Reviewerly/exHarmony huggingface dataset:
KT-XL

With the development of artificial intelligence (AI) technology, online teaching systems have become intellectualized. Knowledge tracing is an important task of intelligent teaching system. The goal of knowledge tracing is to trace students' knowledge status based on their past performance. It is widely used for predicting students' performance and building knowledge graph, which plays an important role in constructing adaptive (personalized) teaching systems. Previous models can not deal with subjective problems, and the performance is somewhat poor when input exercise sequences are long. In this paper, we propose a knowledge tracing model for subjective problems based on Transformer-XL, which can predict students' performance of a specific exercise. Specifically, we introduce a recurrence mechanism to the model to capture longer-term dependency and achieve better performance on both short and long sequences. Experiments on multiple real-world data sets and synthetic data sets show that our model performs better in predicting students' performance than state of the art models.
Online Product Recommendation Engine

In this modern world, most of the products are brought through internet. So by using the recommendation technique we can improve our selling of the product. We have been provided with two csv files. The train.csv dataset contains the User ID, Invoice Number, Date & Time, Quantity, Country, Unit Price and Stock Code. By using these available parameters recommend the products to the customers. Previous work has used SVD algorithm or Matrix factorization for doing the recommendations. In this paper we have used Restricted Boltzmann Machine (RBM) to decode the text into a latent vector sequence, especially for end-to-end collaborative filtering on the required task. For the task of recommending a stock id for each customer with the RBM model helps in yielding a model with significantly higher accuracy. Multi-task Learning is just used to improve the efficiency of learning through RBM which will give better predictions based on user and items it had purchased. This helps our model to recommend a stock code for the individual users based on the product they have already purchased. We have achieved a recommendation score of 94.4% to the customers with RBM method.
MOOC and professional skills development: Enhancing public speaking competence with international teams through Google Classroom

The development of professional skills is one of the most important challenges that Higher Education faces these days. In this sense, the ability to speak in public becomes one of these essential competences to promote during university education because it allows professionals to be better prepared to face life and be more competitive in the labor market. This work exposes the designed a MOOC, applying the Canvas Model, to develop the ability to speak in public of students from two universities; The European University of Madrid, in Spain, and Universidad Tecnológica Centroamericana, in Honduras. Using Google Classroom and YouTube audiovisuals, the training was developed taking advantage of self-evaluation and collaborative learning that resulted from observing and giving feedback to other participants. The results of this learning experience revealed that the virtual training program allowed improvements in the performance of the participants, especially in the way of introducing their presentations and the use of non-verbal communication to enhance the message.
U-shaped Feature Extractor Used on Mask R-CNN for Cell Nuclei Image Segmentation

Abstract The cell nuclei segmentation of is a challenging task in microscopy image analysis. The problems of noise, small cell nuclei, and few training data samples in the data set will all affect the effectiveness of the model to varying degrees. This paper presents a new approach to nuclear image segmentation based on convolutional neural networks. Our approach is based on Mask R-CNN with some modification, which combines low-level semantic features for model training. In order to make the model specific to each low-level feature map, the attention mechanism was used to assign weights to each low-level feature map, making the model learning more purposeful. Our method achieves an average precision value of 62.8%, which is 2.7% higher than that of the Mask R-CNN (ResNet50) basic model and 6.3% of the Mask R-CNN (ResNet101) basic model.
JoyDigit NexIoT: An Open IoT Data Platform for Senior Living

The senior care service plays an important role in senior living industry. With the unprecedented increasing of the seniors and the demanding activities of daily living (ADL) service, caregiver's workload and community's operational cost grow dramatically which lead tremendous challenges. From both caregivers and community operators' perspective, we developed an open IoT data platform for senior living named JoyDigit NexIoT to solve above problems. Multidimensional open IoT data from smart home, health care equipment and security monitoring devices are integrated and processed in real-time on the platform to build dynamical profiles for both seniors and communities, and it also provides open data APIs for customized senior living applications. Together with the novel JoyDigit Intelligent Action Adviser (JIAA), the platform outputs the recommended service or management actions to caregivers and operators, which could greatly reduce the caregiver's daily workload and increase community operator's management efficiency. In this paper, we describe JoyDigit NexIoT platform's features, architecture and JIAA, and also present the practical scenarios to be demonstrated.
BIT: A Blockchain Integrated Time Banking System for Community Exchange Economy

Advancements in Artificial Intelligence (AI) and Big Data technologies are reshaping the global economy. The community currency network is receiving more attention through enhancing social ties within a community. As the most popular type of community currency, Time Banking (TB) is a generalized community exchange economy, which uses the time to evaluate each participant's contributions on the same scale rather than any equivalence with the official national currency. TB is a noble idea with the potential to improve the quality of life through prosocial, reciprocally beneficial activities among community members. However, it also brings new concerns about security and trust issues. Inspired by blockchain and smart contract, this paper introduces a Blockchain Integrated Timebanking (BIT) system to secure a decentralized community exchange economy. In BIT system, service providers and recipients can securely exchange effort through a self-executing smart contract without relying on a third-party trust authority. The blockchain network ensures immutability, auditability, and traceability of all data and service transactions recorded on the distributed ledger. A proof-of-concept prototype was implemented and tested on a private Ethereum network. The experimental results verify the feasibility of the proposed BIT to provide decentralized community service exchanges with limited computation overhead and network latency.
CIRCLE 2020

The Joint Conference of the Information Retrieval Communities in Europe (CIRCLE 2020) is the first joint conference of the French, Italian, Spanish, and Swiss information retrieval communities. Although these communities had conceived the CIRCLE conference as a meeting and networking venue, because of the COVID-19 pandemic, they had to make the conference as fully virtual event. Nonetheless, the three days of conference gathered interesting studies and research work on a wide range of topics on information retrieval, such as topic and document modelling, query and ranking refinement, information retrieval in e-government, social media, recommender systems, information retrieval evaluation, indexing and annotation, user profiling and interaction, frameworks and systems, and semantic extraction.
Smart Auctions for Autonomic Ambient Intelligence Systems

The main goal of Ambient Intelligence (AmI) is to support users in their daily activities by satisfying and anticipating their needs. To achieve such goal, AmI systems rely on physical infrastructures made of heterogenous sensing devices which interact in order to exchange information and perform monitoring tasks. In such a scenario, a full achievement of AmI vision would also require the capability of the system to autonomously check the status of the infrastructure and supervise its maintenance. To this aim, in this paper, we extend some previous works in order to allow the self-management of AmI devices enabling them to directly interact with maintenance service providers. In particular, the combination of smart contracts and blockchains enables AmI systems to autonomously communicate with untrusted entities and complete secure transactions without the brokering of a trusted third party. The proposed approach has been adopted to design a sample AmI application capable of managing requests from faulty devices in a Smart home.
Design of Truly Distributed Storage for Large Medical Datasets

The paper presents the architecture design of the unique IT system that combines the advantages of DLT (blockchain) and DHT (peer2peer) technologies for the storage of large medical files, such as MRI scans or genetic data. Medical data is characterized by exceptional confidentiality and at the same time need for fast and reliable access when saving a patient's health or life. Modern tools of cryptography allow us to achieve these effects using a distributed system architecture, which has the additional advantage of being independent of one data controller.
Attention Based Beauty Product Retrieval Using Global and Local Descriptors

Beauty product retrieval has drawn more and more attention for its wide application outlook and enormous economic benefits. However, this task is always challenging due to the variation of products, especially the disturbance of clustered background. In this paper, we first introduce attention mechanism into a global image descriptor, i.e., Maximum Activation of Convolutions (MAC), and propose Attention-based MAC (AMAC). With this enhancement, we can suppress the negative effect of background and highlight the foreground in an unsupervised manner. Then, AMAC and local descriptors are ensembled to complementarily increase the performance. Furthermore, we try to finetune multiple retrieval methods on the different datasets and adopt a query expansion strategy to obtain more improvements. Extensive experiments conducted on a dataset containing more the half million beauty products (Perfect-500K) demonstrate the effectiveness of the proposed method. Finally, our team (USTC-NELSLIP) wins the first place on the leaderboard of the 'AI Meets Beauty'Grand Challenge of ACM Multimedia 2020. The code is available at: https://github.com/gniknoil/Perfect500K-Beauty-Product-Retrieval-Challenge.
NeuLP: An End-to-End Deep-Learning Model for Link Prediction

Graph neural networks (GNNs) have been proved useful for link prediction in online social networks. However, existing GNNs can only adopt shallow architectures as too many layers will lead to over-smoothing and vanishing gradient during training. It causes nodes to have indistinguishable embeddings if locally they have similar structural positions, and this further leads to inaccurate link prediction. In this paper, we propose a unified end-to-end deep learning model, namely Neural Link Prediction (NeuLP), which can integrate the linearity and non-linearity user interactions to overcome the limitation of GNNs. The experimental evaluation demonstrates our model’s significant improvement over several baseline models. Moreover, NeuLP achieves a reliable link prediction given two users’ different types of attributes and it can be applied to other pairwise tasks. We further perform in-depth analyses on the relation between prediction performance and users’ geodesic distance and show that NeuLP still can make accurate link prediction while two users are far apart in the networks.
Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains

Classifier chains is a key technique in multi-label classification, since it allows to consider label dependencies effectively. However, the classifiers are aligned according to a static order of the labels. In the concept of dynamic classifier chains (DCC) the label ordering is chosen for each prediction dynamically depending on the respective instance at hand. We combine this concept with the boosting of extreme gradient boosted trees (XGBoost), an effective and scalable state-of-the-art technique, and incorporate DCC in a fast multi-label extension of XGBoost which we make publicly available. As only positive labels have to be predicted and these are usually only few, the training costs can be further substantially reduced. Moreover, as experiments on eleven datasets show, the length of the chain allows for more control over the usage of previous predictions and hence over the measure one wants to optimize.
A Virtual Wallet Product Recommender System Based on Collaborative Filtering

Nowadays, there are several options when it comes to making use of products that facilitate financial services to people through virtual wallets. A recommender system quickly provides customers with what they are looking for and helps discover new products that they like. In this paper, a recommender system is proposed that can be customized according to the variables implemented by Movii, a company in the Colombian FinTech sector, taking as input transaction records that indicate the frequency of use of each product, which can be understood as ratings of these products. To determine the model that will implement the recommender system that will be deployed, different models are evaluated, such as techniques based on collaborative filtering. In our evaluation, we found that the model that recommends the most popular products is the one that offers the best performance in recommending a product to users. Thus, it is possible to generate some estimated recommendations on the services available by the company, involving users who consume the available services.
Spectrally resolved polarizing interferometer for single-shot line profiling

In this investigation, we describe a spectrally resolved polarizing interferometer to modify the typical spectrally resolved interferometer based on the Fourier method, which has a dead zone caused by the minimum measurable range. By use of a polarization pixelated CMOS camera, which enables us to obtain spatially phase-shifted spectral interferograms, the spectral phase can be extracted with a single image, and the proposed interferometer can obtain a line profile of a specimen at once. By the nature of phase-shifting technique, moreover, the directions of the measured distances can be identified, and the measuring range is extended to twice that of the typical spectrally resolved interferometer. In the experimental results, the measuring range was 104 µm, and the capability to obtain a line profile of a specimen with a single image was confirmed. For 3D surface profiling, lateral scanning was adopted, and the measurement results were compared with the result of the Fourier method.
Multi-source Transfer Learning for Human Activity Recognition in Smart Homes

With the deployment of smart homes, we find that human activity recognition (HAR) is essentially important to many applications, e.g., child/senior care, intelligent information push and exercise promotion. Although it is always better to build HAR model for each smart home to resolve the practical problem that homes have different floorplans or adopted sensors, it is intractable to acquire labeled data for each home due to cost and privacy. We thus propose a method to transfer the HAR model from multiple labeled source homes to the unlabeled target home. Specifically, we first generate transferable representations for the sensors of these homes, based on which we build the HAR model using the data of labeled source homes. Then, we employ the built HAR model into the unlabeled target home. Experiment results on CASAS dataset illustrate that our proposed method outperforms baseline methods in general and also avoids potential negative transfer caused by using only one source home.
LCCSS

Test code maintainability is a common concern in software testing. In order to achieve good maintainability, test methods should be clearly structured, well named, small in size, and, mainly, test code duplication should be avoided. Several strategies exist to avoid test code duplication, such as implicit setup and delegated setup. However, prior to applying these strategies, first it is necessary to identify the duplicate code, which can be a time-consuming task. To address this problem, we automate the identification of duplicate test code through the application of code similarity metrics. We propose a novel similarity metric, called Longest Common Contiguous Start Sub-Sequence (LCCSS), to identify refactoring candidates. LCCSS is a metric used to measure similarity between pairs of tests. The most similar pairs are reported as strong candidates to be refactored through the implicit setup strategy. We also develop a framework, called Róża, that can use different similarity metrics to identify test code duplication. An experiment shows that LCCSS and Simian, a clone detection tool, have both identified pairs of tests to be refactored through the implicit setup strategy with maximum precision in all the eleven standard recall levels. But, unlike Simian, LCCSS does not need to be calibrated for each project.
Enhancing QoE for Viewport-Adaptive 360-Degree Video Streaming: Perception Analysis and Implementation

Viewport-adaptive streaming is now a widely embraced solution for transmission of 360-degree video, where users perceptual quality after their head movement suffers from the temporary exposure of a low-quality area, as well as the related delay in quality refinement. In this article, subjective experiments were conducted to investigate the humans perception of quality refinement in viewport-adaptive 360-degree video streaming, considering the saccadic suppression, foveal vision, and contrast sensitivity of the human visual system. According to the experimental results, a measurement of the Just Noticeable Refinement Delay is proposed to describe the delay when quality refinement is just noticeable. Based on the findings in perception analysis, a real-time adaptive 360-degree video streaming system using the Random Access Reference Frame is proposed, which can dramatically reduce the refinement delay to enhance users quality of experience. Experimental results demonstrated that the proposed system provide better QoE to users compared to the traditional viewport-adaptive 360-degree video streaming system.
Discovery and Enrichment of Knowledges from a Semantic Wiki

Semantic wikis extend wiki platforms with the ability to represent structured information. They allow users to semantically annotate wiki content to enrich it with semantic properties, and then to propose new ways to find, navigate and present wiki content through reasoning. Semantic wikis are used by many communities for a wide variety of purposes, such as organizing domain knowledge. They have had reasonable success as collaborative platforms in the context of social semantic applications. In this paper, we consider a semantic wiki based on IDOMEN ontology. We use the Wiki infrastructure for the ontology maintenance, with pages containing categories that are IDOMEN concepts, typed links as relations and attributes. IDOMEN is used within wiki for supporting page annotation (tag organization), semantic searching, guided editing, and consistency checking. Our work consisted, on the one hand to discover new knowledge from pairing the concepts of domain ontology and the clusters of tags that we got with tags provided by users; On the other hand, it will be the enrichment of ontology by placing the new knowledge discovered previously.
Towards Non-interactive Witness Hiding

Witness hiding proofs require that the verifier cannot find a witness after seeing a proof. The exact round complexity needed for witness hiding proofs has so far remained an open question. In this work, we provide compelling evidence that witness hiding proofs are achievable non-interactively for wide classes of languages. We use non-interactive witness indistinguishable proofs as the basis for all of our protocols. We give four schemes in different settings under different assumptions:
Evaluation of Speech Input Recognition Rate of AR-Based Drawing Application on Operation Monitor for Communication Support During Endoscopic Surgery

In endoscopic surgery, the surgeon and the assistant use both hands to proceed with the surgical operation. Therefore, the excision site cannot be shown by hand from the image inside the body displayed on the endoscopic monitor. Since there is a lack of communication between the surgeon and the assistant, it is necessary to have a system that indicates the excision point and prevents discrepancies between the surgeon and the assistant. Therefore, we developed a communication system that conveys the excision site from the in-vivo image on the endoscope monitor by operating the head movement and speech input without releasing the hand from the surgical instrument. There was a problem in using speech input in a noisy environment, such as an operation site. In order to make the system generate as few errors as possible, it was necessary to use words with high recognition performance and few unintentional behaviors by mistakenly recognized voice commands. In this experiment, the performance of recognition in the operating room environment and the possibility of unintentional operation were evaluated for each syllable number of words. As a result, the high recognition rate was possible with commands of 3 to 7 syllables, and commands with four or fewer syllables may induce unintentional system behavior. Consequently, we proposed to use the words of 5–7 syllables, which were highly recognized and have few wrong recognitions for voice commands.
GHand: A Graph Convolution Network for 3D Hand Pose Estimation

Vision-based 3D hand pose estimation plays an important role in the field of human-computer interaction. In recent years, with the development of convolutional neural networks (CNN), the field of 3D hand pose estimation has made a great progress, but there is still a long way to go before the problem is solved. Although recent studies based on CNN networks have greatly improved the recognition accuracy, they usually only pay attention on the regression ability of the network itself, and ignore the structural information of the hands, thus leads to a low accuracy in contrast. In this paper we proposed a new hand pose estimation network, which can fully learn the structural information of hands through an adaptive graph convolutional neural network. The experiment on the public dataset shows the accuracy of our graph convolution network exceeds the SOTA methods in 3D hand pose estimation.
Construction of quantum states with special properties by projection methods

We use projection methods to construct (global) quantum states with prescribed reduced (marginal) states, and possibly with some special properties such as having specific eigenvalues, having specific rank and extreme von Neumann or Rényi entropy. Using convex analysis, optimization techniques on matrix manifolds, we obtain algorithms to solve the problem. MATLAB programs are written based on these algorithms, and numerical examples are illustrated. The numerical results reveal new patterns leading to new insights and research problems on the topic.