Reviewerly/exHarmony huggingface dataset:
Query Attribute Recommendation at Amazon Search

Query understanding models extract attributes from search queries, like color, product type, brand, etc. Search engines rely on these attributes for ranking, advertising, and recommendation, etc. However, product search queries are usually short, three or four words on average. This information shortage limits the search engine's power to provide high-quality services.
Building and Deploying a Multi-Stage Recommender System with Merlin

Newcomers to recommender systems often face challenges related to their lack of understanding of how these systems operate in real life. In most online content related to this topic, the focus is on models and algorithms that score items based on the user's preferences. However, the recommender model alone does not comprise everything needed for serving optimized recommender systems that meet the company's business objectives. An industry-standard recommender system involves a number of steps, including data preprocessing, defining and training recommender models, as well as filtering and business logic for serving. In this work, we propose the four-stage recommender system, an industry-wide design pattern we have identified for production recommender systems. The four-stage pipeline includes an item retrieval step that prepares a small subset of relevant items for scoring. The filtering stage then cleans up the subset of items based on business logic such as removing out-of-stock or previously seen items. As for the ranking component, it uses a recommender model to score each item in the presented list based on the preferences of the user. In the final step, the scores are re-ordered to provide a final recommendation list aligned with other business needs or constraints such as diversity. In particular, the presented demo demonstrates how easy it is to build and deploy a four-stage recommender system pipeline using the NVIDIA Merlin open-source framework.
Augmenting Netflix Search with In-Session Adapted Recommendations

We motivate the need for recommendation systems that can cater to the members' in-the-moment intent by leveraging their interactions from the current session. We provide an overview of an end-to-end in-session adaptive recommendations system in the context of Netflix Search. We discuss the challenges and potential solutions when developing such a system at production scale.
Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions

Heterogeneous sequential recommendation (HSR) is a very important recommendation problem, which aims to predict a user's next interacted item under a target behavior type (e.g., purchase in e-commerce sites) based on his/her historical interactions with different behaviors. Though existing sequential methods have achieved advanced performance by considering the varied impacts of interactions with sequential information, a large body of them still have two major shortcomings. Firstly, they usually model different behaviors separately without considering the correlations between them. The transitions from item to item under diverse behaviors indicate some users' potential behavior manner. Secondly, though the behavior information contains a user's fine-grained interests, the insufficient consideration of the local context information limits them from well understanding user intentions. Utilizing the adjacent interactions to better understand a user's behavior could improve the certainty of prediction. To address these two issues, we propose a novel solution utilizing global and personalized graphs for HSR (GPG4HSR) to learn behavior transitions and user intentions. Specifically, our GPG4HSR consists of two graphs, i.e., a global graph to capture the transitions between different behaviors, and a personalized graph to model items with behaviors by further considering the distinct user intentions of the adjacent contextually relevant nodes. Extensive experiments on four public datasets with the state-of-the-art baselines demonstrate the effectiveness and general applicability of our method GPG4HSR.
The Rationale for the Optimal Continuous-Variable Quantum Key Distribution Protocol

This article describes the current technical level of developments in the field of continuous-variable quantum key distribution (CV-QKD). Various classifications are described, the criteria are analyzed, and the optimal protocol is selected. The analysis is focused around device-dependent schemes with a theoretical emphasis, and therefore, a detailed analysis of device-independent CV-QKD and side-channel attacks is out of the scope of the work. However, the latter, one way or another, is taken into account when describing possible classifications. The choice of the optimal protocol was carried out, first of all, from the potential possibility of integration into existing network telecommunication infrastructures. Predominantly, the general classification is carried out in such a way that it is possible to draw up a specific protocol, depending on the task of implementation.
Evaluation of e-learners’ concentration using recurrent neural networks

Recently, interest in e-learning has increased rapidly owing to the lockdowns imposed by COVID-19. A major disadvantage of e-learning is the difficulty in maintaining concentration because of the limited interaction between teachers and students. The objective of this paper is to develop a methodology to predict e-learners' concentration by applying recurrent neural network models to eye gaze and facial landmark data extracted from e-learners' video data. One hundred eighty-four video data of ninety-two e-learners were obtained, and their frame data were extracted using the OpenFace 2.0 toolkit. Recurrent neural networks, long short-term memory, and gated recurrent units were utilized to predict the concentration of e-learners. A set of comparative experiments was conducted. As a result, gated recurrent units exhibited the best performance. The main contribution of this paper is to present a methodology to predict e-learners' concentration in a natural e-learning environment.
Non-invasive diagnosis of fetal arrhythmia based on multi-domain feature and hierarchical extreme learning machine

Heart disease is one of the major causes of affecting the health of newborns. Detecting the presence or potential heart disease of the fetus as soon as possible, and adopting relevant treatment plans in a timely manner, which has a profound impact on doctors and patients. This study aims to develop an accurate screening method with arrhythmia (ARR) to assist physicians to further diagnose whether them have heart disease. Therefore, this paper proposes a multi-domain feature extraction technique and a hierarchical extreme learning machine (H-ELM) network for the prediction of fetal ARR. Firstly, the multi-domain feature extraction technology is used to extract abundant high-dimensional feature for representing the original signal. Secondly, neighborhood component analysis (NCA) is used to screen the sensitive features from the high dimensional feature vectors. Then, the obtained sensitive features are input into stacked extreme learning machine sparse autoencoder (ELM-SAE), which extract high-level fusion features by layer-by-layer unsupervised learning manner. Finally, an original ELM was connected on the end of the ELM-SAE network for the prediction of fetal ARR. The experimental results illustrate that the proposed method can achieve sensitivity of 99.11%, specificity of 93.91%, precision of 93.52%, and accuracy of 96.33%. Furthermore, the proposed method comprehensive performance outperforms the compared models. Therefore, the proposed method can be effectively used for the prediction of fetal ARR, and with the continuous improved the research, it is expected to be considered as an auxiliary diagnostic tool for physicians in the future.
The Complexity of Interaction between Social Media Platforms and Organizational Performance

Social media platforms (SMPs) have become a key source of success for many organizations in today&rsquo;s complex business environment due to advancements in information and communication technologies that have created dynamic innovations. This study aims to explore the complex relationships between SMPs&rsquo; usage and organizational performance with respect to small and medium-sized restaurants in Jordan. In addition, it investigates the impact of competition intensity as a moderating variable. The model of the study was assessed and examined by gathering data from 331 Jordanian restaurants using the partial least squares (PLS) method. The findings revealed that SMPs&rsquo; usage is positively associated with restaurant performance (financial, marketing, and operational). In addition, the results indicate a significant moderating impact of competition intensity only on the relationship between SMPs&rsquo; usage and restaurants&rsquo; marketing performance. Based on the results, several theoretical and practical implications for restaurants have been suggested, as well as further research avenues.
Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput

Abstract The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. By reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.
Time‐cost efficient memory configuration for serverless workflow applications

Summary Recently, workflow applications are increasingly migrated to Function‐as‐a‐Service platforms which are easy to manage, highly‐scalable, and pay‐as‐you‐go. Meanwhile, users face challenges in migration of serverless applications because of the lack of efficient algorithm for workflow memory configuration to optimize the performance. To this end, this article proposes a heuristic urgency‐based algorithm UWC and a meta‐heuristic hybrid algorithm BPSO to tackle the time‐cost tradeoff. UWC sorts functions and allocates each function an appropriate memory size by greedy strategy. BPSO hybridizes particle swarm optimization as well as beetle antennae search algorithm to guide particles to search directionally and utilizes nonlinear inertia weight to avoid local premature convergence. Extensive experiments with classical serverless application demonstrate that UWC and BPSO are very competitive in comparison with existing algorithms as they can find the optimal workflow memory configuration.
A Multi-camera System for Human Detection and Activity Recognition

The employment of mobile manipulators in the factory to assist human workers is increasing. As the mobile manipulator moves and performs tasks in an unstructured environment with human workers, it should be able to detect the human workers and understand their activity. In this work, a multi-camera system is proposed, consisting of a thermal camera, a stepper motor and a depth camera. The system is able to detect the human workers around the mobile manipulator and to recognize the activity. In addition, the paper describes the comparison of a recognition pipeline based on 2D skeleton key points detected from color images using OpenPose with the state-of-the-art 3D skeleton based models. It shows that 2D data based model in general have better results than 3D data with the same model parameter.
The Proposed Context Matching Algorithm and Its Application for User Preferences of Tourism in COVID-19 Pandemic

Currently, many applications of information search tourism are limited in the COVID-19 pandemic using a search engine. However, most application service online has not supported directly, matching end users with their preferences to find suitable tourist places. This paper has presented a proposed model using the Context Matching algorithm mostly based on the Smartphones; matching with user's preferences and behaviors allows users to find tourism packages and regions. The experimental results show that the proposed model achieves significant improvements in matching user preferences for the domain under dynamic uncertainty. We posit that our novel approach holds the prospect of improvements in user preferences for tourism and weather in the COVID-19 Pandemic.
A portable hardware security module and cryptographic key generator

It has been noted with concern that the ability of a password to keep an information system secure is diminishing. Increasingly sophisticated attack vectors and low memorability associated with complicated passwords are among the leading reasons limiting security provisioned by passwords. Cryptographic keys suffer from issues including lack of memorability, vulnerable storage mechanisms, key retrieval attacks, lockouts due to key loss and risk of using the same key for multiple services. This study proposes a novel Hardware Security Module (HSM) as a basis for the generation/ re-creation of cryptographic keys. The designed hardware module entirely eliminates the stored cryptographic keys thus eliminating attacks against stored keys. The HSM derives the cryptographic key from sub-components behaving similar to multi-factor authentication, where each factor is an independent authenticator. The proposed scheme enhances security by incorporating physical security into digital security, i.e. as long as either the crypto provider device remains secure or the human component remains secure, the system security remains intact. The scheme proposes a strategy based on defense in depth to secure the HSM, its user, the related service from attacks ranging from simple shoulder surfing to sophisticated Man-in-the-Middle attacks. The proposed HSM is based on commodity hardware components thus having limited cost implications.
Wheelchair control system for the disabled based on EMOTIV sensor gyroscope

In this study, a wheelchair control system (WCS) was designed and constructed that utilizes an EMOTIV gyroscope sensor for comfortable wheelchair movement. The proposed WCS involves an electroencephalograph (EEG), DC motors, an electronic wheelchair, a microcontroller, and a laptop. The control system algorithm is bulit in Arduino microcontroller using C++ language. The WCS comprises 15 states and is dependent on head motions. There are 12 motor speed settings and eight wheel rotary radius cases. The WCS was tested by 10 women and men aged between 20 and 70 years. The system was implemented and tested in both indoor and outdoor environments to represent smooth and rough surfaces. It was also evaluated at various directions and speeds as well as at rotational radii (interior and exterior areas), and it prevented collisions with barriers using an ultrasonic sensor and reduced the wheelchair movement response time. The control system algorithm achieves an average response time of approximately 1–2 s to move the wheelchair in the specified direction. The performance metrics were also investigated and included sensitivity, specificity, and accuracy with encouraging results: The average WCS accuracy is 99.16%, sensitivity is 99.2%, and specificity is 99.15%. The overall performance is approximately 98%. The accuracy and response times of this WCS outperforms previous research findings.
The use of functional programming library to parallelize on graphics accelerators with CUDA technology

Modern graphics accelerators (GPUs) can significantly speed up the execution of numerical tasks. However, porting programs to graphics accelerators is not an easy task, sometimes requiring their almost complete rewriting. CUDA graphics accelerators, thanks to technology developed by NVIDIA, allow you to have a single source code for both conventional processors (CPUs) and CUDA. However, in this single source code, you need to somehow tell the compiler which parts of this code to parallelize on shared memory. The use of the functional programming library developed by the authors allows you to hide the use of one or another parallelization mechanism on shared memory within the library and make the user source code completely independent of the computing device used (CPU or CUDA). This article shows how this can be done.
Rethinking adversarial domain adaptation: Orthogonal decomposition for unsupervised domain adaptation in medical image segmentation

Medical image segmentation methods based on deep learning have made remarkable progress. However, such existing methods are sensitive to data distribution. Therefore, slight domain shifts will cause a decline of performance in practical applications. To relieve this problem, many domain adaptation methods learn domain-invariant representations by alignment or adversarial training whereas ignoring domain-specific representations. In response to this issue, this paper rethinks the traditional domain adaptation framework and proposes a novel orthogonal decomposition adversarial domain adaptation (ODADA) architecture for medical image segmentation. The main idea behind our proposed ODADA model is to decompose the input features into domain-invariant and domain-specific representations and then use the newly designed orthogonal loss function to encourage their independence. Furthermore, we propose a two-step optimization strategy to extract domain-invariant representations by separating domain-specific representations, fighting the performance degradation caused by domain shifts. Encouragingly, the proposed ODADA framework is plug-and-play and can replace the traditional adversarial domain adaptation module. The proposed method has consistently demonstrated effectiveness through comprehensive experiments on three publicly available datasets, including cross-site prostate segmentation dataset, cross-site COVID-19 lesion segmentation dataset, and cross-modality cardiac segmentation dataset. The source code is available at https://github.com/YonghengSun1997/ODADA.
Motifs-based recommender system via hypergraph convolution and contrastive learning

Recently, the strategy of leveraging various motifs to model social semantic information and using self-supervised learning tasks to boost recommendation performance has been proven to be very promising. In this paradigm, each channel describes a common motif (e.g., a triangular social motif) via hypergraph convolution. Richer motifs can be encoded through multiple channels, and self-supervised learning can leverage this multichannel information to build self-supervised tasks (such as contrastive learning tasks), which can greatly improve the recommendation performance in scenarios without enough data labels. However, accurately determining the relationships between different channels and fully utilizing them, while maintaining the uniqueness of each channel, is a problem that has not been well studied or resolved in this field. This paper explores and verifies the disadvantages of directly constructing contrastive learning tasks on different channels with practical experiments and proposes a scheme of interactive modeling and matching representation across different channels. This is the first such attempt in the field of recommender systems, and we believe that this paper will inspire future self-supervised learning research based on multichannel information. To solve this problem, we propose a cross-motif matching representation model based on attentive interaction, which can efficiently model the relationships between cross-motif information. Based on this, we also propose a hierarchical self-supervised learning model that realizes self-supervised learning within and between channels, respectively, which improves the ability of self-supervised learning tasks to autonomously mine different levels of potential information. We have conducted abundant experiments, and various metrics on multiple public datasets show that the method proposed in this paper significantly outperforms the state-of-the-art methods, regardless of the general or cold-start scenario. In the model variant analysis experiment, the benefits of the cross-motif matching representation model and the hierarchical self-supervised model proposed in this paper are also fully verified.
Synchronization of reservoir computing models via a nonlinear controller

A drive-response scheme of reservoir computers coupled by a nonlinear controller is proposed to realize lag synchronization. Numerical results on several typical chaotic systems have well demonstrated the effectiveness of this scheme. It does not require the computation of the Lyapunov exponents, and can be taken as a key step for a new data-driven route achieving lag synchronization.
An efficient method to detect series of fraudulent identity documents based on digitised forensic data

Document fraud is a transnational form of crime, and its serial character has already been highlighted. To combat this phenomenon, the Interstate Database of Fraudulent Identity Documents (BIDIF) has been created and implemented in Switzerland. It supports the comparison of documents and the detection of series, i.e., documents that share a common source. To efficiently use such a system, forensic document examiners would benefit from a harmonised and proven profiling method. Thus, the aim of this study is to develop a method for comparing documents and establishing series. The method is meant to improve the detection capabilities of forensic document examiners operating BIDIF or engaged in the profiling of fraudulent documents. First, a method based on the visual characteristics of digitised images of fraudulent identity documents has been developed. Subsequently, the method was qualitatively and quantitatively evaluated using four tests. The first test verified the ability of the method to detect pre-existing series. The second test checked the capability of the method to detect links amongst isolated documents. Finally, two further tests were carried out to compare the method impact on the successful detection of series. These tests were carried out by professional forensic document examiners and Master students in forensic science, respectively. This allowed a comparison of the method influence on series detection. The method allowed a significant increase in the number of series and links detected, while also decreasing the occurrence of false negatives and false positives. Furthermore, links were more rapidly detected.