NFQs that people tend to ask, what form of answers they expect to see in return, and what the key research challenges of each category are.
A Color Image Encryption Algorithm Based on Double Fractional Order Chaotic Neural Network and Convolution Operation

A color image encryption algorithm based on double fractional order chaotic neural network (CNN), interlaced dynamic deoxyribonucleic acid (DNA) encoding and decoding, zigzag confusion, bidirectional bit-level diffusion and convolution operation is proposed. Firstly, two fractional order chaotic neural networks (CNNs) are proposed to explore the application of fractional order CNN in image encryption. Meanwhile, spectral entropy (SE) algorithm shows that the sequence generated by the proposed fractional order CNNs has better randomness. Secondly, a DNA encoding and decoding encryption scheme with evolutionary characteristics is adopted. In addition, convolution operation is utilized to improve the key sensitivity. Finally, simulation results and security analysis illustrate that the proposed algorithm has high security performance and can withstand classical cryptanalysis attacks.
A Novel and Effective Model to Predict Skip Metastasis in Papillary Thyroid Carcinoma Based on a Support Vector Machine

Skip metastasis, referred to as lymph node metastases to the lateral neck compartment without involvement of the central compartment, is generally unpredictable in papillary thyroid carcinoma (PTC). This study aims to establish an effective predictive model for skip metastasis in PTC.Retrospective analysis was performed of clinical samples from 18192 patients diagnosed with thyroid cancer between 2016 to 2020. The First Affiliated Hospital of Wenzhou Medical University. The lateral lymph node metastasis was occureed in the training set (630 PTC patients) and validation set (189 PTC patients). The univariate and multivariate analyses were performed to detect the predictors of skip metastasis and the support vector machine (SVM) was used to establish a model to predict skip metastasis.The rate of skip metastasis was 13.3% (84/631). Tumor size (≤10 mm), upper location, Hashimoto's thyroiditis, extrathyroidal extension, absence of BRAFV600E mutation, and less number of central lymph node dissection were considered as independent predictors of skip metastasis in PTC. For the training set, these predictors performed with 91.7% accuracy, 86.4% sensitivity, 92.2% specificity, 45.2% positive predictive value (PPV), and 98.9% negative predictive value (NPV) in the model. Meanwhile, these predictors showed 91.5% accuracy,71.4% sensitivity, 93.1% specificity, 45.5% PPV, and 97.6% NPV in validation set.This study screened the predictors of the skip lateral lymph node metastasis and to establish an effective and economic predictive model for skip metastasis in PTC. The model can accurately distinguish the skip metastasis in PTC using a simple and affordable method, which may have potential for daily clinical application in the future.
Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images

Due to the extreme complexity of scale and shape as well as the uncertainty of the predicted location, salient object detection in optical remote sensing images (RSI-SOD) is a very difficult task. The existing SOD methods can satisfy the detection performance for natural scene images, but they are not well adapted to RSI-SOD due to the above-mentioned image characteristics in remote sensing images. In this paper, we propose a novel Attention Guided Network (AGNet) for SOD in optical RSIs, including position enhancement stage and detail refinement stage. Specifically, the position enhancement stage consists of a semantic attention module and a contextual attention module to accurately describe the approximate location of salient objects. The detail refinement stage uses the proposed self-refinement module to progressively refine the predicted results under the guidance of attention and reverse attention. In addition, the hybrid loss is applied to supervise the training of the network, which can improve the performance of the model from three perspectives of pixel, region and statistics. Extensive experiments on two popular benchmarks demonstrate that AGNet achieves competitive performance compared to other state-of-the-art methods. The code will be available at https://github.com/NuaaYH/AGNet.
ReLoop

Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users' dynamic behaviors from newly collected interaction logs. However, the current model training process only acquires users' feedbacks as labels, but fails to take into account the errors made in previous recommendations. Inspired by the intuition that humans usually reflect and learn from mistakes, in this paper, we attempt to build a self-correction continual learning loop (dubbed ReLoop) for recommender systems. In particular, a new customized loss is employed to encourage every new model version to reduce prediction errors over the previous model version during training. Our ReLoop learning framework enables a continual self-correction process in the long run and thus is expected to obtain better performance over existing training strategies. Both offline experiments and an online A/B test have been conducted to validate the effectiveness of ReLoop.
Knowledge-based environment dependency inference for python programs

Besides third-party packages, the Python interpreter and system libraries are also critical dependencies of a Python program. In our empirical study, 34% programs are only compatible with specific Python interpreter versions, and 24% programs require specific system libraries. However, existing techniques mainly focus on inferring third-party package dependencies. Therefore, they can lack other necessary dependencies and violate version constraints, thus resulting in program build failures and runtime errors.
CRET

Given a text query, the text-to-video retrieval task aims to find the relevant videos in the database. Recently, model-based (MDB) methods have demonstrated superior accuracy than embedding-based (EDB) methods due to their excellent capacity of modeling local video/text correspondences, especially when equipped with large-scale pre-training schemes like ClipBERT. Generally speaking, MDB methods take a text-video pair as input and harness deep models to predict the mutual similarity, while EDB methods first utilize modality-specific encoders to extract embeddings for text and video, then evaluate the distance based on the extracted embeddings. Notably, MDB methods cannot produce explicit representations for text and video, instead, they have to exhaustively pair the query with every database item to predict their mutual similarities in the inference stage, which results in significant inefficiency in practical applications.