Reviewerly/exHarmony huggingface dataset:
Learning What and Where to Learn: A New Perspective on Self-supervised Learning

Self-supervised learning (SSL) has demonstrated its power in generalized model acquisition by leveraging the discriminative semantic and explicit positional information of unlabeled datasets. Unfortunately, mainstream contrastive learning-based methods excessive focus on semantic information and ignore the position is also the carrier of image content, resulting in inadequate data utilization and extensive computational consumption. To address these issues, we present an efficient SSL framework, learning What and Where to learn (W <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SSL), to aggregate semantic and position features. Concretely, we devise a spatially-coupled sampling manner to process images through pre-defined rules, which integrates the advantage of semantic (What) and positional (Where) features into framework to enrich the diversity of feature representation capabilities and improve data utilization. Besides, a spectrum of latent vectors is obtained by mapping the positional features, which implicitly explores the relationship between these vectors. Whereafter, the corresponding discriminative and contrastive optimization objectives are seamlessly embedded in the framework via a cascade paradigm to explore semantic and positional features. The proposed W <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SSL is verified on different types of datasets, which demonstrates that it still outperforms state-of-the-art SSL methods even with half the computational consumption. Code will be available at https://github.com/WilyZhao8/W2SSL.
Mitigating Cyber-Risks from the internal Software Supply Chain of InnerSource

This talk describes the issue of possible cybersecurity risks associated with the supply chain of InnerSource software and potential mitigation strategies as companies are trying to ensure that they are not exposed to any unnecessary risk due to adopting InnerSource.
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations. Many state-of-the-art (SOTA) methods fuse different sources of information (user, item, knowledge graph, tags, etc.) into a graph and use Graph Neural Networks (GNNs) to introduce the auxiliary information through the message passing paradigm. In this work, we seek an alternative framework that is light and effective through self-supervised learning across different sources of information, particularly for the commonly accessible item tag information. We use a self-supervision signal to pair users with the auxiliary information (tags) associated with the items they have interacted with before. To achieve the pairing, we create a proxy training task. For a given item, the model predicts which is the correct pairing between the representations obtained from the users that have interacted with this item and the tags assigned to it. This design provides an efficient solution, using the auxiliary information directly to enhance the quality of user and item embeddings. User behavior in recommendation systems is driven by the complex interactions of many factors behind the users’ decision-making processes. To make the pairing process more fine-grained and avoid embedding collapse, we propose a user intent-aware self-supervised pairing process where we split the user embeddings into multiple sub-embedding vectors. Each sub-embedding vector captures a specific user intent via self-supervised alignment with a particular cluster of tags. We integrate our designed framework with various recommendation models, demonstrating its flexibility and compatibility. Through comparison with numerous SOTA methods on seven real-world datasets, we show that our method can achieve better performance while requiring less training time. This indicates the potential of applying our approach on web-scale datasets.
Efficient Sink-Reachability Analysis via Graph Reduction (Extended Abstract)

We study a variation of the elementary graph reachability problem, called the sink-reachability problem, which can be found in many applications such as static program analysis, social network analysis, large scale web graph analysis, XML document link path analysis, and the study of gene regulation relationships. To scale sink-reachablity analysis to large graphs, we develop a highly scalable sink-reachability preserving graph reduction strategy for input sink graphs, by using a composition framework. That is, individual sink-reachability preserving condensation operators, each running in linear time, are pipelined together to produce graph reduction algorithms that result in close to maximum reduction, while keeping the computation efficient. Experiments on large real-world sink graphs demonstrate that our compositional approach achieves a reduction rate of up to 99.74% for vertices and a rate of up to 99.46% for edges.
Collaborative List-and-Pairwise Filtering from Implicit Feedback : Extended Abstract

Collaborative filtering (CF) from implicit datasets has attracted much attention in recent years. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proven to be helpful to exploit implicit feedback, but lead to not addressing the rank-biased scenarios where positive items are supposed to be placed on the top-k positions. Although there exist listwise methods, they have low efficiency and are not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely Collaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we first smooth a well-known rank-biased measure called Mean Average Precision (MAP) as a low-bound version to make it can be optimized. After that, we combined the objective functions of optimizing the MAP with pairwise comparisons. The CLAPF framework is a new hybrid model that provides the idea of utilizing a listwise measure in a pairwise way on implicit feedback.
Comparison of Multiple Models of Recommendation Systems

In patients’ medical service consumption behavior, patients’ choice of medical institution is an important link, which determines patients’ medical quality and medical cost, and even further affects the distribution of medical resources in the whole health service market. Patients may have problems such as high knowledge barrier and information redundancy in the process of choosing hospitals. Nowadays, with the continuous development of machine learning, the recommendation system using graph neural network has achieved good results in solving this kind of information overload problem. Therefore, we mainly focus on the application of the recommendation system in the process of patients choosing hospitals. Here we complete the construction of the initial data set through data simulation, and then we train and debug the six graph neural network recommendation system models. In addition, we propose a new comprehensive index to improve the traditional index, which is difficult to better represent the model performance. In the future, we plan to apply this research to our smart medical big data cloud platform. On the one hand, the cloud platform will provide a more solid data basis for our model; on the other hand, we can provide personalized medical recommendation services for platform users by using the recommendation system.
COLLECTION MANAGEMENT IN THE CLOUD: A GUIDE FOR USING CLOUD COMPUTING TECHNOLOGIES IN LIBRARIES

"COLLECTION MANAGEMENT IN THE CLOUD: A GUIDE FOR USING CLOUD COMPUTING TECHNOLOGIES IN LIBRARIES." Technical Services Quarterly, 40(3), pp. 242–243
Comparative Analysis of Nonlinear Programming Solvers: Performance Evaluation, Benchmarking, and Multi-UAV Optimal Path Planning

In this paper, we propose a set of guidelines to select a solver for the solution of nonlinear programming problems. We conduct a comparative analysis of the convergence performances of commonly used solvers for both unconstrained and constrained nonlinear programming problems. The comparison metrics involve accuracy, convergence rate, and computational time. MATLAB is chosen as the implementation platform due to its widespread adoption in academia and industry. Our study includes solvers which are either freely available or require a license, or are extensively documented in the literature. Moreover, we differentiate solvers if they allow the selection of different optimal search methods. We assess the performance of 24 algorithms on a set of 60 benchmark problems. We also evaluate the capability of each solver to tackle two large-scale UAV optimal path planning scenarios, specifically the 3D minimum time problem for UAV landing and the 3D minimum time problem for UAV formation flying. To enrich our analysis, we discuss the effects of each solver’s inner settings on accuracy, convergence rate, and computational time.
Multi criteria optimize secure VM placement strategy against cross VM attack

Cloud computing is becoming more and more popular, but it also presents several security challenges. VMs are generally rented out on demand to users on large scale. Cloud service providers have numerous issues as a result of the increasing user demand for lowcost and high-performance cloud computing, both in terms of security and resource. One of these difficulties is the “co-residence Cross VM attack,” in which attackers use the shared cloud’s resource pooling to launch various attacks against their co-residents on shared physical machine (PM). Co-resident attacks, in which malicious users create side channels and steal sensitive data from VMs, are often used by attackers. The goal is to assure secure VM placement while optimizing physical resource in cloud data centers. In this study, we concentrate on safe virtual machine placement techniques that provide security against the co-location-based Cross VM attacks. To find the optimum allocation technique, we further apply the Particle swarm optimization (PSO algorithm motivated by particle swarm and their behavior. The proposed approach may help to make the cloud energy-efficient and secure, according to the findings of the experiments.
Blockchain technology combined with the CNN and Hashing algorithms enabled the secure storage of 3D biometric face and ear data

The integration of biometrics and blockchain is proposed as a sophisticated and enduring security strategy to protect sensitive information in the digital era. 3D biometric data will not be affected by changes in posture and changes of illumination. This research proposed a security model for 3D multimodal (face and ear). To secure the 3D data uses the convolutional neural networks (CNNs) for 3D image feature extraction based on fusion of 3D face and ear data. Further compare the different hashing algorithms for the security of 3D multimodal Biometric model in Blockchain. This research also compares the different hashing algorithm efficiency and impact of blockchain mechanism on 3D face and ear data.
Validation: Syntax, Semantics, and Pragmatics

Validation is about preconditions for operation. It may be useful to separate preconditions into three subtypes: syntax, semantics, and pragmatics.1 Syntax: Rules about what’s grammatically well-formed. Example: A CalculateAqueousStability command may have a set of atomic-composition pairs and a set of ion-concentration pairs. An atomic-composition pair is a string paired with a number between 0 and 1. An ion-concentration pair is a string paired with a number. Semantics: Rules about...
Numbered Heads Together based on Contextual Teaching and Learning to Improve Students’ Reading Comprehension

This research aims to find out the significance difference in the reading comprehension of students taught through original NHT and through modified NHT based on CTL. This is a quasi-experimental research design that conducts a quantitative method. The subjects of this research are two classes of SMPN 30 Bandar Lampung in. Furthermore, the researcher collected the data using reading test in form of multiple choices. Then, the researcher analyzed using independent sample t-test. The finding shows that there is a significant difference between experimental and control class with the significant level 0.015. Hence, this suggests that teaching reading using modified NHT based on CTL can improve students reading comprehension achievement.
K-NN with Frequency Domain Features for Identify Fingers Movement

Prosthetic hands, which make daily chores more accessible, are one of the improvements brought about by quick technology advancements. The study and use of technological, therapeutic, and diagnostic principles concerning human activity are known as biomechanics, and it has led to the development of
Advancing Robot Autonomy for Long-Horizon Tasks

Autonomous robots have real-world applications in diverse fields, such as mobile manipulation and environmental exploration, and many such tasks benefit from a hands-off approach in terms of human user involvement over a long task horizon. However, the level of autonomy achievable by a deployment is limited in part by the problem definition or task specification required by the system. Task specifications often require technical, low-level information that is unintuitive to describe and may result in generic solutions, burdening the user technically both before and after task completion. In this thesis, we aim to advance task specification abstraction toward the goal of increasing robot autonomy in real-world scenarios. We do so by tackling problems that address several different angles of this goal. First, we develop a way for the automatic discovery of optimal transition points between subtasks in the context of constrained mobile manipulation, removing the need for the human to hand-specify these in the task specification. We further propose a way to automatically describe constraints on robot motion by using demonstrated data as opposed to manually-defined constraints. Then, within the context of environmental exploration, we propose a flexible task specification framework, requiring just a set of quantiles of interest from the user that allows the robot to directly suggest locations in the environment for the user to study. We next systematically study the effect of including a robot team in the task specification and show that multirobot teams have the ability to improve performance under certain specification conditions, including enabling inter-robot communication. Finally, we propose methods for a communication protocol that autonomously selects useful but limited information to share with the other robots.
Embedding Models for Supervised Automatic Extraction and Classification of Named Entities in Scientific Acknowledgements

Acknowledgments in scientific papers may give an insight into aspects of the scientific community, such as reward systems, collaboration patterns, and hidden research trends. The aim of the paper is to evaluate the performance of different embedding models for the task of automatic extraction and classification of acknowledged entities from the acknowledgment text in scientific papers. We trained and implemented a named entity recognition (NER) task using the Flair NLP framework. The training was conducted using three default Flair NER models with four differently-sized corpora and different versions of the Flair NLP framework. The Flair Embeddings model trained on the medium corpus with the latest FLAIR version showed the best accuracy of 0.79. Expanding the size of a training corpus from very small to medium size massively increased the accuracy of all training algorithms, but further expansion of the training corpus did not bring further improvement. Moreover, the performance of the model slightly deteriorated. Our model is able to recognize six entity types: funding agency, grant number, individuals, university, corporation, and miscellaneous. The model works more precisely for some entity types than for others; thus, individuals and grant numbers showed a very good F1-Score over 0.9. Most of the previous works on acknowledgment analysis were limited by the manual evaluation of data and therefore by the amount of processed data. This model can be applied for the comprehensive analysis of acknowledgment texts and may potentially make a great contribution to the field of automated acknowledgment analysis.
ESTRESSE ENTRE PROFISSIONAIS DE SAÚDE IDOSOS/AS DURANTE A PANDEMIA DA COVID-19: UMA REVISÃO INTEGRATIVA DA LITERATURA

The present research aims to analyze publications on work stress among healthcare professionals, with emphasis on elderly professionals in the midst of the COVID-19 pandemic. Health professionals face psychological and physical pressure through continuous exposure to COVID-19, resulting in emotional suffering, overload of service and fear of contaminating themselves and their families, facing a situation that requires more attention and specific care. Thus, part of this team corresponds to elderly people emerged in stressful situations, whether in primary, secondary or tertiary care in the front line against COVID-19. Therefore, an integrative literature review was carried out, through scientific articles in Portuguese and English, searched in the Pubmed, PsycINFO, LILACS and BVS-PSI Psychology databases. The 169 scientific studies found directly in the databases were organized by the EndNote Web reference manager, and only 11 were selected based on the eligibility criteria. The results of surveys conducted in several countries showed that women had higher levels of stress than men and stress rates ranged from mild to moderate. In this context, professionals with children under 18 years old, married and with less time of experience presented higher levels of stress. It is expected that this study contributes to the promotion of actions to promote and care for the health of health workers, especially the elderly, who need more attention in the current pandemic context.
Open Set Domain Adaptation with Zero-shot Learning on Graph

Open set domain adaptation focuses on transferring the information from a richly labeled domain called source domain to a scarcely labeled domain called target domain, while classifying the unseen target samples as one unknown class in an unsupervised way. Compared with the close set domain adaptation, where the source domain and the target domain share the same class space, the classification of the unknown class makes it easy to adapt to the real environment. Particularly, after the recognition of the unknown samples, the model can either ask for manually labeling or further develop the classification ability of the unknown classes based on pre-stored knowledge. Inspired by this idea, we propose a model for open set domain adaptation with zero-shot learning on the unknown classes in this paper. We utilize adversarial learning to align the two domains while rejecting the unknown classes. Then the knowledge graph is introduced to generate the classifiers for the unknown classes with the employment of the graph convolution network (GCN). Thus the classification ability of the source domain is transferred to the target domain, and the model can distinguish the unknown classes in detail with prior knowledge. We evaluate our model on digits datasets and the result shows superior performance.
A Multi-Head Attention Based Dual Target Graph Collaborative Filtering Network

Recently, cross-domain collaborative filtering (CDCF) has been widely used to solve the data sparsity problem in recommendation systems. Therein, the dual-target cross-domain recommendation becomes a research hotspot, which aims to improve the recommendation performance of both target and source domains. Most existing approaches tend to use fixed weights or self-attention in a single representation space for the bi-directional inter-domain transfer of the user representation. However, a single representation space leads to limited representation capability, which makes the transfer of the user representation coarse-grained and inaccurate. In this paper, Multi-head Attention Based Dual Target Graph Collaborative Filtering Network (MA-DTGCF) is proposed. The core of the model is the bi-directional transfer graph convolution layer, consisting of a graph convolution layer and a bi-directional transfer layer based on a multi-head attention mechanism. The latter can achieve fine-grained and adaptive transfer of user features in multiple representation subspaces. It is worth noting that by stacking multiple bi-directional transfer graph convolutional layers, we can get high-order user and item features and achieve adaptive transfer of each order user features. Experimental results on three real datasets show that the proposed MA-DTGCF model significantly outperforms the state-of-the-art models in terms of HR and NDCG.
Genetic Algorithm-Based Task Assignment for Fleet of Unmanned Surface Vehicles in Dynamically Changing Environment

Unmanned vehicles are gaining the attention of professional operators and the general public. The implementation of unmanned vehicles is evident in, among other fields, emergency management, agriculture, traffic monitoring, post-disaster operations, and delivery of goods. Naturally, a group of unmanned vehicles can cooperatively complete operations more proficiently than a single vehicle. However, several issues must be resolved before a stable and reliable group of unmanned vehicles can be generally deployed to solve tasks in civil infrastructures and in industrial facilities. Here, a framework for the guidance of a fleet of unmanned surface vehicles is proposed. The framework utilizes several levels of control, namely Global Planning Level, Local Planning Level, and Low-Level Control. While the individual vehicles are completely autonomous in their operational locomotion and obstacle avoidance (low-level control and local planning), the task assignment for each vehicle (or group of them) is provided by a global planning process, based on the genetic algorithm. The framework provides a concept to solve complex tasks for the fleet of unmanned surface vehicles (USVs). This includes, but is not necessarily limited to, a dynamically changing environment, different types of USVs with special abilities, multiple types of areal restrictions and obstacles, different restrictions for individual USVs, cooperation of multiple USVs to solve their subtasks, energy consumption optimization, etc. The framework can be advantageously applied to tasks such as warehouse logistics, surface maintenance, area exploration, etc. At the end of the study, the application of the framework is presented using a simulated example of cooperative problem solving using six vehicles.
Universal quantum computing with anharmonic photonic cavities and free- electron ancillas

We propose and analyze a new paradigm for optical quantum computation using anharmonic photonic cavity qubits and free-electron ancillas. Our approach enables deterministic, high-fidelity quantum gates and preparation of cluster states between remote cavities.
A Chiral One-dimensional Atom Using a Quantum Dot in an Open Microcavity

We realize an optical equivalent of a diode that simultaneously shows nonlinear and non-reciprocal behavior. Using a quantum dot in a microcavity, we show that photon transmission is one order of magnitude larger in the forward direction compared to the backward direction. The system shows a strong nonlinearity with an onset at 100pW.
Blended Learning as a Model of Learning Technology in the Digital Era and Industrial Revolution 4.0

This study analyzes how the Blended Learning Model affects writing skills, language and attitudes. We employes a quasi-experiment using a posttest-only control group design. The population of this study was high school students consisting of 10 classes. Of the 10 classes, 4 classes had equal abiliti