Reviewerly/exHarmony huggingface dataset:
Monitoring System for Elderly Health Care Using Smart Band, Raspberry Pi, and Node-Red

The decline in physical, cognitive, and sensory functions for most elderly people leads to the development of several health issues such as memory loss, a tendency to fall, dizziness, and reduced ability to care for oneself independently. These can be mitigated using devices designed to monitor vital signs and human activity such as a smart band. However, the application of a smart band is characterized by a disadvantage which involves storing user data in the vendor's cloud and only one device can access the client applications. Therefore, nurses, doctors, and family members cannot simultaneously analyze data and receive alerts when abnormal conditions occur in elderly patients. This study aims to develop a system capable of monitoring the health conditions of multiple elderly individuals and sending alerts during abnormal conditions based on the heart rate value read by the smart band sensor. The process involved installing a database on the Raspberry Pi to store data while a monitoring system was built on the Node-RED platform to display elderly health data and made it accessible locally. The detection of an abnormal heart rate condition in the elderly patient leads the system to send notifications via telegram to multiple receivers registered in the system using their telegram identity.
An Experience of Using the GQM Approach in a Remote Environment to Define Requirements Metrics

It is well known that many problems in software development arise because the software requirements area is neglected. In this way, the quality of requirements is essential to guarantee the acceptance of the software and, thus, the success of the project. Software metrics can be used to assess the quality of products and artifacts. The literature has several metrics for the requirements area. However, to better meet the particularities of a project, the ideal is to define specific metrics. The Goal-Question-Metric (GQM) is an approach for defining metrics oriented to the organization’s goals. This paper aims to present an experience report about applying the GQM approach to define requirements metrics in a software project in the industry. Since we are going through a pandemic, we adopted GQM in the context of remote work. This paper presents the adaptations that were made, as well as the lessons learned about the application of GQM in a remote environment.
Performance Analysis of High Availability Video Conference Service with Kubernetes Cluster Across Data Center

Due to increasing number of users and network traffics, video conference services can experience downtime in case of overloading request to video conference servers. To avoid this, an infrastructure that can replicate itself is for mitigating the risk of service downtime. The infrastructure is built using a container orchestration tool called Kubernetes that runs on Digital Ocean and Linode which functions as Cloud Services. This paper discusses the design of a Kubernetes cluster in which there are two Data Centers originating from two different countries. The Data Center can accommodate several Kubernetes nodes where each node accommodates one pod that can run one application through an intermediary service. In addition, nodes are equipped with the auto-scaling feature, i.e., ability to replicate pods and resize computational resources automatically, which can increase the high availability of the infrastructure and mitigate the risk of downtime. As for the experiment, The WebRTC service is observed by adjusting several parameters, which are QoS (delay, jitter, packet loss), CPU and memory usages, and the number of pods. The experimental results show that the number of pods before the existence of HPA was one while after the existence of HPA there were four pods. The highest number of errors before the HPA was 490 responses/sec, while after the HPA there were 11 responses/sec.
Information technologies available to historians of science

Historians of science, like other scientists whose results of scientific activity can be described in the language of mathematics and modeling, began to use computers in their professional activities almost immediately after their appearance. In Russia, one of the centers where IT technologies began to be used in solving various historical and scientific problems was the Institute for the History of Science and Technology of the Russian Academy of Sciences.
Text-enhanced Multi-Granularity Temporal Graph Learning for Event Prediction

When working with forecasting the future, it is all about learning from the past. However, it is non-trivial to model the past due to the scale and complexity of available data. Recently, Graph Neural Networks (GNNs) have shown flexibility to process different forms of data and learn interactions among entities, giving them advantages in real-life applications. More and more researchers have started to apply GNNs and temporal models for event forecasting because events are formalized in knowledge graphs. However, most of these models are based on the Markov assumption that the probability of a event is only influenced by the state of its last time step (or recent history). We claim that the occurrence of an event not only has short-term but also long-term dependencies. In this work, we propose a temporal knowledge graph (KG)-based model that considers different granularties of histories when forecasting an event; this method also integrates news texts as auxiliary features during the graph learning process. Extensive experiments on multiple datasets are conducted to examine the effectiveness of the proposed method. Code is available at: https://github.com/yuening-lab/MTG.
A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning

Cross-domain recommendation (CDR) methods are proposed to tackle the sparsity problem in click through rate (CTR) estimation. Existing CDR methods directly transfer knowledge from the source domains to the target domain and ignore the heterogeneities among domains, including feature dimensional heterogeneity and latent space heterogeneity, which may lead to negative transfer. Besides, most of the existing methods are based on single-source transfer, which cannot simultaneously utilize knowledge from multiple source domains to further improve the model performance in the target domain. In this paper, we propose a centralized-distributed transfer model (CDTM) for CDR based on multi-source heterogeneous transfer learning. To address the issue of feature dimension heterogeneity, we build a dual embedding structure: domain specific embedding (DSE) and global shared embedding (GSE) to model the feature representation in the single domain and the commonalities in the global space, separately. To solve the latent space heterogeneity, the transfer matrix and attention mechanism are used to map and combine DSE and GSE adaptively. Extensive offline and online experiments demonstrate the effectiveness of our model.
Contrastive Code-Comment Pre-training

Pre-trained models for Natural Languages (NL) have been recently shown to transfer well to Programming Languages (PL) and largely benefit different intelligence code-related tasks, such as code search, clone detection, programming translation and code document generation. However, existing pre-trained methods for programming languages are mainly conducted by masked language modeling and next sentence prediction at token or graph levels. This restricted form limits their performance and transferability since PL and NL have different syntax rules and the downstream tasks require a multimodal representation. Here we introduce C3P, a Contrastive Code-Comment Pre-training approach, to solve various downstream tasks by pre-training the multi-representation features on both programming and natural syntax. The model encodes the code syntax and natural language description (comment) by two encoders and the encoded embeddings are projected into a multi-modal space for learning the latent representation. In the latent space, C3P jointly trains the code and comment encoders by the symmetric loss function, which aims to maximize the cosine similarity of the correct code-comment pairs while minimizing the similarity of unrelated pairs. We verify the empirical performance of the proposed pre-trained models on multiple downstream code-related tasks. The comprehensive experiments demonstrate that C3P outperforms previous work on the understanding tasks of code search and code clone, as well as the generation tasks of programming translation and document generation. Furthermore, we validate the transferability of C3P to the new programming language which is not seen in the pre-training stage. The results show our model surpasses all supervised methods and in some programming language cases even outperforms prior pre-trained approaches. Code is available at https://github.com/TerryPei/C3P.
Towards High-Order Complementary Recommendation via Logical Reasoning Network

Complementary recommendation gains increasing attention in e-commerce since it expedites the process of finding frequently-bought-with products for users in their shopping journey. Therefore, learning the product representation that can reflect this complementary relationship plays a central role in modern recommender systems. In this work, we propose a logical reasoning network, LOGIREC, to effectively learn embeddings of products as well as various transformations (projection, intersection, negation) between them. LOGIREC is capable of capturing the asymmetric complementary relationship between products and seamlessly extending to high-order recommendations where more comprehensive and meaningful complementary relationship is learned for a query set of products. Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation. We demonstrate the effectiveness of our LOGIREC on multiple public real-world datasets in terms of various ranking-based metrics under both low-order and high-order recommendation scenarios.
A Convex Formulation for Graph Convolutional Training: Two Layer Case

Graph convolutional networks (GCNs) are popular neural network models for relational graph data and have inspired de facto models for learning on relational datasets with high-dimensional vertex features. GCNs have also been successfully applied for data mining applications. Though GCNs have been studied from the lens of expressivity and generalisability, their theoretical and especially optimisation properties are less well understood. Understanding the theoretical properties is a great challenge because of the highly non-convex training and non-linear structure of GCNs. In this paper, we provide the first steps towards understanding the optimisation properties of GCNs by introducing a convex program that globally solves the training problem for GCNs and other closely related neural models with two layers equipped with rectified linear unit (ReLU) activations.
Bayesian Deep Structured Semantic Model for Sub-Linear-Time Information Retrieval

We present a Bayesian deep structured semantic model (BDSSM) that is efficient in retrieval tasks with a large pool of candidates for real-time applications, e.g., in search engines and digital ads. Efficiency is achieved by indexing the items into groups based on sparse representation during offline pre-computation. In online retrieval, the algorithm only retrieves and ranks items from indices that are relevant to the query. We then explore optimization strategies in the algorithm to make sparse representations sparser. Finally, our algorithm is compared with other popular clustering-based, hashing-based, and tree-based retrieval methods. We measure the differences in multiple dimensions, including retrieval recall, storage of embeddings, and CPU time. We show that our approach outperforms other algorithms in both recall and CPU time with the same storage limit.
MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation

Graph neural networks have been widely used in recent recommender systems, where negative sampling plays an important role. Existing negative sampling methods restrict the relationship between nodes as either hard positive pairs or hard negative pairs. This leads to the loss of structural information, and lacks the mechanism to generate positive pairs for nodes with few neighbors. To overcome limitations, we propose a novel soft link-based sampling method, namely MixDec Sampling, which consists of Mixup Sampling module and Decay Sampling module. The Mixup Sampling augments node features by synthesizing new nodes and soft links, which provides sufficient number of samples for nodes with few neighbors. The Decay Sampling strengthens the digestion of graph structure information by generating soft links for node embedding learning. To the best of our knowledge, we are the first to model sampling relationships between nodes by soft links in GNN-based recommender systems. Extensive experiments demonstrate that the proposed MixDec Sampling can significantly and consistently improve the recommendation performance of several representative GNN-based models on various recommendation benchmarks.
Rethinking Symmetric Matrix Factorization: A More General and Better Clustering Perspective

Nonnegative matrix factorization (NMF) is widely used for clustering with strong interpretability. Among general NMF problems, symmetric NMF is a special one that plays an important role in graph clustering where each element measures the similarity between data points. Most existing symmetric NMF algorithms require factor matrices to be nonnegative, and only focus on minimizing the gap between similarity matrix and its approximation for clustering, without giving a consideration to other potential regularization terms which can yield better clustering. In this paper, we explore factorizing a symmetric matrix that does not have to be nonnegative, presenting an efficient factorization algorithm with a regularization term to boost the clustering performance. Moreover, a more general framework is proposed to solve symmetric matrix factorization problems with different constraints on the factor matrices.
A Fuzzy Multi-Identity Based Signature

Identity based digital signature is an important research topic of public key cryptography, which can effectively guarantee the authentication, integrity and unforgeability of data. In this paper, a new fuzzy multi-identity based signature scheme is proposed. It is proved that the scheme is existentially unforgeable against adaptively chosen message attack, and the security of the signature scheme can be reduced to CDH assumption. The storage cost and the communication overhead are small, therefore the new fuzzy multi-identity based signature (FMIBS) scheme can be implemented efficiently.
Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment

Entity alignment aims to discover unique equivalent entity pairs with the same meaning across different knowledge graphs (KGs). Existing models have focused on projecting KGs into a latent embedding space so that inherent semantics between entities can be captured for entity alignment. However, the adverse impacts of alignment conflicts have been largely overlooked during training, thereby limiting the entity alignment performance. To address this issue, we propose a novel Conflict-aware Pseudo Labeling via Optimal Transport model (CPL-OT) for entity alignment. The key idea is to iteratively pseudo-label alignment pairs empowered with conflict-aware optimal transport (OT) modeling to boost the precision of entity alignment. CPL-OT is composed of two key components—entity embedding learning with global-local aggregation and iterative conflict-aware pseudo labeling—that mutually reinforce each other. To mitigate alignment conflicts during pseudo labeling, we propose to use optimal transport as an effective means to warrant one-to-one entity alignment between two KGs with the minimal overall transport cost. Extensive experiments on benchmark datasets validate the superiority of CPL-OT over state-of-the-art baselines under both settings with and without prior alignment seeds.
Learning Representations from Local to Global for Fine-grained Patient Similarity Measuring in Intensive Care Unit

Patient similarity measurement is an essential step in discovering clinically meaningful subgroups and building case retrieval systems. Most existing studies implement this procedure using similarity measurement algorithms on the multivariate clinical time-series (input space) or the low-dimensional patient representation (representation space) learned by a representation learning model. However, they either suffer from the adverse effects of irrelevant variables in the data or fail to assess the fine-grained similarity underneath the disease progress. In this paper, we propose a method to measure more fine-grained patient similarity in the state space, where each patient is represented by a series of state representations that reveal the dynamic health status. We discuss three desiderata, including stability, personality, and interpretability, for the state representations, and on this basis, develop a supervised predictive model that learns good state representations for identifying similar patients and predicting patient outcomes. Experimental results on the publicly available dataset MIMIC-III show that our method offers a promising direction for precisely identifying similar patients at the state trajectory level, as well as accurately predicting outcomes.
Voice-Assistant Liveness Analysis

Deep learning is becoming a mainstream technology for speech recognition as well as face recognition at an industrial scale. The ability of devices to respond to spoken commands is basically speech recognition. The main objective of building a voice assistant is using semantic data sources available on the web providing knowledge to the users from the knowledge database. For the security purpose of the voice-triggered device, liveness analysis is required. The objective of this paper is to prevent spoofing attacks on voice assistant devices by introducing a liveness analysis of genuine faces. Different classification algorithms are used for face recognition purposes. Finally, the performance analysis of different classification models is made.
Comparison of Resampling Techniques for Imbalanced Datasets in Student Dropout Prediction

One of the challenges in the Student Dropout Prediction (SDP) problem is imbalanced data, which reduces the efficiency of the Machine Learning (ML) classifier when predicting dropout students. The disproportionate distribution of samples between the majority class (more samples) and the minority class (fewer samples) causes the class imbalance problem, which is a significant challenge in classification problems. When a dataset is highly imbalanced, the ML classifiers give high accuracy as they learn mostly from the majority class. Hence, the accuracy may not always give correct insight about the trained model. In this paper, the findings of the study of several resampling techniques for handling imbalanced data at the data preprocessing level are presented. The Machine learning algorithms, viz. Logistic Regression and Support Vector Machine (SVM), over different performance evaluation metrics for binary classification problems, have been used in the present study to predict the minority class. It is found that the Area Under Curve (AUC) score gives the most reliable result amongst the other considered metrics for predicting the minority class, i.e., the dropout rate of the students.
Mizo Visual Genome 1.0 : A Dataset for English-Mizo Multimodal Neural Machine Translation

Multimodal machine translation (MMT) handles extracting information from several modalities, considering the presumption that the extra modalities will include beneficial alternative perspectives of the input data. Regardless of its significant benefits, it is challenging to implement an MMT system for several languages, mainly due to the scarcity of the availability of multimodal datasets. As for the low-resource English-Mizo pair, the standard multimodal corpus is not available. Therefore, in this paper, we have developed a Mizo Visual Genome 1.0 (MVG 1.0) dataset for English-Mizo MMT, including images with corresponding bilingual textual descriptions. According to automated assessment measures, the performance of multimodal neural machine translation (MNMT) is better than text-only neural machine translation. To the best of our knowledge, our English-Mizo MMT system is the pioneering work in this approach, and as such, it can serve as a baseline for future study in MMT for the low-resource English-Mizo language pair.
IoT-based Human Activity Recognition Models based on CNN, LSTM and GRU

Smartphones’ ability to generate data with their inbuilt sensors has made them used for Human Activity Recognition. The work highlights the importance of Human Activity Recognition (HAR) systems capable of sensing human activities like the inertial motion of a human body. The sensors are worn on a body part and tracked from whole-body motions and monitoring. Real-time signal processing is used to sense human body movements using wearable sensors. The work aims to provide opportunities for promising health applications using IoT. There are many challenges to recognising human activities, including accuracy. This work analyses Human Activity recognition concerning CNN, LSTM, and GRU deep learning models to improve the accuracy of the human activity recognition in the UCI-HAR and WISDM datasets. The comparative analysis shows promising results for Human activity recognition.
Applying Tuple Migration to Preserve Privacy in Databases

Nowadays, in the age of big data, cloud computing, and the internet of things, there has been the ubiquity of open data for people to conveniently approach, use, and mine. However, most of the valuable data is data relating to personal sensitive information such as data about diseases or salaries. This data should be protected by some policies to conceal the relationship between sensitive data and people. As of late, there are many approaches and techniques for preserving data privacy, of which k-anonymity is the most popular. Nevertheless, most of the k-anonymity algorithms are too general and do not concentrate on any concrete data mining technique, so the data utility does not remain high. In this work, we introduce a k-anonymity algorithm based on tuple member migration between tuple groups to achieve k-anonymity while preserving data quality for a data mining algorithm, i.e., association rule mining because it is one of the most popular data mining techniques which discovers the association of items or itemsets in a dataset. The algorithm was evaluated on the adult dataset to assess the performance as well as the data utility.
Pushup Counting and Evaluating Based on Human Keypoint Detection

In recent years, the fitness industry has undergone significant expansion, with a significant percentage of the population adamantly refusing to give up their weekly training sessions. The growth of this industry has been accompanied by a rise in the services given by gyms and sports organisations, with user-centric planning and management systems for more technological sports activities. The mix of fitness and technology was already a burgeoning phenomenon, which has expanded further as a result of COVID-19. During the quarantine, though, we may only exercise at home. This project serves as your personal trainer for at-home workouts. Using Deep Learning and Signal Processing, it will assist you in doing correct push-ups.